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If you seek feedback for your products or a new launch from a diverse group of people with different opinions, that’s a panel. Panels help businesses make better decisions, from designing a new gadget to figuring out the latest fashion trend. They’re like a mini-audience that gives a sneak peek into what everyone else might think.
But are you managing these panels? It can be challenging. Think of it like trying to organize a big group trip – everyone has different schedules, preferences, and ideas. There’s a lot to juggle! You must find the right people, keep everyone in the loop, ensure they share honest thoughts, and grow the team when needed. And just like how sometimes plans can go haywire, keeping panels running smoothly also has challenges.
The good news is we have excellent tech tools and innovative strategies to help us. Handling these challenges has become more accessible, especially with artificial intelligence and machine learning. Let’s dive in, understand these bumps in the road better, and see how the latest tech can be our co-pilot in managing panels.
Ready? Let’s dive into panel and panel management!
Introduction to Panels
At their core, panels are selected groups of individuals or entities brought together for a specific purpose, whether it’s for feedback, data collection, or to oversee an initiative. The applications are vast and varied, From focus groups that vet product designs to panels that assess the effectiveness of advertising campaigns.
Managing these panels is more than just gathering data. It encompasses recruitment, engagement, data accuracy, scalability, and efficient communication. With effective panel management, the chaos of data collection turns into a harmonious symphony of insights.
Benefits of Panel Management to Research Companies
Below are the benefits that research companies get with a panel management platform.
Steady and Reliable Data Stream
Having a dedicated panel means research companies can tap into a consistent source of participants. This removes the variability that comes with constantly seeking new respondents. Research companies can build upon previous data with each interaction, making insights richer and more detailed.
Cost-Efficiency
Setting up and recruiting for a panel might have initial costs, but in the long run, it proves more economical. Research companies don’t have to allocate funds and resources to find new participants for every study. They have a ready-made group familiar with the research process, saving time and money.
Swift Turnaround
With an accessible panel, research companies can quickly deploy surveys or studies without the wait associated with recruitment. This is especially valuable in time-sensitive projects where research companies need feedback promptly.
Quality Assurance
Having a managed panel ensures that research companies are working with validated respondents. Setting up a panel involves careful selection, ensuring participants are genuine and fit the required criteria. This reduces the risk of fraudulent data or outliers, which can skew results.
Depth of Insights
Panels, especially those engaged over a long period, can offer longitudinal data. This means research companies can track changes, habits, and opinions over time, giving them a deeper understanding of trends and shifts within the group.
Tailored Demographics
Panels can be curated to represent specific demographics or profiles. If a research company is keen on understanding a particular age group, region, profession, or criterion, panels can be designed to fit that need, ensuring targeted and relevant insights.
Enhanced Engagement
When panelists feel they’re part of a continual process and their opinions are valued, they’re more likely to engage deeply. This enhanced engagement translates to richer feedback, with panelists more willing to offer detailed responses or partake in lengthy studies.
Strategic Edge
In industries or sectors where trends change rapidly, having immediate access to a panel can give research companies a competitive advantage. They can quickly gauge reactions, test hypotheses, or validate assumptions, allowing businesses or institutions to stay one step ahead of the curve.
No doubt, panel management has helped market research companies and will impact their efficiency in the coming years. let’s see what benefits it holds for panelists.
What’s in for Panelists?
Panelists aren’t just data sources but partners in the research journey. With panel management tools, panelists enjoy.
Rewards & Incentives- Many platforms offer tangible rewards, from gift cards to cash.
Feedback Loops- Panelists can see the impact of their contributions, fostering a sense of purpose.
Flexibility- Modern tools allow panelists to contribute at their convenience, respecting their time.
Diverse Engagement- Interactive surveys, gamified inputs, and multimedia feedback options keep the engagement lively and varied.
Core Challenges in Panel Management & Tech-Driven Solutions
Market research panels give valuable insights to agencies and brands. They’re great for collecting feedback, tracking public opinion over time, and testing new concepts with specific groups.
While the benefits are many, the challenges are just as abundant. Below, we highlight core challenges in panel management, shedding light on practical examples and offering tech-driven solutions reshaping the field.
1. Recruitment & Onboarding
Challenge: One of the fundamental challenges in panel management is the recruitment of suitable panelists who genuinely meet the set criteria and the subsequent task of onboarding them seamlessly.
Example: Consider a research study focused on gourmet coffee consumption. The targeted panel might consist of coffee lovers or baristas. Ensuring that each recruited panelist genuinely fits this niche and isn’t just an occasional coffee drinker can be daunting.
Solutions: Using sophisticated panel software can aid in specifically recruiting and targeting the desired demographics. AI-based profiling of panelists can scrutinize them based on set criteria (qualification and quotas) and past behaviors, streamlining the selection process and ensuring a higher fit percentage. Moreover, digital onboarding platforms can facilitate easy integration and orientation for new panelists.
2. Data Quality & Integrity
Challenge: Central to any research or study is the assurance that the data collected is of the highest quality, is unbiased, and genuinely represents the feedback or input of the panelists. Eliminating data quality issues is one of the prior challenges, as it can drastically impact business decisions. Also, vague historical data can lead to a chain of business failures.
Example: If a certain fraction of panelists habitually provide fabricated information or quickly skim through surveys, the overall data integrity is compromised, leading to flawed insights.
Solutions: Sophisticated data validation tools can cross-check responses for authenticity. Incorporating CAPTCHA and similar mechanisms can thwart bot submissions. Apart from this, integrating powerful fraud detection techniques can help detect fraud efficiently and eliminate it to an extent. Moreover, advanced algorithms can detect and filter out inconsistent or suspicious answers, ensuring higher reliability in collected data.
3. Engagement & Retention
Challenge: Maintaining the enthusiasm and dedication of panelists over prolonged periods is a formidable task, especially when faced with potential monotony or a perceived lack of appreciation for their contributions. Consumers today are eager to voice their opinions, as seen on social media, review sites, and online forums. But there’s also more competition for their attention. This makes it hard to get panelists, and many drop out. Finding replacements takes more time and money.
Example: After participating in a series of surveys about automobiles, a panelist might feel disengaged if the content starts becoming too repetitive or doesn’t evolve.
Solutions: Introducing gamification elements can make participation more interactive and appealing. Regularly updating and revamping the content and transparent feedback mechanisms can keep panelists invested. Furthermore, digital reward management systems can offer instant gratifications, bolstering retention rates.
Also, businesses can create customized and engaging surveys with interesting questionnaires to keep panelists engaged throughout, increasing the survey completion rate. For this, companies must also have advanced survey creation tools.
4. Privacy & Security Concerns in Panel Management
Challenge: In an era dominated by digital interactions, ensuring the complete safety and confidentiality of panelist data is a significant concern. Protecting consumer data and ensuring their privacy is paramount today. It’s vital to have a panel management system rooted in privacy, with robust security measures to safeguard participant information. As consumers become more aware of data privacy, businesses that neglect it risk losing their trust.
Some respondent’s or panelist’s stats-
84% of participants expressed privacy concerns, their and society’s data, and a desire for more oversight over its usage.
48% have shifted to different businesses due to their data-related policies or sharing habits.
79% of respondents are either very or somewhat worried about how companies utilize their collected data.
64% are equally concerned about governmental data gathering.
81% of respondents believe they possess little to no influence over the collected data.
46%of customers feel they’ve relinquished control of their data.
Both brands and agencies must confidently safeguard consumer data and meet all regulations. Managing research panels effectively is vital for many companies’ research plans, offering essential insights for intelligent decisions. Thus, choosing the right technology and procedures is critical to tackling panel management challenges and delivering timely and thorough insights to your business.
Example: Recent history has witnessed several high-profile data breaches. Such incidents involving the leak of personal details can irreversibly damage the trust panelists place in the managing organization.
Solutions: Adopting state-of-the-art encryption techniques, multi-factor authentication systems, and secure cloud storage solutions can ensure robust data protection. Regular audits and adherence to international data protection standards like GDPR can further fortify data defenses.
5. Scalability & Growth
Challenge: As organizations expand and evolve, there’s an intrinsic need to enlarge the panel commensurately. Managing this burgeoning number without diluting the quality becomes a crucial challenge.
Example: Transitioning from managing a cozy panel of 500 to a massive group of 50,000 involves drastically different logistics, strategies, and dynamics.
Solutions: Leveraging scalable cloud-based platforms can comfortably accommodate increased data traffic. Automation tools, harnessing the power of AI, can efficiently handle tasks like dispatching surveys, reminders, and notifications, enabling managers to concentrate on growth-oriented strategies.
6. Segmentation & Customization
Challenge: Recognizing the diversity and uniqueness of panelists is vital. Categorizing them based on various metrics and customizing tasks to cater to these specific segments enhances the relevance and accuracy of collected data. As the panel size increases, it offers more detailed insights, but it can also complicate targeting the right audience swiftly. Your panel management tools and methods should simplify identifying and zeroing in on specific groups.
Example: A health brand might cater to professional athletes and sedentary office workers. These two groups’ feedback, requirements, and concerns would differ.
Solutions: AI and ML tools can facilitate dynamic, real-time segmentation of panelists based on evolving data patterns. Advanced survey tools can customize the content, tailoring questions to align perfectly with a recipient’s profile and background, fostering more meaningful engagement and richer insights.
7. Inefficient Communication
Communication is the bedrock upon which successful panel management is built. Maintaining a seamless communication channel with each panelist is crucial in a world where rapid feedback and real-time collaboration are vital. Streamlined communication ensures that every panel member is continuously in the loop and fosters a sense of belonging and purpose, enhancing their commitment to the panel’s objectives. The challenge amplifies as panels become more global, encompassing many cultures, languages, and time zones.
Finding a suitable time for interactions, addressing language barriers, and ensuring that every message resonates with its intended clarity and significance are simple tasks. This growing complexity can lead to miscommunications, which in turn can jeopardize the very essence of the panel’s purpose.
Example: Consider a global environmental research panel designed to understand and mitigate climate change impacts. This panel, with experts from five continents, aims to integrate diverse knowledge, from desertification in Africa to melting glaciers in Antarctica. A crucial update intended for all members is sent out from London during business hours.
While this suits European panelists, members in Australia might find the timing intrusive, and those in the Americas might miss it entirely due to time zone differences. Such communication lapses can lead to fragmented insights, potentially hampering the panel’s overall efficacy.
Solution: Enter AI-powered communication medium – the modern-day answer to these challenges. Going beyond standard scheduling or translation capabilities, these tools can learn from previous interactions and user preferences.
For instance, if a panelist often responds to updates in the evening, the platform will prioritize sending them messages during that window. By integrating mobile email notifications with AI enhancements, we can ensure timely communications are tailored to resonate best with each panelist. Such a personalized approach addresses the logistical challenges and makes each panelist feel uniquely valued, fostering greater engagement and collaboration.
All-in-One OnGraph’s Panel Management Solution to Beat Challenges
Driving their panels not only helps businesses and research companies in driving quick insights but also a reliable one. But sometimes managing panels becomes challenging, especially when it grows. But looking at the brighter side, some challenges must pay attention to the benefits of it.
So, to overcome such challenges and provide a better experience for panelists and research companies, OnGraph is leaping by integrating AI and ML solutions to deal with panel management challenges and developing a customized white-label panel management platform for every research company in the market.
If you are one of those businesses who want control of their panels, we are the right partner for your subsequent research. Connect with us with your queries today.
In today’s fast-paced world, time is of the essence, and organizations must stay caught up to their competitors. Did you know 90% of businesses report improved efficiency by implementing ready-made apps? These pre-built solutions save valuable time and provide cost-effective alternatives, allowing companies to allocate resources strategically.
With over 2.5 million readymade apps available, the possibilities are endless. So, join us as we delve into the transformative benefits of readymade apps and discover why they are the intelligent choice for businesses of all sizes.
Before that, we will have a quick introduction to what readymade apps are.
What do you mean by Readymade or white-label app solutions?
Readymade apps refer to pre-built software applications designed and developed for specific purposes or industries. These apps come with ready-to-use features, functionalities, and interfaces, making them easily deployable and customizable. They are often created to fulfill everyday business needs like e-commerce, social media, or productivity tools.
Readymade apps provide a convenient solution for individuals or businesses looking to save time and resources by leveraging existing software frameworks.
Benefits of using Readymade apps for businesses
Readymade apps have played a significant role in helping businesses streamline their operations, enhance customer engagement, and increase efficiency. Here are some stats highlighting the benefits of readymade apps for businesses.
Cost-Effective Solution
According to a survey by Clutch, 59% of small businesses reported cost savings as the primary reason for adopting readymade apps.
The survey revealed that 45% of small businesses saved more than $10,000 annually using readymade apps.
Time Savings
A study conducted by Salesforce found that businesses that adopted readymade apps experienced a 20% reduction in time spent on administrative tasks.
In a survey by QuickBase, 66% of respondents reported saving at least 240 hours per year by utilizing readymade apps for various business functions.
Increased Productivity
A study by Canvas revealed that 64% of businesses reported improved productivity after implementing readymade apps.
According to a report by Capterra, 78% of businesses reported increased employee productivity by adopting readymade apps.
Enhanced Customer Engagement
The same Capterra report mentioned above found that 62% of businesses reported improved customer engagement after implementing readymade apps.
According to Statista, mobile app downloads are projected to reach 258.2 billion in 2022, indicating a significant opportunity for businesses to engage with customers through mobile apps.
Access to Advanced Features
Readymade apps often come with various advanced features and functionalities to help businesses streamline processes and improve user experience.
According to a study by App Annie, the average smartphone user has over 80 apps installed on their device, indicating the high demand and usage of apps with advanced features.
Increased Revenue
In a survey by Appster, 62% of businesses reported an increase in revenue after adopting readymade apps.
A report by Statista predicts that global mobile app revenues will reach $935.2 billion in 2023, highlighting the potential for businesses to generate significant revenue through mobile apps.
Market Reach
Readymade apps enable businesses to expand their market reach by targeting mobile users across various platforms.
According to eMarketer, smartphone users worldwide are projected to reach 3.8 billion by 2022, providing businesses with a vast potential customer base.
These stats demonstrate the positive impact of readymade apps on businesses, offering cost savings, time efficiency, productivity gains, improved customer engagement, access to advanced features, increased revenue, and broader market reach.
Are readymade apps better than custom solutions?
Readymade apps might have some advantages over custom app solutions but come with some limitations as well. Check out the detailed comparison between readymade app development and custom app development.
Most companies have reached the top and embarked on their significant presence, leveraging the immense benefits of readymade apps. Let’s find out.
Companies that started their journey with White-label app Solutions
Here are some success stories of businesses that have succeeded using ready-made apps.
Uber
Uber, the popular ride-sharing service, started its journey using a ready-made app solution. By leveraging an existing app framework, Uber could launch its service and connect drivers with riders efficiently and quickly. Using a ready-made app allowed Uber to rapidly scale its operations, leading to significant cost savings and a broad market reach.
Instagram
Instagram, the photo-sharing social media platform, initially relied on a ready-made app to build its foundation. The app provided a user-friendly interface, advanced photo-editing features, and seamless social sharing capabilities. This allowed Instagram to gain popularity and attract millions of users quickly. Eventually, Instagram was acquired by Facebook for a billion dollars, showcasing the success of its ready-made app approach.
WhatsApp
WhatsApp, the messaging app that revolutionized communication, utilized a ready-made app to develop its platform. With features like instant messaging, voice calls, and multimedia sharing, WhatsApp quickly gained traction and expanded its user base globally. Using a ready-made app provided WhatsApp with a stable and reliable foundation, ensuring efficient communication and driving its success.
Shopify
Shopify, an e-commerce platform, offers businesses a ready-made app to set up and manage online stores. With its user-friendly interface, robust features, and integration capabilities, Shopify has empowered countless entrepreneurs and businesses to establish their online presence quickly and effectively. The platform’s ready-made app has contributed to increased efficiency, cost savings, and expanded user market reach.
Slack
Slack, a widely used team collaboration tool, utilized a ready-made app to build its communication platform. By offering features such as channels, direct messaging, and file sharing, Slack has transformed the way teams communicate and collaborate. The ready-made app approach enabled Slack to provide a seamless and intuitive user experience, improving efficiency and productivity for businesses worldwide.
These success stories highlight the positive impact of ready-made apps on efficiency, cost savings, and market reach for businesses. They have demonstrated that leveraging existing app solutions can lead to rapid growth, streamlined operations, and significant market penetration.
Testimonials and specific metrics from businesses that have embraced ready-made apps can further illustrate their success. For example, showcasing how a company improved customer engagement by 50% or reduced its development costs by 30% after implementing a ready-made app solution can provide concrete evidence of the benefits. Sharing quotes or testimonials from business owners or executives who have experienced these positive outcomes can also add credibility to the success stories.
Readymade/White-label apps, a business must look for
Ready-made apps have become increasingly popular in various industries, offering businesses a quick and cost-effective solution to meet their needs. Here are some types of ready-made apps that companies can consider.
Dating Apps– Dating apps provide a platform for individuals to connect and find potential partners. These apps often include features like user profiles, matching algorithms, messaging, and location-based services.
Market Research Apps– Market research apps facilitate data collection and analysis for businesses. It will cover tools for survey creation, project management, fraud detection techniques, panel websites, and DIY platforms to gather insights about consumer preferences, trends, and market dynamics.
Travel Apps- Travel apps offer a range of features to assist travelers, such as flight and hotel bookings, itinerary planning, navigation, local recommendations, and reviews. These apps aim to enhance the overall travel experience and provide convenience to users.
Food Delivery Apps– Food delivery apps enable users to order food from local restaurants and deliver it to their doorstep. These apps typically include menus, ordering and payment systems, real-time tracking, and user reviews.
Education Apps- Education apps cater to the needs of learners, offering educational resources, online courses, tutoring services, language learning tools, and virtual classrooms. These apps facilitate learning and skill development in a mobile-friendly format.
These are the types of readymade apps that we deliver. We can customize the existing readymade solution and make it customizable per your business requirements if required.
Get White-label Apps Development Solutions with OnGraph
Readymade apps have proven to be a game-changer for businesses, providing a cost-effective and efficient solution to their diverse needs. With technological advancements and the growing availability of customizable app templates, the future scope of readymade apps in the business world is promising.
As businesses continue to embrace digital transformation and seek quick deployment of solutions, readymade apps offer a convenient option for rapid development and deployment. Moreover, the increasing trend of app marketplaces and the growing community of developers contribute to the continuous evolution and enhancement of readymade apps, ensuring their relevance and competitiveness in the ever-changing business landscape.
With the rise of AI coding assistant tools, coding, debugging, editing, and generating code has become much easier. With this mission, Replit integrated AI capabilities in transforming the lives of the next generation of software creators.
The good news is, now, Replit has made Replit AI available for free to their 25+ million developer community. Developers on the free plan can access the basic AI features, while Pro users will retain exclusive access to the most powerful AI models and advanced features.
Replit AI- Your AI partner in Coding
Get ready to harness the power of Replit’s AI to boost your productivity and creativity.
Replit AI is a collection of artificial intelligence tools provided by Replit, comprising features like Complete Code, Generate Code, Edit Code, and Explain Code. These tools collectively enhance your coding experience on Replit.
The insights provided by Replit AI stem from expansive language models trained on open-source code and refined by Replit. To offer suggestions and decode your code, it assesses your input and other contextual information from your Repl, including the programming language in use.
While JavaScript and Python are the optimal languages for Replit AI, it can work with 16 languages altogether. The supported languages are Bash, C, C#, C++, CSS, Go, Java, JavaScript, HTML, PHP, Perl, Python, R, Ruby, Rust, and SQL. However, the efficiency might differ based on the language.
The efficiency has now improved with new Replit’s AI model- Replit Code V1.5 3B
To make AI accessible to everyone, Replit introduced a new code generation model, Replit Code V1.5 3B, on Hugging Face. Being supporters of open-source language models, Replit allowed individuals to utilize it as a base model for specialized fine-tuning with minimal restrictions on commercial applications. This model has specifically trained for code-completing tasks.
Main Highlights
Rich License-Friendly Training Data: Utilizes a vast 1 trillion tokens of code sourced from the permissive Stack dataset and opens developer-focused content on StackExchange.
Cutting-Edge Performance: Achieves top-notch scores in HumanEval and Multi-PLe assessments for a 3B code completion model.
Wide Language Spectrum: Covers the top 30 programming languages recognized by Replit, reinforced by a uniquely trained 32K vocabulary for optimal efficiency and broad scope.
Contemporary Methods: Engineered with the most recent advances such as Grouped Query Attention using Flash Attention Triton Kernels, ALiBi positional embeddings, and beyond, ensuring swift responses and superior generation quality. Additionally, it employs the newest training approaches like the LionW optimizer, adjusted learning rates, QKV clipping, and others.
Pristine Training Data Quality: Infused with special code quality assessment techniques, readability checks, and filters to remove offensive and inappropriate content, guaranteeing enhanced generation outputs.
There is no doubt that this new AI model has outperformed models of much larger size such as CodeLlama7.
OnGraph, turns ideas into software, real quick, with AI capabilities
If you want quick solutions to help your business scale then, OnGraph is your way to go. Successfully integrating AI capabilities to fast-track software development with AI-powered tools. We put efforts where it matters the most- building innovative software with fast coding at the speed of thought.
In modern programming, two languages often find themselves in the limelight of debates and comparisons between Ruby and Python. Both revered for their simplicity and readability, they’ve powered some of our generation’s most iconic web platforms and tools.
Yet, developers often find themselves at a crossroads when choosing one for their next project. Each language has its own strengths, applications, and staunch advocates, which makes the decision far from straightforward. In this blog, we will dive deep into the nuances, compare the capabilities, and provide insights to help you make an informed choice between Ruby and Python for your upcoming endeavor.
Introduction to Ruby
Born in the 1990s, Ruby is a versatile programming language crafted by Yukihiro Matsumoto. Inspired by languages like Perl and Smalltalk, it was shared with the world in 1995, aiming to make coding more adaptable and efficient.
Ruby is a vibrant, freely available programming language emphasizing clarity and efficiency, ideal starting point for beginners. Its consistent structure and abundant resources and documentation simplify the learning curve.
Prioritizing developer efficiency, Ruby offers libraries for task automation and promotes clear code, enabling newcomers to develop applications swiftly. The supportive Ruby community offers guidance through forums and study groups, enhancing the learning experience. Its versatility spans web development to data analysis, offering novices varied programming avenues.
Furthermore, frameworks like Ruby on Rails guide beginners in web development, streamlining the creation of web-based projects.
Core Ruby Features
Below are the core features of the Ruby programming language.
Clean Syntax- Boasting a syntax mirroring natural language, Ruby ensures code clarity and ease of maintenance, which expedites development.
Metaprogramming– Ruby’s robust features enable the dynamic creation of classes and methods at runtime. With functionalities like define_method, Ruby can intuitively produce and adapt code based on real-time data or logic, leading to concise and DRY (Don’t Repeat Yourself) code.
Object-Centric- Everything – from strings and numbers to booleans – is treated as an object in Ruby. This inherent object orientation paves the way for class definitions, inheritance, polymorphism, and encapsulation, promoting modular programming.
Community Support- With a thriving developer community, Ruby offers extensive libraries and frameworks. Notably, the Ruby on Rails framework, grounded in Ruby, stands out for building robust web applications.
Rapid prototyping- Ruby’s adaptable syntax dynamic characteristics and extensive library offerings expedite development. Furthermore, its metaprogramming capabilities facilitate swift advancements and prototype modifications, optimizing time and effort.
Advantages of Ruby
These advantages have made Ruby a preferred choice for many startups and established companies looking to build robust web applications efficiently.
Easy Learning Curve- Ruby’s syntax is intuitive and mirrors natural language, making it accessible for newcomers and allowing seasoned developers to grasp its nuances quickly.
Faster Time-to-Market- With frameworks like Ruby on Rails and a wealth of ready-made libraries (gems), Ruby facilitates rapid development, ensuring your applications and websites can be launched swiftly.
Cost-Efficient Development- Ruby’s open-source nature, combined with many free libraries and tools, means businesses can reduce development costs. Additionally, the development speed translates to lower labor costs and quicker returns on investment.
Limitations of Ruby
While Ruby () has numerous advantages, like any framework, it has limitations. Here are 3-4 core limitations.
Performance Overheads- Ruby, the language on which Rails is built, is often considered slower than languages like C++ or Java. While Rails is sufficient for many applications, extremely high-performance applications might find it less optimal.
Niche Usage- While Ruby shines in web development, its adaptability for diverse applications might be somewhat restricted.
It’s important to note that while Ruby has these limitations, it remains a powerful tool for many use cases, especially web applications that benefit from rapid development cycles.
Where to use Ruby?
Given its dynamic, object-oriented nature and strong ecosystem, Ruby is well-suited for several scenarios. Here are some instances where using the Ruby programming language might be a great choice.
MVPs and Startups- If you’re a startup looking to quickly prototype or build a minimum viable product (MVP), Ruby’s ease of use and rapid development capabilities make it a solid choice.
E-commerce Sites- Platforms like Shopify, built on Ruby, showcase its strengths in developing scalable and user-friendly e-commerce solutions.
Content Management- If you want to create a content-driven site or a custom content management system, Ruby offers several gems (libraries) and tailored tools.
Custom Database Solutions- Ruby can effectively craft solutions involving intricate database work, given its robust ORM (Object-Relational Mapping) capabilities.
Web Scraping- If you need to extract large amounts of data from websites, Ruby, combined with libraries like Nokogiri, can be a powerful tool for web scraping.
Automation and Scripting- Ruby’s expressive syntax and extensive standard library can be beneficial for automating repetitive tasks or building utility scripts.
API Development- If your project involves creating a RESTful API, frameworks like Sinatra or Rails can be used to design efficient and scalable APIs in Ruby.
Integration Projects- Ruby’s vast library ecosystem can aid in projects where you must integrate different software components or services.
Cloud and DevOps- For infrastructure automation, configuration management, or other DevOps-related tasks, tools like Chef or Capistrano, built with Ruby, come in handy.
Companies Using Ruby
Introduction to Python
Python, one of the most versatile and accessible programming languages, has carved a significant niche in the tech world since its inception by Guido van Rossum in the late 1980s. With a syntax that emphasizes readability and a design philosophy that champions code simplicity and clarity, Python has become the go-to language for beginners and seasoned developers alike.
Its vast array of applications, from web development and data analysis to artificial intelligence and scientific computing, showcases its adaptability. Today, Python stands as a language and a community-driven movement, powering some of the most innovative projects in the digital landscape. With many plugins, tools, and libraries, Python is widely used in creating complex apps leveraging AI, ML, and other technologies.
There is no doubt about the growing popularity of Python.
Image Credits- Statista
Core Python Features
Python is renowned for its myriad features that cater to beginners and seasoned developers. Here are 5-6 core features of Python.
Readable Syntax- Python emphasizes code readability, using indentation to define code blocks. This makes the code clean and easy to understand, often resembling plain English.
Dynamic Typing- Variables in Python do not require an explicit type declaration, and their types can change over their lifecycle. This provides flexibility in coding but also requires developers to be cautious.
Extensive Standard Library- Python boasts a rich standard library that covers a range of modules and tools, reducing the need for external libraries and making many tasks more straightforward.
Interpreted Language- Python is interpreted, meaning it executes code directly, line-by-line, which aids in easier debugging and rapid prototyping.
Cross-platform Compatibility- Python is inherently portable. Code written on one platform, be it Windows, macOS, or Linux, can typically run on any other platform without modification.
Support for Multiple Paradigms- While Python is primarily object-oriented, it also supports procedural and functional programming paradigms, offering flexibility in how developers approach problems.
The combination of these core features and the vibrant community supporting it makes Python a top choice for various applications, from web and software development to scientific research and AI.
Advantages of Python
Python has become one of the leading programming languages in the tech industry, and its widespread adoption can be attributed to several benefits.
Beginner-Friendly- Python’s clean and readable syntax makes it a favorite for beginners. The simplicity aids in quick learning and understanding of programming concepts.
Efficient Development- Python’s rich standard library and vast ecosystem of third-party packages mean developers can accomplish a lot with fewer lines of code, leading to faster development times.
Flexibility- Python is versatile and supports multiple programming paradigms, including procedural, object-oriented, and functional programming.
Robust Community- A vibrant community backs Python. This ensures a constant influx of tools, libraries, tutorials, and forums to help developers at every skill level.
Cross-Domain Application- Python finds utility in diverse domains- web development (Django, Flask), data science (Pandas, NumPy), artificial intelligence (TensorFlow, PyTorch), automation, gaming, and more.
Portability & Scalability- Python applications can run seamlessly across different platforms, and with the right tools and libraries, it can scale to handle large-scale applications.
Integration Features- Python integrates with other languages and technologies, facilitating features like web services, database connections, and integration with C/C++ libraries.
Strong Support for Testing- Python offers tools and libraries for robust testing, enabling a test-driven development approach, which results in fewer bugs and more stable products.
Cost-Effective- Being open-source, Python reduces development costs. Additionally, its efficiency and ease of integration can reduce time-to-market, leading to cost savings.
These benefits collectively make Python a compelling choice for organizations and individual developers seeking an efficient, reliable, and versatile programming language.
Limitations of Python
While Python offers many benefits, it has certain limitations. Here are the critical limitations of Python.
Speed Constraints- Python is an interpreted language, and thus, its execution is generally slower than compiled languages like C++ or Java. While this difference in speed isn’t noticeable for many applications, it can be a limiting factor for compute-intensive applications.
Mobile Development Limitations- While Python can be used for mobile app development, it’s not the go-to choice. Languages and frameworks like Swift (for iOS) and Java/Kotlin (for Android) are commonly used in mobile development.
Despite these limitations, Python’s versatility, simplicity, and extensive libraries often outweigh its drawbacks for many applications. Still, it’s essential to consider these limitations when determining the best fit for specific projects.
Where to use Python?
Python’s adaptability makes it suitable for a wide array of tasks. Here are 6-7 use cases where you can employ Python.
Web Development- Using frameworks like Django or Flask, developers can design and deploy intricate web applications ranging from blogs to e-commerce sites.
Data Analysis & Visualization- With Pandas for data manipulation and Matplotlib or Seaborn for visualization, Python is a top choice for data scientists to dissect information and generate insightful visual representations.
Machine Learning- By leveraging libraries such as sci-kit-learn, TensorFlow, and PyTorch, researchers and developers can craft predictive models, neural networks, and more.
Automation & Scripting- Python’s concise syntax makes it perfect for writing scripts to automate repetitive tasks, from managing files to automating emails or web browser sessions.
Cybersecurity- Python’s flexibility enables security professionals to write penetration testing scripts and network scanners or even develop full-fledged cybersecurity tools.
Game Development- Though not its most common use, Python’s Pygame framework allows for creating simple video games.
Internet of Things (IoT)- With platforms like Raspberry Pi, Python becomes a bridge to control and gather data from embedded devices, making smart machines more intelligent.
These use cases showcase Python’s wide-ranging capabilities and vast potential in various fields and industries.
Companies Using Python
Similarities
While Ruby and Python have their own unique characteristics and are often used for different purposes, they share several core similarities.
High-Level Languages- Both Ruby and Python are high-level, interpreted languages. This means they abstract away most of the complex details of the computer’s operation, allowing developers to write applications using easily understandable syntax.
Object-Oriented- Both languages primarily adopt an object-oriented approach. This means they treat data as objects with associated attributes and methods, making organizing and managing code easier.
Dynamic Typing- In both Ruby and Python, variables are dynamically typed. This means that a variable’s type is determined at runtime and can be changed as the program executes.
Standard Libraries- Each of these languages comes equipped with extensive standard libraries that cater to a wide range of functionalities, allowing developers to perform many tasks without needing external packages or tools.
Community and Open Source- Ruby and Python have solid and active communities. They are open-source languages, meaning that their source code is freely available. This has led to a rich ecosystem of tools, libraries, and frameworks built around them.
Platform Independence- Both languages are cross-platform, meaning that, in most cases, code written in one operating system (like Windows) can be run on another (like macOS or Linux) without any modifications.
While these similarities exist, it’s essential to remember that each language has its strengths and ideal use cases. The choice between Ruby and Python often comes down to the specific requirements of a project or personal preference.
Ruby vs. Python: Detailed Comparison
Having understood the foundational attributes, nuances, and commonalities, let’s delve into the contrasts between Ruby and Python. In the following sections, we’ll explore their standing in the tech world, application scenarios, employment prospects, and other factors to assist you in determining which language—Ruby or Python—best aligns with your needs.
1. Ruby vs. Python: Popularity
When comparing Ruby and Python based on their popularity, various metrics, including developer surveys, job postings, community contributions, and more, can be considered. As per w3techs, below are the current scenarios between Ruy and Python usage over the last year.
Based on TIOBE’s data, Ruby accounts for a mere 0.94% of the global market, landing it in the 18th spot on the list of the Top 20 most-used technologies worldwide.
On the other hand, Python claims around 12% of the worldwide market share. It has experienced a remarkable ascent from the 8th position in 2013 to clinch the top spot among programming languages, a surge primarily driven by the consistent advancements in AI.
Image Credits- jaydevs
2. Role in Software Development
Python and Ruby are employed in app creation, refining, and automating DevOps activities.
In particular, these languages are adept at crafting scripts that simplify processes such as building, deploying, and managing infrastructure. Platforms like GitLab, Jenkins, and Ansible effortlessly accommodate scripts from both Python and Ruby, ensuring uninterrupted development, even among geographically dispersed teams.
While both have their merits, a distinguishing feature of Python within project frameworks is its prowess in interfacing with other coding languages. Often dubbed a “bridging language,” Python seamlessly connects with other technologies like C++, Java, and Go. This characteristic becomes especially significant in a microservice setup, allowing different app “modules” to be constructed using various technologies.
3. ML & Data Science
Python for ML & Data Science- Python has become the dominant language in Machine Learning (ML) and Data Science. This dominance is primarily attributed to its rich ecosystem of data-centric libraries such as NumPy, pandas, and matplotlib for data manipulation and visualization, as well as TensorFlow, Keras, and scikit-learn for machine learning.
Additionally, the community support for Python in the ML and data analytics spaces is unparalleled, which ensures continued growth, updates, and readily available resources. The vast array of tools and extensive documentation make it easier for newcomers to dive into data science projects with Python.
Ruby for ML & Data Science- While Ruby is a powerful and elegant language, it has yet to see the same level of adoption in the ML and Data Science sectors as Python. Nevertheless, libraries like Ruby Numo for numerical computations and Rumale for machine learning show the language’s potential in this domain.
The Ruby community’s emphasis on developer happiness and productivity makes it an attractive choice for various applications, but when it comes to ML and Data Science, its ecosystem is not as expansive as Python’s, making it less of a go-to choice for these fields.
4. DevOps
Ruby for DevOps- Ruby, mainly due to the success of Chef and Puppet, has been a significant player in the DevOps world. These configuration management tools, written in Ruby, have set a strong precedent for infrastructure as code, enabling IT automation at a grand scale.
The Ruby ecosystem promotes a ‘convention over configuration’ philosophy, seen notably in Ruby on Rails, which translates well to the DevOps practices of consistency and automation. Consequently, many DevOps professionals have become acquainted with Ruby due to its association with these influential tools.
Python for DevOps- Python’s versatility has also made it a popular choice in the DevOps space. Tools like Ansible, a robust IT automation tool, and SaltStack, designed for configuration management, are Python-based and have garnered substantial attention.
Python’s extensive standard library and the vast availability of third-party packages make it conducive for scripting and automation, both critical components in DevOps. Moreover, Python’s readability and straightforward syntax make it easy for DevOps professionals to write and maintain scripts, fostering a more collaborative and efficient environment.
5. Automation
Ruby for Automation- with its expressive syntax and powerful metaprogramming capabilities, Ruby offers a robust automation platform. Its dynamic nature and flexibility, combined with the ‘principle of least surprise’, make it relatively straightforward to understand and write scripts for various automation tasks.
Gems like “Capybara” and “Watir” showcase Ruby’s strength in web automation, allowing for efficient testing and web scraping. Moreover, tools like “Thor” and “Rake” are popular in the Ruby community for building command-line applications and task automation, respectively. This ecosystem, centered on developer happiness, ensures Ruby remains a strong contender in the automation domain.
Python for Automation- Python has long been lauded as the “Swiss army knife” of programming languages due to its versatility, which extends prominently into automation. With its clear and readable syntax, Python scripts are often self-explanatory, making automation tasks more maintainable and collaborative.
Libraries like “Selenium” for web automation, “PyAutoGUI” for GUI automation, and “Fabric” for system administration showcase the breadth of Python’s capabilities. Additionally, Python’s expansive standard library and modules such as “os”, “sys”, and “subprocess” offer native tools that make system automation more accessible. This richness and breadth in its ecosystem ensure Python is often the first choice for various automation challenges.
6. Testing
Ruby for Testing- Ruby has a strong testing culture, with the Ruby on Rails framework significantly emphasizing Test-Driven Development (TDD). The Ruby ecosystem boasts a variety of robust testing tools and libraries. “RSpec” stands out as a popular Domain Specific Language (DSL) for writing readable tests, while “Capybara” and “FactoryBot” streamline integration and unit tests, respectively, for web applications. “MiniTest”, another versatile tool, offers traditional assertion-based and spec-style tests.
Ruby’s metaprogramming capabilities also provide a dynamic means to generate test cases, mock objects, and craft flexible test setups. This rich landscape emphasizes readable, maintainable, and comprehensive test suites.
Python for Testing- Testing in Python is supported by various tools, each catering to different testing paradigms and needs. The built-in “unittest” module provides a traditional xUnit-style framework, ensuring that even without external libraries, Python developers have testing capabilities at their fingertips. Tools like “pytest” elevate the testing experience by offering a no-boilerplate approach and feature-rich plugins. “Nose2“, building on the legacy of “nose”, further extends Python’s testing capabilities.
For web testing, “Selenium” integrates seamlessly with Python, and for mocking and patching, “unittest.mock” offers a range of options. Python’s focus on readability and simplicity is mirrored in its testing tools, emphasizing clarity and thoroughness.
7. Community Growth
The presence of a robust community and a supportive network is pivotal when harnessing any technology for a project. The bigger the community, the smoother the project’s progression. With an active community, developers can.
Seek guidance from the pooled wisdom on online platforms, forums, and community-led websites to tackle coding hurdles.
Utilize open-source resources to amplify application functionalities and hasten the development process.
Stay abreast of recent innovations and churn out cutting-edge solutions. Additionally, the community can act as a reservoir of potential talent. By participating in community events or engaging online, businesses can spot and recruit proficient professionals to enrich their teams.
Ruby’s community is home to approximately 2.4 million dedicated developers. They benefit from a close-knit ambiance, top-notch documentation, and supportive peers, ensuring projects are executed adeptly.
Regarding community strength, Python outshines many of its counterparts with a staggering community of around 17 million enthusiasts. It offers a wealth of educational materials and a suite of ready-for-action tools.
8. Security Aspect
Python and Ruby are recognized for their security strengths. Data from WhiteSource indicates that only 6% of total vulnerabilities are linked to Python, while Ruby accounts for 5%. In contrast, the C language is associated with 47% of reported vulnerabilities. When considering more severe threats, 19% of vulnerabilities found in Ruby are highly severe, compared to 15% in Python.
Most security issues in applications built with Python or Ruby arise from components written in other languages. Common vulnerabilities include SQL injection and XML file parsing. Other areas of concern include Cross-site Scripting (XSS) and authentication protocols.
The proactive involvement of the community bolsters the security of both languages. Ruby actively engages in the HackerOne bounty program, ensuring rapid response and resolution to reported vulnerabilities, with patches released at a minimum semi-annual frequency. Python’s security can be assessed using community-crafted tools such as Python Taint, Tinfoils, and Pyntch.
9. Multithreading
Effective multi-threading is vital for maximizing computer resources. These applications have segments operating simultaneously, tackling tasks like reading files or managing API connections. Designing such applications is intricate, demanding deep expertise to navigate issues like race conditions and deadlocks.
Python uses its ‘threading’ package for parallelism, with the Global Interpreter Lock (GIL) managing concurrent execution. While GIL ensures data consistency, it can sometimes slow down processes.
On the other hand, Ruby initiates threads with Thread.new. Exceptions don’t typically halt the program; the thread quietly ends. Thread pools can limit active threads, and the Mutex class manages access to shared resources. Deadlocks are addressed with condition variables, and gems like ‘parallel’ and ‘workers’ can expedite multi-threaded application development in Ruby.
10. Performance
While both Python and Ruby exhibit impressive performance, Ruby often processes computer programs at a speed roughly double that of Python. In a head-to-head comparison using benchmark programs, Ruby finishes tasks in nearly half the time it takes Python. For instance, while Python requires 567.56 seconds to run an n-body program, Ruby accomplishes it in just 232.08 seconds.
However, this advantage in speed for Ruby comes with a trade-off in memory usage. Running the n-body program consumes 22,968 memory units in Ruby, whereas Python uses a more modest 8,076 units.
While loops
Image credits- Quora
Ruby takes 3.82 seconds,
Python takes 24.04 seconds.
Winner: Ruby.
Bigint Multiplication
Image credits- Quora
Ruby takes: 23.503788766s
Python takes: 40.691734s
Winner: Ruby.
Eratosthenes
Image credits- Quora
Ruby takes 2.394614951s
Python3 takes: 3.111802s
Winner: Ruby.
11. Average Developer’s Salaries
The varying popularity and demand between Ruby on Rails and Python shape developers’ salaries, with differences often based on geographical regions. Here’s a general salary comparison between Ruby engineers and Python developers for informed hiring decisions.
With its expansive application in web development, data science, AI, and machine learning, Python has seen a surge in demand for skilled developers.
As of the data from the StackOverflow Developer Survey in 2020, the median global salary for a Python developer stood at $59k.
The versatility of Python means that specialists in fields like data science or AI might command even higher salaries due to the specialized nature of their skills.
Ruby (particularly Ruby on Rails)
Image credits- stackoverflow
Ruby, mainly when associated with the Ruby on Rails web framework, has been a choice for many startups and tech companies.
The median annual salary for Ruby developers globally, according to the same 2020 survey, was $71k, placing it higher than Python in this context.
The potentially higher salary reflects the scarcity of seasoned Ruby on Rails developers compared to the broader pool of Python developers.
While Ruby developers, on average, had a higher median salary as of the 2020 data, it’s essential to consider the specific role, specialization, geographic location, and industry demand when evaluating these figures.
12. Latest Trends in 2023
In the dynamic landscape of web development, staying updated is paramount. Whether you’re already harnessing the power of Ruby on Rails (RoR) or contemplating using Python, being in tune with the newest developments is vital. As we venture into 2023, let’s delve into the emerging trends to redefine web development’s horizon.
Ruby Trends to Watch in 2023
Rails 7- Eagerly awaited by the RoR community, Rails 7 promises streamlined web development with various enhancements.
API-Only Applications- API-first approaches dominate 2023, with RoR ideally suited for crafting backend APIs for diverse front-end solutions.
Frontend Diversification- RoR’s move from server-side leans towards integrating frontend frameworks like React and Vue.js for dynamic UIs.
Containerization and Orchestration- RoR applications in 2023 will frequently utilize container solutions like Docker and orchestration via Kubernetes for scalability and maintenance.
Microservices Architecture- Monolithic apps are fragmenting into microservices, with RoR aiding in efficiently constructing and managing these more minor services.
Performance Optimization- RoR will witness advanced optimization techniques in 2023, focusing on database queries, caching, and HTTP/2 adoption.
Enhanced Security Practices- With evolving cyber threats, RoR will emphasize fortified security, incorporating regular audits and advanced authentication.
Sustainability and Green Computing- With environmental awareness, RoR developers will prioritize energy-efficient, environmentally-friendly application designs.
Serverless Integration- RoR’s adoption of serverless platforms, like AWS Lambda, streamlines development by shifting focus from infrastructure to pure coding.
Python Trends to Watch in 2023
Python 3.12- comes with Improved error messages and offers suggestions and guidance. Python’s PEG parser now supports enhanced f-strings. There are speed optimizations, including inlined comprehensions. A new syntax has been introduced for annotating generics with type variables. Additionally, Linux users can utilize the robust perf profiler.
Surge in AI, ML, and Data Analysis- Python is the preferred language for AI, ML, and data science due to its user-friendly nature and abundant libraries.
Python in Web Development- Frameworks like Django, Flask, and FastAPI amplify Python’s footprint in web development.
Rising Asynchronous Programming- Asyncio’s emergence is streamlining concurrent programming, fostering scalable and efficient applications.
Embracing Type Annotations- Adopting type hints in Python elevates code clarity and minimizes runtime glitches.
Evolution via PEPs- Python Enhancement Proposals (PEPs) continue to refine the language, targeting features, performance, and security.
Python in Serverless Architecture- The growing appeal of serverless computing sees Python rising as a top choice, credited to its versatility.
Python’s Foray into Robotics and IoT- Frameworks like MicroPython boost Python’s adoption in robotics and IoT, catering to limited-resource settings.
Pursuit of Performance with Cython and PyPy- Initiatives like Cython and PyPy are harnessed to supercharge Python applications, meeting high-performance demands.
Hybrid Development- Momentum Blending Python with languages like Rust and C++ is becoming prevalent to leverage the best of diverse tech stacks in projects.
Ruby vs. Python: Tabular Difference
Make the Right Choice with OnGraph Experts
When faced with “Ruby vs. Python, which should I choose?”, aligning with your project’s unique needs and objectives is essential.
Ruby excels in web app development, offering rapid prototyping, end-to-end action, and cost-effective solutions for business projects. On the other hand, Python opens doors to diverse applications, from embedding machine learning algorithms to streamlining DevOps tasks within projects.
Regardless of your choice, ensuring access to competent developers is crucial. If your team lacks expertise, consider recruiting remote professionals as a cost-effective and flexible solution. Partnering with OnGraph can connect you with vetted Python and Ruby experts skilled across various fields!
For more details, go through our portfolio page to see how we have helped companies develop leading web apps using Python and Ruby.
If you want your business to be a part of digital transformation, then migrating it to the cloud is the first step. Many businesses have migrated to the cloud, leveraging its immense benefits of pay-per-go resource utilization and reduced cost.
Then what is stopping you from being a part of it?
You will be amazed that the cloud migration market is growing with the global adoption of digital transformation. Cloud migration services were valued at USD 9.22 Billion in 2020 and are estimated to grow to USD 49.96 Billion by 2028, growing at a CAGR of 23.52% from 2021 to 2028.
Isn’t it amazing how the world is getting digitalized with the simple adoption of cloud technologies? Suppose you have yet to migrate your business and apps and are struggling to migrate to the cloud. In that case, you have landed on the right page, as we have gathered all the necessary information to help you understand its long-term business benefits and concerns related to cloud migration.
So, let’s get you started.
But wait, are you clear with the core term “cloud” here?
Before you think of migration, you must be clear about what cloud means. Simply put, the cloud is cloud computing with a vast pool of resources, services, and others over the internet. To avail of these services (database, storage, applications, servers, etc.), you only need an internet connection without any specific setup.
Image credits: Atlassian
Today, you will find several companies providing cloud services to others and even helping migrate their entire setup to the cloud with different plans.
However, understanding your business requirements is necessary before you start migration, as the cloud offers different services, and you must know what services you want to help your business scale. You need to know what other cloud services are there so you can choose your plan wisely and reduce your IT spending.
Different Types of Cloud Computing
Cloud computing provides instant access to various computing services and resources online. This encompasses a range of applications, services, and servers, all stationed at remote data centers operated by cloud service providers.
Different types of cloud structures cater to distinct business needs. Let’s delve into the main types of cloud solutions to help you determine the best fit for your enterprise.
Public Cloud
Image credits: netvault
These services are accessible to anyone via the Internet. They may be available at no cost or based on subscription or pay-as-you-go models. Cloud providers take on the responsibility of managing the data centers, hardware, and infrastructure, ensuring seamless operations for their users.
The public cloud market is anticipated to experience significant growth in 2022 thanks to its adaptability in accommodating fluctuating workloads.
It is a perfect fit for any business, so why do some companies opt for other options? If you have a scaling business that requires consistent resource demand, then the public cloud is not for you, as all the resources are shared over the network. If any other company exhausts all the help, it might be available at a different time, leaving your business to lose potential opportunities.
This is why we have another option- Private cloud.
Private Cloud
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With a private cloud, businesses will get dedicated resources, overcoming the shared resources issue of the public cloud. In a private cloud, the virtual environment is split into different backgrounds for various businesses.
Many businesses choose the private cloud as it is more secure and can have all resources dedicated to them to scale their business.
But some businesses are on a significant level that need private and public cloud benefits, so they go for Hybrid Cloud.
Hybrid Cloud
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For leveraging the benefits of both private and public cloud, Hybrid cloud is the better option for your business to scale efficiently. A hybrid cloud can connect your business’s private and public services into a single architecture so you can seamlessly run complex workloads.
Also, the hybrid cloud allows your business to switch between private and public clouds per changing business needs.
The core purpose of any business is to get necessary services within its budget. What if the company wants to avail of only some required assistance from a single cloud vendor as they get cheaper rates of a specific service from another vendor?
Of course, they want the best and cheapest services available. To accommodate such requests, multi-cloud and hybrid multi-cloud come to the rescue.
Multi-cloud
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With the multi-cloud option, the cloud can avail services from different cloud vendors (Amazon, Oracle, Salesforce, and others), eliminating vendor lock-in situations. This is why it is popular among businesses as they get suitable services, such as IaaS, PaaS, and SaaS, at a reasonable price without sticking to a single vendor.
This helps businesses to migrate among the cloud and vendors efficiently. Some companies might be wondering what are IaaS, PaaS, and SaaS. Well, these are core cloud computing services that you will be available. Let’s understand them in the next section.
Cloud computing services
The cloud offers unlimited services that are categorized into three categories- IaaS (Infrastructure-as-a-Service), PaaS (Platform-as-a-Service), and SaaS (Software-as-a-Service).
Let’s understand in detail how availing of such services can help your business save high costs in the long term.
SaaS (Software-as-a-Service)
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The cloud vendors host software on the cloud that companies can use directly. Anyone connected to that company network can use a good internet connection. It eliminates the need to physically install the software on each system on the company’s network. All you need is to pay as per your business requirement.
The teams can access that software from any location or device with a single click. You know what is the next best thing- no technical support and maintenance of that software is on you. All is handled by the cloud vendor, from updating, upgrading, and troubleshooting that software.
The software as a service (SAAS) market is expected to grow to $328.03 billion in 2027 at a CAGR of 6.6%.
PaaS (Platform-as-a-Service)
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The cloud vendors will provide on-demand platforms that include hardware, software, entire tech stack, infrastructure, servers, networks, storage, database, operating system software, and middleware.
It helps businesses to integrate different platforms without even managing their internal complexities. The cloud vendors will handle all the technical work so the company can focus on the core part.
Seeing the benefits, many businesses are adopting cloud PaaS services. The platform-as-a-service (PaaS) market is projected to expand at a compound annual growth rate (CAGR) of 26.42% from 2022 to 2027. An increase of approximately USD 80.82 billion in the market size is anticipated during this period.
IaaS (Infrastructure-as-a-Service)
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This service offers easy online access to basic computing like servers and storage.
IaaS lets companies adjust resources as their needs change and removes the need to own their infrastructure. After the pandemic, there was a significant increase in the use of IaaS cloud services worldwide.
Some leading IaaS providers in the market are-
Image Credits: Statista
Reasons to Migrate to the Cloud
Migrating to the cloud offers a variety of advantages for businesses and individuals alike. Here are the core benefits.
Cost Efficiency
Traditional IT infrastructure requires significant capital investment in hardware, software, and the physical space to house it. There are also ongoing costs for maintenance, energy, and hardware upgrades. With cloud computing, you typically operate on a pay-as-you-go model, allowing for predictable operational expenses and eliminating the need for hefty upfront investments. Maintenance costs are often included in the subscription price, further reducing overhead.
Scalability & Flexibility
Traditional infrastructure might require a complete hardware overhaul to scale up. The cloud, on the other hand, allows businesses to adjust resources based on real-time needs quickly. This ensures that during peak periods, the system can handle the extra load while you’re not paying for unused resources during off-peak times.
Disaster Recovery
Cloud providers typically have data centers in multiple locations. This redundancy means if one center faces an issue, your data can be quickly recovered from another area. This robust backup and disaster recovery solution would be costly and complex to implement on-premises.
Automatic Updates
Cloud providers take care of routine software and security updates, ensuring all users have access to the latest features and are protected from known vulnerabilities. This removes the burden and potential human error from manual updates.
Remote Access
The cloud facilitates a modern mobile workforce. Employees can access their work from home, during travel, or at client locations, improving productivity and flexibility.
Collaboration Efficiency
Cloud platforms often include collaboration tools that allow multiple team members to work on documents or projects in real-time. This boosts team efficiency and ensures everyone is on the same page.
Security
Cloud providers invest heavily in state-of-the-art security protocols. This includes encryption during transmission and while at rest, multi-factor authentication, and regular security audits.
While no system can be 100% secure, the expertise and resources of major cloud providers often exceed what a single organization can muster.
Resource Management
Managed cloud services relieve businesses from the technicalities of daily IT operations. This allows companies to focus on their core competencies instead of getting bogged down in IT challenges.
Competitiveness
With cloud platforms, small and medium-sized businesses can access sophisticated technology without a massive investment, allowing them to compete more effectively with larger corporations. This democratization of technology can be a game-changer in many industries.
These benefits are tempting for any business to consider cloud migration. If you know that migrating your business to the cloud will reduce your workload, reduce cost, enhance security, and much more, why should you not do it?
But the question arises- migrating to the cloud as easy as it sounds. It can be complex, but the right cloud migration partner can make it easy for you and handle all the complexities.
To understand the process of migration. How it works and what challenges you can face during migration.
Is Migrating to the cloud easy?
Moving to the cloud can be smooth if you plan well and understand your business needs. The cloud provider will handle the technical parts for you.
It’s essential to pick the right cloud partner. You might only get the desired results if you plan well.
Before migrating, ask yourself-
Where are you moving to?
Why are you making the move?
How will you do it?
Cloud Migration Process
It’s a good idea to have a checklist for your cloud migration. This way, you can track what’s done and what’s left, ensuring you keep all the data and avoid potential problems.
Here’s a simplified process-
Know your purpose of migration- Understand why you’re moving to the cloud. Everyone in your team, especially decision-makers, should know the benefits and reasons for the move.
Decide What and How to Move- Get to know the cloud you’re moving to. Figure out which applications you’ll transfer, any related dependencies, and the tools you’ll need.
Start the Migration- Begin with more straightforward tasks. Once you’ve moved, test your applications to ensure they work well and address any issues.
In short, a clear plan and understanding can make your cloud journey smooth and effective. Several migration strategies depend on your business situation. Let’s understand those strategies and choose the right one for your business.
The 6 Rs of Cloud Migration (strategies)
These popular 6R cloud migration strategies will fit all your business needs. You can help you choose the right one after understanding them in detail. With the right approach, your migration will be more feasible and accessible.
These strategies have been popularized by AWS (Amazon Web Services) but apply to any cloud migration effort.
Rehost (“Lift and Shift”)
This involves moving applications and data directly from the on-premises data center to the cloud without any modifications. It’s the quickest method but may only partially exploit cloud-native features.
Picture just lifting your entire room and placing it in a new house—no changes, no fuss. This is a straight move, and it’s fast. But, like when moving homes, sometimes you wish you’d rearranged a bit.
Replatform (“Lift, Tinker and Shift”)
This approach involves making a few cloud optimizations to realize a tangible benefit without changing the core application architecture. An example might be moving a database to a cloud-based managed database service.
Think of this as packing your stuff but getting a shiny new cupboard at the new place. You’re not changing everything but making a few tweaks to fit better.
Repurchase (The “New Start”)
This involves moving to a different product or service, often shifting to a cloud-native solution. A typical scenario is changing from a traditional CRM to a SaaS platform like Salesforce. Have you ever felt like leaving behind the old and starting afresh? This is your chance to switch to a brand-new set, like swapping out that old couch for a modern sectional.
Refactor / Re-architect (The “Home Makeover”)
This is about reimagining how an application is architected and developed using cloud-native features. It’s the most involved strategy and is often undertaken to add scalability, flexibility, or other cloud-specific benefits.
Imagine redesigning your room in the new house entirely. This is for those looking for a fresh, modern vibe, taking advantage of the latest home decor – or, in this case, cutting-edge cloud features!
Retire (The “Declutter”)
Identify IT assets that are no longer useful and can be turned off. This helps save costs and reduces the complexity of migration. If you’ve got stuff you no longer need, why take it with you? This is the Marie Kondo method – if it doesn’t “spark joy” (or isn’t helpful), leave it behind.
Retain (The “Not Now”)
Not all applications will be migrated immediately. Some might be kept in their current state for various reasons, such as an impending phase-out or regulatory constraints. The idea is to keep these applications and data in the current environment without migrating them.
Some items have sentimental value or might be needed later. So, for now, they remain where they are. It’s okay; we all have those pieces we’re not ready to part with or move.
Challenges during Cloud Migration
It can be challenging if you are migrating your business from traditional architecture. Sometimes, it includes developing an entire system from scratch. Not only this, there are a few list of challenges that any business can face despite full-proof understanding.
System Outages
During the migration, there might be times when your local servers need to be paused. This can impact how well your applications run and test the patience of your loyal customers. It’s essential to have robust backups and proper resource planning to prevent significant disruptions.
Risk of Data Compromise
When transitioning to the cloud, your data can be especially exposed. There might be moments when specific data is inaccessible or prone to security threats. Enhancing protection during this phase is critical, employing measures like advanced access controls and ensuring data encryption.
Adjusting to the New Environment
Some IT staff might be wary of the cloud, having been accustomed to traditional server management. Adapting to the cloud might mean retraining some team members or reshaping specific IT roles. This transformation can also touch on the fundamental processes of your business operations.
Ensuring Seamless Communication
Marrying your existing applications with the new cloud setting can be a challenge. To maintain smooth system interoperability, it’s crucial to tweak and align your procedures with your cloud provider’s environment.
Experience Seamless Cloud Migration with OnGraph.
The day when every business leverages cloud-based solutions is just around the corner. With the right tools, any organization can embrace the cloud, simplifying the workflow for professionals across all sectors.
Businesses can shift to the cloud multiple times or transition between providers based on evolving needs. However, it’s crucial to identify which cloud solutions align best with your business objectives, the specific services you wish to utilize, and the nature of your chosen cloud provider.
The advantages of cloud migration often unfold over time. Seamlessly expanding resources in tandem with business growth brings immense satisfaction, especially to visionary leaders who championed the migration journey. to make your cloud migration journey easier, you need expert hands.
Market research is a crucial component of advancing knowledge in various fields. It encompasses a range of methodologies, including qualitative and quantitative research. There has always been confusion about which method to adopt for data analysis.
The actual difference lies in the type of data they collect and analyze.
Qualitative research explores subjective experiences, meanings, and interpretations through interviews and observations. On the other hand, quantitative research relies on numerical data and statistical analyses to uncover patterns and relationships.
Understanding these research approaches provides researchers with a comprehensive toolkit to analyze and interpret data, ensuring rigorous and insightful conclusions.
For those who are still struggling through their applications and usage, we will be highlighting in and out of both methodologies to help you understand and implement better.
What is Data Analysis in Market Research?
Research data analysis refers to examining, organizing, interpreting, and deriving meaningful insights from collected data. It involves applying various statistical and analytical techniques to conclude, identify patterns, test hypotheses, and make informed decisions based on the data.
Data analysis helps researchers make sense of the information gathered and provides a systematic approach to uncovering relationships, trends, and underlying meanings within the data. It is crucial in validating research findings, informing research questions, and understanding the research topic.
Need to Analyze Data in Market Research
Data analysis is a critical component of research for several reasons.
Drawing Conclusions- Analyzing data allows researchers to draw valid and reliable conclusions based on the collected information. It helps summarize the findings, identify patterns, and understand the relationships between variables.
Validating Research Questions and Hypotheses- Data analysis helps researchers evaluate the validity of their research questions and hypotheses. By examining the data, researchers can determine whether the evidence supports or refutes their initial assumptions and expectations.
Making Informed Decisions- Data analysis provides the necessary information for making informed decisions. Whether in academia, business, or policymaking, analyzing data allows researchers to identify trends, patterns, and insights that tell decision-making processes.
Enhancing Research Validity- Through data analysis, researchers can improve the validity of their research. By employing appropriate statistical techniques and methods, they can ensure that the findings are reliable, replicable, and representative of the studied population.
Identifying Research Gaps and Future Directions- Analyzing data helps researchers identify gaps in the existing knowledge and potential avenues for future research. By examining the results, researchers can gain insights into areas that require further investigation or can be expanded upon in subsequent studies.
Type of Data in Research: Qualitative Vs. Quantitative Market Research
Understanding Qualitative Market Research
Qualitative research is an approach that aims to explore and understand the complexities of human experiences, behaviors, and social phenomena. It seeks to uncover meanings, patterns, and underlying motivations by delving into the subjective interpretations and context of the participants.
Characteristics of Qualitative Market Research
Subjectivity– Qualitative research recognizes the subjective nature of human experiences and acknowledges the role of the researcher’s perspective in shaping the understanding of the phenomena under study.
Contextual understanding- Qualitative research emphasizes the importance of studying phenomena within their natural settings and social contexts to gain a holistic experience.
In-depth data collection– Researchers employ techniques like open-ended interviews, participant observations, and detailed document analysis to collect rich and complex data.
Emergent design- Qualitative research often involves a flexible and iterative method that allows for exploring new themes, ideas, and research questions as data collection and analysis progress.
Interpretive analysis– Qualitative research involves analyzing data by identifying patterns, themes, and categories and constructing narratives or explanations to make sense of the findings.
Small sample sizes- Qualitative research often focuses on in-depth exploration with a relatively small number of participants, aiming for detailed insights rather than statistical representation.
Qualitative Data Analysis
Qualitative data analysis systematically explores and interprets non-numerical data, such as text, images, and interviews. It involves coding, categorizing, and identifying patterns to uncover rich insights and understand the underlying meanings. Through carefully examining the data, qualitative data analysis helps researchers gain a deeper understanding of the research topic and generate meaningful narratives that capture the complexities of human experiences and perspectives.
There are two different approaches to Qualitative data analysis.
Below are the steps to do qualitative research.
There are different methods to conduct qualitative research.
Why do we need Qualitative Data Analysis in Market Research?
Below are the reasons we need qualitative data analysis.
In-depth and contextual understanding– Qualitative research allows for a comprehensive exploration of complex phenomena, providing detailed insights into the depth and richness of human experiences, behaviors, and social interactions.
Rich data collection– Qualitative research methods, such as interviews and observations, enable researchers to gather rich and descriptive data, capturing participants’ nuances, context, and subjective perspectives.
Flexibility and adaptability– Qualitative research offers flexibility in data collection, allowing researchers to adapt their approach and research questions during the study to explore emerging themes or unexpected findings.
Participant perspectives– Qualitative research focuses on understanding the attitudes and meanings attributed by participants, giving voice to their experiences and allowing for a more inclusive and diverse understanding of the phenomenon.
Theory development– Qualitative research contributes to theory development by generating new concepts, hypotheses, or theories based on qualitative data analysis, allowing for exploring novel ideas and perspectives.
Validity and authenticity– Qualitative research prioritizes establishing trustworthiness and credibility through methods such as prolonged engagement, triangulation of data, member checking, and reflexivity, enhancing the validity and authenticity of the findings.
Exploratory and hypothesis-generating– Qualitative research is experimental, making it valuable in the early stages of research when little is known about a phenomenon, helping to generate hypotheses for further investigation.
Real-world applications– Qualitative research findings inform real-world applications in healthcare, education, social work, marketing, and policy-making, providing practical insights and guiding evidence-based decision-making.
Limitations of Qualitative Market Research
It is important to note that these limitations do not diminish the value and significance of qualitative research but rather highlight the considerations and trade-offs associated with this research approach.
Limited generalizability– Qualitative research often involves small sample sizes and focuses on specific contexts, making it difficult to generalize findings to larger populations or different settings.
Potential for researcher bias– The subjective nature of qualitative research introduces the possibility of researcher bias in data collection, analysis, and interpretation, impacting the objectivity of the findings.
Time-consuming data collection and analysis– Qualitative research requires substantial time and resources for data collection through interviews, observations, and document analysis. The analysis process of qualitative data can be time-consuming due to the need for detailed coding and interpretation.
Lack of quantifiable data– Qualitative research does not rely on numerical data and statistical analysis, which can limit the ability to measure and quantify phenomena, making it challenging to establish statistical relationships.
Limited statistical analysis– Qualitative research does not typically employ statistical tests and measurements, which may limit the ability to draw statistical conclusions or analyze quantitative relationships.
Understanding Quantitative Market Research
Quantitative research is a systematic and empirical approach that collects and analyzes numerical data to understand and explain phenomena. It involves using statistical methods to draw objective conclusions and generalize findings to larger populations.
As per Statista, below are the traditional quantitative methods used in the market research industry worldwide in 2022.
Image Credits: Statista
Characteristics of Quantitative Market Research
Objective measurement– Quantitative research relies on standardized and measurable variables, allowing for precise and accurate data collection.
Numerical data collection– Researchers gather numerical data through structured surveys, experiments, or existing datasets to quantify and analyze relationships, patterns, and trends.
Statistical analysis– Quantitative research utilizes statistical techniques to analyze data, including measures of central tendency, correlation, regression, and inferential statistics to make statistical inferences.
Generalizability– Quantitative research aims to generalize findings from the sample to the larger population, seeking to draw broader conclusions and make predictions.
Control over variables– Researchers often strive to control extraneous variables and manipulate independent variables to establish cause-and-effect relationships.
Large sample sizes– Quantitative research typically involves larger sample sizes to ensure statistical power and enhance the representativeness of findings.
Replicability– Quantitative research emphasizes the ability to replicate studies to test the robustness and reliability of findings, contributing to scientific validity.
Deductive reasoning– Quantitative research often follows a reasoned approach, starting with a hypothesis or theory tested and refined through data analysis.
Quantitative research is widely used in psychology, economics, sociology, and natural sciences, where numerical data and statistical analysis are instrumental in understanding patterns and relationships and making evidence-based decisions.
Quantitative Data Analysis
Quantitative data analysis is like uncovering the secrets of numbers. It’s a process of crunching data, exploring statistical relationships, and unlocking patterns and trends. Rigorous analysis allows researchers to make sense of large datasets and draw precise conclusions. With the power of numbers, quantitative data analysis empowers decision-making, reveals insights, and provides a quantitative lens to understand the world around us.
Different methods for quantitative data analysis are-
Why do we need Quantitative analysis in Market Research?
There are several ways in which Quantitative research is helpful for Research purposes.
Objectivity and reliability– Quantitative research focuses on measurable data, promoting objectivity and increasing the reliability of findings as multiple researchers can replicate the study and obtain similar results.
Generalizability– With larger sample sizes, quantitative research allows for the generalization of findings to broader populations, enhancing the external validity of the research.
Statistical analysis– Quantitative research employs statistical methods that enable precise analysis, facilitating the identification of patterns, correlations, and causal relationships among variables.
Efficiency in data analysis– Quantitative data analysis can be automated and conducted using software, enhancing the efficiency of analyzing large datasets and reducing the potential for human errors.
Precise measurement– Quantitative research provides numerical data, allowing for accurate measurement of variables, which is particularly useful in fields where precise quantification is necessary, such as physics or economics.
Trend identification– Quantitative research can detect trends and changes over time, facilitating the identification of long-term patterns or shifts in behaviors, attitudes, or phenomena.
Evidence-based decision-making– Quantitative research provides empirical evidence that supports evidence-based decision-making in various domains, such as healthcare, policy-making, and business strategy development.
Limitations of Quantitative Market Research
It is essential to recognize that these limitations do not undermine the value of quantitative research but rather highlight the considerations and trade-offs associated with its application in specific research contexts.
Lack of depth and context– Quantitative research often focuses on numerical data, which may limit understanding of complex phenomena and fail to capture the depth and context of human experiences or social interactions.
Limited exploration of new concepts– Quantitative research may not be suitable for exploring new or emerging ideas, as it relies on predefined variables and measures, potentially overlooking essential aspects of the phenomenon.
Restrictive data collection methods– Quantitative research relies heavily on structured surveys or experiments, which may limit the types of data collected compared to more flexible qualitative methods.
Potential for researcher bias- Despite the objective nature of quantitative research, researcher bias can still influence study design, data collection, and interpretation, potentially compromising the validity of the findings.
Difficulty capturing context-specific factors– Quantitative research may need help to account for unique contextual factors that can significantly influence outcomes, as it often emphasizes generalizability over context specificity.
Inability to explore complex social phenomena– Some social phenomena, such as power dynamics, cultural meanings, and subjective experiences, may be challenging to quantify accurately, limiting the insights provided by quantitative research.
Potential for measurement errors– Quantitative research relies on the accurate measurement of variables, and errors in data collection or measurement instruments can affect the reliability and validity of the findings.
Tabular Difference: Quantitative vs Qualitative Market Research
The above table provides a general overview of qualitative and quantitative research’s main characteristics and differences. The actual practice and implementation may vary depending on the research design and field of study.
Real-world example: Combining the power of Qualitative and Quantitative Market Research
Understanding Customer Satisfaction in an E-commerce Company
For example, a large e-commerce company aimed to enhance customer satisfaction and improve its services. They employed qualitative and quantitative data analysis methods to gain comprehensive insights.
Qualitative Research
The company conducted in-depth interviews with customers to understand their experiences, preferences, and pain points. The interviews were transcribed and analyzed using thematic analysis. Themes such as delivery speed, product quality, and customer support emerged, providing a deeper understanding of customer perceptions.
Quantitative Research
A survey was designed and distributed to a more extensive customer base to collect quantitative data. The survey included Likert-scale questions to measure satisfaction levels and demographic questions for segmentation purposes. Statistical analysis techniques, such as descriptive statistics and correlation analysis, were used to examine the relationships between customer satisfaction and various factors.
Outcomes and Insights
The combined analysis of qualitative and quantitative data provided valuable outcomes and insights. The qualitative analysis revealed areas where the company needed improvement, such as enhancing customer support and streamlining the delivery process. The quantitative analysis showed overall satisfaction levels, identified customer segments with different preferences, and highlighted factors significantly influencing satisfaction.
The company implemented targeted strategies to address the identified issues based on the findings. They improved their customer support system, optimized the delivery process, and introduced personalized recommendations based on customer segments. As a result, customer satisfaction increased, leading to higher customer loyalty, positive word-of-mouth, and improved business performance.
Improve Market Research with OnGraph
The debate between qualitative and quantitative market research methods regarding data analysis is not a matter of one being inherently superior to the other. Both approaches have unique strengths and limitations, and their appropriateness largely depends on the research objectives, the nature of the phenomenon under investigation, and the available resources.
Ultimately, the choice between qualitative and quantitative research should be driven by the research question and the study’s specific needs, with the recognition that a combination of both methods can often yield the most comprehensive and robust understanding of a phenomenon.
We have the tools to help you make the best from qualitative and quantitative methodologies. So drop us a query today and see the results yourself.
Today, next-gen e-commerce personalization is the topmost priority of every customer while they shop. A practical personalization approach can help you achieve that.
Welcome to the new era of e-commerce, where personalized experiences are the key to unlocking customer satisfaction. For years, businesses have pursued a seamless, cross-channel experience that adapts to real-time individual needs.
But why is there so much emphasis on providing a personalized experience to each customer?
As per a study conducted by McKinsey in 2021, customers are willing to pay more for brands offering customized or personalized services.
The good news? We now have the technologies to make it happen.
The bad news? With a flood of options offering varying levels of personalization, designing the perfect user experience can feel overwhelming.
But fear not! Our expert team is here to guide you. Let’s navigate this exciting landscape together and create tailored experiences that leave a lasting impact. Let’s redefine e-commerce with high-level personalization.
What do you mean by E-commerce Personalization?
E-commerce personalization is tailoring the online shopping experience to meet individual customers’ unique needs, preferences, and goals. It goes beyond a one-size-fits-all approach and aims to create a highly personalized and engaging experience for each shopper.
To elevate the customer shopping experience, personalization is the key to the success of every business. This is why marketers explicitly emphasize implementing customized experiences for their customers.
At its core, e-commerce personalization involves leveraging customer data, advanced technologies, and analytics to deliver relevant content, recommendations, and real-time offers across various channels. By understanding customer behavior, purchase history, demographics, and preferences, businesses can customize product displays, website layouts, marketing messages, and more to cater to customers’ interests and desires.
Some stats that will justify the adoption of personalization.
Personalized experiences make a significant impact!
Personalization works wonders for marketers, resulting in a significant 20% boost in sales.
A whopping 80% of shoppers are more inclined to purchase from a company that offers personalized experiences.
When customers receive personalized shopping experiences, around 60% will likely become loyal repeat buyers.
An impressive 78% of consumers have preferred brands that deliver personalized services, often choosing, recommending, or even paying more. Interestingly, over half (53%) of digital experience professionals need more technology to implement effective personalization.
Let’s unlock the full potential of personalization together!
Benefits of E-commerce Personalization
The increasing love for personalization is the main reason why marketers are opting for it. But that’s not it. Personalization brings a ton of benefits to the eCommerce industry that we are going to explain.
Here are the top advantages of personalization in eCommerce.
Increased Sales and Conversion Rates
As per a study, including targeted content for your services will bring around a 20 percent increase in sales and better opportunities. Personalization helps your business to target leads and convert them successfully. This means that through personalization, you are helping your customers to go through the services efficiently and find the right services they are looking for.
For example, if you curate the content of your landing page after understanding the customer’s preference, it will bring more leads, resulting in increased engagement and conversion.
That’s what you exactly want from your customers.
Enhanced User Engagement
Maximizing customer engagement requires a personalized content approach in today’s digital landscape. From tailored product recommendations to curated experiences designed specifically for each user, personalization is critical. It signals to customers that they are seen and heard individually, fostering loyalty and encouraging return visits.
Customizing the shopping experience empowers businesses to present products most likely purchased by each customer, resulting in higher conversion rates. Personalized emails and videos have shown up to five times higher click-through rates than non-personalized communications, driving conversions to new heights.
Delight with Satisfaction
Customer satisfaction is a top priority for online businesses. Personalized experiences play a pivotal role in elevating satisfaction levels, with studies revealing that 80% of customers make multiple purchases from a brand offering customized services and experiences.
Understanding individual needs allows businesses to offer tailored products and services, surpassing customer expectations and leading to increased satisfaction.
Improved Customer Loyalty
Having a list of loyal customers is a dream of every eCommerce business.
If personalization is the key to turning that dream into reality, why not unthinkingly go for it?
Almost 70 percent of customers are more likely to be loyal to brands that prioritize their needs and customize a whole new experience for them. Making your customers feel essential will do the rest.
So take care of your customers, understand their needs, tailor your services, and customize them for persistent customers. The probability of selling products to loyal customers is up to 70 percent compared to the new ones. So do not leave your existing customers in search of new customers.
Offer customers an experience they would love
If customers do not find what they seek, they tend to leave. About 65 percent of customers go to the brand due to poor experience and look for alternatives.
Creating personalized experiences opens the door to even greater profitability and margin from high-value customers with the highest lifetime value.
By offering tailored and exclusive experiences, such as product recommendations based on zero-party data or special discounts and exclusive events, businesses can bring short- and medium-term value to their brand and build lasting relationships with customers who could become lifelong advocates.
Embrace the power of personalization to ignite engagement, drive conversions, enhance satisfaction, cultivate loyalty, and captivate high-value customers. Let data-driven insights guide you toward creating extraordinary experiences that leave a lasting impression.
Best Tactics for E-commerce Personalization
Discover the perfect eCommerce personalization tactics tailored for your business. Simplify your journey with a handpicked list of easily implementable tactics, even if you’re just starting.
Let’s make personalization work wonders for you!
Behavioural Personalization
What do you mean by behavioral personalization? Businesses mainly use it to analyze the user’s behavior about any displayed personalized offer on their website or app.
Let us explain with an example. Suppose you run an online store, and a user visits it to look for a specified product, a shoe. However, the user returns without making a purchase. Then you will send a personalized notification to the user like “Amazing offer on the go, Take Nike shoes at FLAT 70% OFF”.
This notification will tempt the user to go through the app again and look for products that are on sale and most likely to make a purchase.
Behavioral personalization is more likely to improve the user’s experience. Some tools like HubSpot are available to implement behavioral personalization and create unique campaigns to increase page view time.
Checkout Personalization to extend user’s buying journey
Boosting sales in eCommerce through personalization is no secret, and one effective way is through target upsells, cross-sells, and downsells. Each technique uses strategies to increase the customer’s average order value (AOV).
Upsells: Suggest higher-priced products that customers have purchased or added to their cart.
Cross-sells: Offer related items based on customer browsing or past purchases, irrespective of pricing.
Downsells: Recommend less expensive alternatives to what customers have chosen.
These techniques work best during three key moments: before, during, and immediately after checkout. At these times, customers are excited, ready to purchase, and more likely to add complementary products. Let’s explore successful examples of companies that excel in these personalized strategies.
For example-
Amazon (Cross-Sell)
Image Credits: optinmonster
They are offering products that are related to the products present in the cart. The motive is not to down-sell. Instead, the effects are linked, boosting AOV from the specific customer.
Their strategy of offering low-priced items before going to the check-out page will increase the chance that customers will add them as they have already added high-priced items to their cart, resulting in more probability of adding those items.
Personalize Product Recommendations
In the tech age, intelligent product recommendations have become easier with the implementation of AI in the right place. It gathers customer data based on their interactions on your website, enabling personalized suggestions for products that align with their interests.
While some may view this as another typical eCommerce feature, it’s worth noting that a significant 35% of Amazon’s sales stem from product recommendations.
The key to converting customers through product recommendations lies in their relevance to each individual. When customers add items to their cart, they seize the opportunity to showcase product recommendations that align with their current preferences.
Fresh Foam demonstrates this intelligent approach by offering similar products on its product description page.
Image Credits: convertcart
Location-specific Product Recommendation
According to a survey, 9 out of 10 marketers have attested that location-specific marketing led to an astounding 86% increase in their customer base. This form of targeting holds excellent value as it considers cultural disparities and the unique needs associated with specific locations, resulting in customers showing a more substantial interest and preference for particular products.
Enhance your eCommerce store by implementing geo-targeting, which allows you to gather location information when customers arrive on your website.
By employing geo-targeting, you can tailor your marketing efforts to specific areas, focusing on available and relevant products to customers in those locations. This approach eliminates the need to display an entire catalog that may not be accessible or suitable for those areas.
An excellent example of this strategy in action can be seen with Madewell. As customers land on their website, they collect location data to deliver a personalized user experience, ultimately improving conversion rates.
Image Credits: Madewell
Personalized email in Real-time
Regarding email campaigns, behavior-triggered emails outshine other types with an impressive 53% higher click-to-open rate. This statistic indicates that customers highly appreciate and find value in these emails.
Behavior-triggered emails focus on cultivating a more extensive base of loyal customers by providing them with valuable content even before they realize they need it. Within your eCommerce store, you can target customers who have recently made a purchase that typically requires restocking. Leveraging insights from their past purchasing behavior, you can identify these customers.
Whether or not they have made multiple purchases, targeting them just before they would typically restock is beneficial. By sending a gentle email reminder, you significantly decrease the likelihood of them seeking their next purchase elsewhere.
Look at how Sephora encourages customers to replenish their previous purchases, providing a helpful nudge to keep their beauty routines on track.
Image Credits: Sephora
Leveraging dynamic landing pages
A customized landing page that caters to a customer’s interests and needs has proven more effective in converting mobile users than general landing pages. When a page is tailored to their preferences, they are likelier to take the desired action or engage with the provided call-to-action (CTA).
To maximize its holiday traffic and increase sales, Sabon took the initiative to implement dynamic landing pages that offered different holiday promotions targeted at specific audience segments.
Image Credits: Sabon
Each landing page was designed to cater to the behavior and interaction patterns of other groups of visitors on their website. As a result of this implementation, Sabon witnessed a significant 35% boost in their Black Friday sales and a 20% increase in the value of orders placed through their landing pages.
By utilizing behavioral dynamic landing pages, Sabon gained valuable insights. They discovered that their returning customers were inclined to click on individual landing pages that provided value based on their previous purchases.
Personalized Coupons
Do your loyal customers only hit your online store for a short time? What is keeping them away?
Send them customized coupons to persuade them and try re-engaging inactive customers to make sales. For example, Royal Canin analyzed their customer’s data and adopted a personalized coupon strategy for their customers. It resulted in driving around 74 percent of customers, improving brand visibility.
So, keep track of your CRM data to design personalized coupons for better customer engagement.
Image Credits: royalcanin
Leveraging Chatbots for additional sales support
ECommerce transactions have taken a new fold today, projected to amount to $112 billion by 2023. One of the top brands, Amazon, is leveraging its trendy gadget “Alexa” as its shopping assistant on their app, making it easier for customers to add items directly to their cart and place orders.
This is the future, or prepare yourself with high-end chatbot integration for personalization. Below is an example of how eBay leverages shopbots to improve customers’ shopping experience.
Image Credits: eBay
Well, tons of tactics can suit your business needs. All you need is to think out of the box and understand how you can leverage today’s technology to make it better for your customers.
Companies Benefitting from E-commerce Personalization
Not only startups but fully established businesses are also leveraging the power of personalization in e-commerce. See who those businesses will entice you to go for personalization.
It is a brand everyone uses and knows about—thanks to their fantastic personalization tactics. One of the effective tactics is their cross-sell strategy, which we have already highlighted, from personalizing emails and notifications to personalized scratch vouchers after making payments.
Amazon has taken personalization seriously and excels in what they do and sells.
Need help finding the right product for you? Flipkart helps its customers by recommending product alternatives based on your recent history. This will provide you with a list of items that you might consider.
This strategy ends up selling the product much faster than any other tactic, saving many customers time in finding the right product.
With their dynamic pricing tactic with location-based personalization, they help their customers find the accurate amount they have to pay for their CRM services.
Personalization has more scope based on what technology you are using to make it work. As the technology expands, companies can leverage them and make customer’s experience better than ever.
Go for E-commerce Personalization with OnGraph.
In a world where customers crave personalized experiences, OnGraph emerges as a game-changer in e-commerce personalization. By harnessing the power of advanced recommendation engines, dynamic content personalization, and intelligent search capabilities, OnGraph empowers businesses to deliver exceptional customer engagement.
Elevate your e-commerce game and unlock the true potential of personalization with OnGraph. Leap and revolutionize your customer experience today!
Blockchain Technology in web development has been in the market for a very long but got people’s attention during COVID-19. Due to its increasing demand and security aspects, it a huge buzzword in every IT sector. Today, global businesses are adopting blockchain technology which is the major factor in increasing blockchain technology development costs.
But, do you know how much it costs to develop a blockchain App?
Well, the answer is not a definite number. It depends on your purpose and your requirements for the app. If you want to dig deeper and find out what factors significantly affect blockchain development cost, then you are on the right page.
We have gathered all the stats and figures for you to understand how much you need to invest to make a next-gen, tech-led blockchain app. For that, we need to understand the blockchain market, factors affecting cost, and more.
Consistently Evolving Blockchain Market: A Quick Peek
Today, major market sectors are adopting blockchain technology to stay in the race. To compete with them, you need an out-of-the-box blockchain app that can significantly help your business grow and stand out. This is why there has been tremendous growth in blockchain projects globally.
As per Grand View Research, In 2022, the worldwide market for blockchain technology was worth $10.02 billion. This market is predicted to expand quite rapidly, with an average yearly growth rate of 87.7% from 2023 to 2030. This growth is likely because more and more investors are putting their money into businesses that work with blockchain technology.
As you can see the increasing trends of blockchain adoption over the last few years, blockchain development might not be easy. The cost will definitely rise going from simple to complex, tech-integrated apps.
So what do you think could be the average cost of developing a blockchain app? Let’s simplify the Blockchain app cost.
Cost of a Blockchain App Development
Different industries have different app requirements, features, and technologies to be used. So, what could be the rough estimate for you? No idea?
Well, on average, developing a Blockchain app can cost from $40,000 to $300,000, and beyond. Different factors contribute to the cost of blockchain app development, which we will discuss in the next section.
This is a rough estimate, these numbers can increase with the app’s complexity.
So as per your understanding, what could be other significant factors that can impact the blockchain development cost? Let’s explore those factors with us.
Top 5 Factors Affecting the Cost of Blockchain App Development
1. The size of the Agency
If you are outsourcing the development of your blockchain application, then the size of the agency matters a lot and can impact the overall cost of your app development. Generally, there are three categories of agencies in the market- Small Cap Agencies, medium-sized agencies, and Large Size Agencies.
Image Credits: Oyelabs
Small Cap Agencies- a group of up to 50 blockchain developers ready to bring your ideas to life. Although they might not be the most seasoned experts out there, their fresh perspective in the industry shouldn’t be underestimated. Plus, their competitive pricing provides a great opportunity for those on a budget. A word of caution though – if you have an exceptionally large or complex project, they might find it a challenge to manage. Choose them if you appreciate the hustle of growing talent and the freshness of new perspectives.
Medium Size Agencies- Blockchain App Development Companies boasting a battalion of 500-1000 blockchain developers. With their impressive resume of experience, they promise nothing short of quality service. It’s true, that their rates might be a step above those of the small-cap agencies, but what you invest in cost, you reap in assurance and expertise. Select them if you’re after a blend of solid experience and dedicated service.
Large Size Agencies- Large Size Companies featuring a staggering roster of 1000+ blockchain developers. Their wealth of experience is as vast as their team size, making them pros at handling sizeable projects with finesse. Their rates might be on the higher end, but in return, you’ll gain access to a powerhouse of knowledge and capability. Choose them if your project demands the might and mettle of the industry’s most seasoned veterans.
2. Industry-based Blockchain App Development Cost
Do you know, that Blockchain has made its impact on almost every industry in the market?
Considering blockchain app development, your industry decides the price tag. Take the banking sector, with its layers of complexity and need for tight security – developing a blockchain app here would naturally carry a higher cost compared to, say, the healthcare sector.
Similarly, the intricate web of supply chain management presents its own challenges that call for more advanced security features. As a result, it’s costlier to build a blockchain app here than it is for the more straightforward retail sector. In essence, the complexity and security requirements of your target industry directly influence the cost of your blockchain app development.
3. Complexity Level of Blockchain App
While creating a blockchain app, several factors come into the picture that must not be neglected. Such factors might increase the complexity of the app, resulting in increased cost.
So what factors define the complexity level of the Blockchain App?
Let’s discuss each factor in depth.
Goal- Before diving into blockchain app development, focus in on your ‘why’. Understand the user’s pain points, explore current solutions, and assess how a blockchain app could up the ante. Ask yourself – why does the world need this blockchain innovation and how can your app raise the bar? Answering these questions will guide your choice of development services. For any enterprise stepping into the blockchain arena, this initial clarity is invaluable.
Consensus Mechanism- Think of Consensus Mechanisms as the rulebook for how a blockchain network agrees on its information. There are a bunch of different types, like Proof of Work that Bitcoin uses, or others might use Proof of Stake, Delegated Proof of Stake, Proof of Elapsed Time, or Federated. Each type has its own balance of security, decentralization, cost, and efficiency, so it’s important to pick the one that fits your needs best.
There’s more to consider, such as who can use the system, how new assets are made and managed, how transactions are handled and confirmed, and the format of addresses and signatures. This is a big step, so if you’re new to all this, it could be a good idea to get advice from a company that specializes in making blockchain apps.
Platforms to Build Blockchain Apps- You can build blockchain applications on various platforms like Hyperledger Fabric, Ethereum, and others, each with its own special tricks and tools. The platform you pick will influence how complicated your application ends up being. So, it’s a bit like choosing the ingredients for a recipe – the choice directly affects the final dish!
Many businesses find it challenging to find the right platform for their blockchain app development. To make the choice easier, below is the checklist for choosing the right platform based on your requirements.
Image Credits: 101blockchains
Tools and Technology Stack
Below is the list of all tech stacks that can be implemented within each layer of blockchain.
Layer 3 (APIs)- Covalent, QuickNode, The Graph, Bitquery, Alchemy, and Biconomy
Layer 4 (Web3 and Web2 Dev Tools and Platforms)- Firebase, Supabase, and PlayFab
Layer 5 (Dapps)- DeFi dashboards, DEXs, identity and authentication dapps, NFT marketplaces, data handling dapps, MetaMask, and many others.
Every stack has its own good points and not-so-good points. This means you need to choose the best mix of stacks to make your development work really shine.
Third-party Tools
APIs- You’ll find many ready-made APIs out there that can help with the development process. But sometimes, you might need to create your own API for specific tasks like checking and keeping track of data, making key pairs and addresses, storing and retrieving data, or managing how smart contracts interact with the system.
UI & UX- After you’ve figured out all the backend stuff, it’s time to make a control panel for the UI and Admin. This is where you decide on the right front-end coding language, servers, and external databases for your app.
POC or MVP- Blockchain is still pretty new, so it’s often better to start with an MVP (Minimum Viable Product) approach. This means building and testing a simple version of your app that just includes the core features, rather than trying to build the whole thing at once.
4. Blockchain App Development Category
The world of blockchain applications basically splits into two categories.
Cryptocurrency-based Solutions- Imagine apps built using a system where everyone keeps track of all transactions together, with no need for a middleman. This kind of system is called a decentralized ledger. It can help keep costs down when developing blockchain apps.
Non-cryptocurrency-Based Solutions- These apps work a bit differently. They use a system where one main player is in charge of checking and approving all transactions. This is known as a centralized ledger. Because of this setup, it can be a bit pricier to develop these kinds of blockchain apps.
In short, whether the ledger is run by everyone or run by one can affect the cost and design of your blockchain application.
5. Blockchain App Development Services
The type of blockchain app services you need can affect the cost. Here are some popular ones.
ICO Development- Initial coin offerings, or ICOs, help blockchain startups get funding. It takes both tech and marketing skills to create an ICO, which can be expensive. Costs can include things like designing a website, writing a whitepaper, and creating a smart contract. Marketing an ICO can be costly too because you need to reach a lot of people. Expect to spend around $10K to $15K on this.
Smart Contract Development- Smart contracts are a big deal in blockchain because they let transactions happen without a middleman, saving on fees. However, creating smart contracts can be tricky and time-consuming. So when you’re planning your budget, remember to include around $5,000 for this.
Cryptocurrency Exchange Development- Building a place for people to trade cryptocurrencies can be a challenge, requiring high security and functionality, and that means higher costs. However, if you want your users to trade cryptocurrencies, then it’s a must-have. The cost for a basic exchange can range from $50,000 to $98,000.
Wallet Development- A crypto wallet holds users’ private keys, which lets them send and receive cryptocurrencies. Because this involves sensitive data, wallet development can be tricky and pricey. This could cost you about $15,000 to $150,000.
NFT Marketplace- Finally, there’s the NFT marketplace, a place where users can buy and sell non-fungible tokens (NFTs) online. These represent an authenticity certificate related to online or physical assets.
Remember, figuring out the exact cost of a blockchain app isn’t easy. You’ll first need to decide what kind of blockchain app you want for your business.
How Much Does it Cost to Hire Blockchain Developers?
With the increase in the development of blockchain apps, the demand for blockchain developers has increased.
A blockchain developer must possess various skills that also impact their cost.
Strong programming skills in Java, Solidity, Python, PHP, etc.
Web development skills
Proficiency in data structure and concepts
Skilled in developing smart contracts
Cryptography skills
Such a combination of skills also increases the developer’s cost. As per Codementor.io, the average and medium hourly rates of blockchain developers are shown in the below image.
All these costings can be improved and optimized if you choose the right agency offering better pricing with expertise in every domain of Blockchain app development.
Fast-track your Blockchain Software Development with OnGraph
No matter the size or complexity, we’re ready to assist with every blockchain development service your business requires. Our talented team, equipped with 15+ years of experience, creates decentralized blockchain systems that pave the way for new business strategies and ensure greater transparency in data and transactions. From crafting smart contracts to developing apps, OnGraph (a blockchain development company in the USA) offers a comprehensive range of blockchain development services.
Scale your most complex AI and Python workloads with Ray, a simple yet powerful Parallel and distributed computing framework.
Can you imagine the pain of training complex machine learning models that take days or even months depending on the amount of data you have? What if you can train those models within minutes to a maximum of a few hours? Impressive, right? Who does not want that?
But the question is how?
This is where Python Ray comes to your rescue and helps you train models with great efficiency. Ray is a superb tool for effective distributed Python to speed up data processing and ML workflows. It leverages several CPUs and machines that process the code parallelly and process all the data at lightening fast speed.
This comprehensive Python Ray guide will help you understand its potential usage and how it can help ML platforms to work efficiently.
Let’s get you started.
What is Ray?
Ray is an open-source framework designed to scale AI and Python applications, including machine learning. It simplifies the process of parallel processing, eliminating the need for expertise in distributed systems. Ray gained immense popularity in quick time.
Do you know that top companies are leveraging Ray? Prominent companies such as Uber, Shopify, and Instacart utilize Ray.
Spotify leveraging Ray
Ray helps Spotify’s data scientists and engineers access a wide range of Python-based libraries to manage their ML workload.
Image Credit: Anyscale
Understanding Ray Architecture
The head node in a Ray cluster has additional components compared to worker nodes.
The Global Control Store (GCS) stores cluster-wide information, including object tables, task tables, function tables, and event logs. It is used for web UI, error diagnostics, debugging, and profiling tools.
The Autoscaler is responsible for launching and terminating worker nodes to ensure sufficient resources for workloads while minimizing idle resources.
The head node serves as a master that manages the entire cluster through the Autoscaler. However, the head node is a single point of failure. If it is lost, the cluster needs to be re-created, and existing worker nodes may become orphans and require manual removal.
Each Ray node contains a Raylet, which consists of two main components: the Object Store and the Scheduler.
The Object Store connects all object stores together, similar to a distributed cache like Memcached.
The Scheduler within each Ray node functions as a local scheduler that communicates with other nodes, creating a unified distributed scheduler for the cluster.
In a Ray cluster, nodes refer to logical nodes based on Docker images rather than physical machines. A physical machine can run one or more logical nodes when mapping to the physical infrastructure.
Ray Framework
It is possible with the help of the following low-level and high-level layers. Ray framework lets you scale AI and Python apps. It comes with a core distributed runtime and set of libraries (Ray AIR) that simplifies ML computations.
Image Credits: Ray
Scale ML workloads (Ray AI Runtime)- Ray provides ready-to-use libraries for common machine learning tasks such as data preprocessing, distributed training, hyperparameter tuning, reinforcement learning, and model serving.
Build Distributing Apps (Ray Core)- It offers user-friendly tools for parallelizing and scaling Python applications, making it easy to distribute workloads across multiple nodes and GPUs.
Deploy large-scale workloads (Ray Cluster)- Ray clusters consist of multiple worker nodes that are connected to a central Ray head node. These clusters can be configured to have a fixed size or can dynamically scale up or down based on the resource requirements of the applications running on the cluster. Ray seamlessly integrates with existing tools and infrastructure like Kubernetes, AWS, GCP, and Azure, enabling the smooth deployment of Ray clusters.
Ray and Data Science Workflow and Libraries
The concept of “data science” has evolved in recent years and can have different definitions. In simple terms, data science is about using data to gain insights and create practical applications. If we consider ML, then it involves a series of steps.
Data Processing– Preparing the data for machine learning, if applicable. This step involves selecting and transforming the data to make it compatible with the machine learning model. Reliable tools can assist with this process.
Model Training- Training machine learning algorithms using the processed data. Choosing the right algorithm for the task is crucial. Having a range of algorithm options can be beneficial.
Hyperparameter Tuning– Fine-tuning parameters and hyperparameters during the model training process to optimize performance. Proper adjustment of these settings can significantly impact the effectiveness of the final model. Tools are available to assist with this optimization process.
Model Serving– Deploying trained models to make them accessible for users who need them. This step involves making the models available through various means, such as using HTTP servers or specialized software packages designed for serving machine learning models.
Ray has developed specialized libraries for each of the four machine-learning steps mentioned earlier. These libraries are designed to work seamlessly with Ray and include the following.
Ray Datasets- This library facilitates data processing tasks, allowing you to efficiently handle and manipulate datasets. It supports different file formats and store data as blocks rather than a single block. Best used for data processing transformation.
Run the following command to install this library.
pip install ‘ray[data]’
Ray Train- Designed for distributed model training, this library enables you to train your machine-learning models across multiple nodes, improving efficiency and speed. Best used for model training.
Image Credits: Projectpro
Run the following command to install this library.
pip install ‘ray[train]’
Ray RLlib– Specifically built for reinforcement learning workloads, this library provides tools and algorithms to develop and train RL models.
Ray Tune– If you’re looking to optimize your model’s performance, Ray Tune is the library for efficient hyperparameter tuning. It helps you find the best combination of parameters to enhance your model’s accuracy.
Ray tune can parallelize and leverage multiple cores of GPU and multiple CPU cores. It optimizes the hyperparameter tuning cost by providing optimization algorithms. Best used for Model hyperparameter tuning.
Run the following command to install this library.
pip install ‘ray[tune]’
Ray Serve– Once your models are trained, Ray Serve comes into play. It allows you to easily serve your models, making them accessible for predictions or other applications.
Run the following command to install this library.
pip install ‘ray[serve]’
Ray benefits Data Engineers and Scientists
Ray has made it easier for data scientists and machine learning practitioners to scale apps without having in-depth knowledge of infrastructure. It helps them in
Parallelizing and distributing workloads- You can efficiently distribute your tasks across multiple nodes and GPUs, maximizing the utilization of computational resources.
Easy access to cloud computing resources- Ray simplifies the configuration and utilization of cloud-based computing power, ensuring quick and convenient access.
Native and extensible integrations- Ray seamlessly integrates with the machine learning ecosystem, providing you with a wide range of compatible tools and options for customization.
For distributed systems engineers, Ray handles critical processes automatically, including-
Orchestration- Ray manages the various components of a distributed system, ensuring they work together seamlessly.
Scheduling- It coordinates the execution of tasks, determining when and where they should be performed.
Fault tolerance- Ray ensures that tasks are completed successfully, even in the face of failures or errors.
Auto-scaling- It adjusts the allocation of resources based on dynamic demand, optimizing performance and efficiency.
In simple terms, Ray empowers data scientists and machine learning practitioners to scale their work without needing deep infrastructure knowledge, while offering distributed systems engineers automated management of crucial processes.
The Ray Ecosystem
Image Credits: Thenewstack
Ray’s universal framework acts as a bridge between the hardware you use (such as your laptop or a cloud service provider) and the programming libraries commonly used by data scientists. These libraries can include popular ones like PyTorch, Dask, Transformers (HuggingFace), XGBoost, or even Ray’s own built-in libraries like Ray Serve and Ray Tune.
Ray occupies a distinct position that addresses multiple problem areas.
The first problem Ray tackles is scaling Python code by efficiently managing resources such as servers, threads, or GPUs. It accomplishes this through essential components: a scheduler, distributed data storage, and an actor system. Ray’s scheduler is versatile and capable of handling not only traditional scalability challenges but also simple workflows. The actor system in Ray provides a straightforward method for managing a resilient distributed execution state. By combining these features, Ray operates as a responsive system, where its various components can adapt and respond to the surrounding environment.
Reasons Top Companies Are Looking For Python Ray
Below are significant reasons why companies working on ML platforms are using Ray.
A powerful tool supporting Distributed Computing Efficiently
With Ray, developers can easily define their app’s logic in Python. Ray’s flexibility lies in its support for both stateless computations (Tasks) and stateful computations (Actors). A shared Object Store simplifies inter-node communication. This allows Ray to implement distributed patterns that are way beyond the concept of simple data parallelism, which involves running the same function on different parts of a dataset simultaneously. In case of the machine learning applications, Ray supports more complex patterns.
Image Credits: Anyscale
These capabilities allow developers to tackle a wide range of distributed computing challenges in machine learning applications using Ray.
An example that demonstrates the flexibility of Ray is the project called Alpa, developed by researchers from Google, AWS, UC Berkeley, Duke, and CMU for simplifying large deep-learning model training.
Sometimes a large model cannot fit on the same device like a GPU, this type of scaling requires partitioning a computation graph across multiple devices distributed on different servers. These devices perform different types of computations. This parallelism involves two types: inter-operator parallelism (assigning different operators to different devices) and intra-operator parallelism (splitting the same operator across multiple devices).
Image Credits: Anyscale
Alpa brings together different ways of doing multiple tasks at once by figuring out and doing the best ways to split up and do things both within and between steps. It does this automatically for really big deep-learning models that need lots of computing power.
To make all this work smoothly, the creators of Alpa picked Ray as the tool for spreading out the work across many computers. They went with Ray because of its capability to handle different ways of doing things at once and make sure the right tasks are done on the right computers. Ray is the perfect fit for Alpa because it helps it run big and complex deep-learning models efficiently and effectively across many computers.
Few lines of code for complex deployments
Ray Serve, also known as “Serve,” is a library designed to enable scalable model inference. It facilitates complex deployment scenarios including deploying multiple models simultaneously. This capability is becoming increasingly crucial as machine learning models are integrated into different apps and systems.
With Ray Serve, you can orchestrate multiple Ray actors, each responsible for providing inference for different models. It offers support for both batch inference, where predictions are made for multiple inputs at once, and online inference, where predictions are made in real time.
Ray Serve is capable of scaling to handle thousands of models in production, making it a reliable solution for large-scale inference deployments. It simplifies the process of deploying and managing models, allowing organizations to efficiently serve predictions for a wide range of applications and systems.
Efficiently scaling Diverse Workload
Ray’s scalability is a notable characteristic that brings significant benefits to organizations. A prime example is Instacart, which leverages Ray to drive its ML pipeline for large-scale completion. Ray empowers Instacart’s ML modelers by providing a user-friendly, efficient, and productive environment to harness the capabilities of expansive clusters.
With Ray, Instacart’s modelers can tap into the immense computational resources offered by large clusters effortlessly. Ray considers the entire cluster as a single pool of resources and handles the optimal mapping of computing tasks and actors to this pool. As a result, Ray effectively removes non-scalable elements from the system, such as rigidly partitioned task queues prevalent in Instacart’s legacy architecture.
By utilizing Ray, Instacart’s modelers can focus on running models on extensive datasets without needing to dive into the intricate details of managing computations across numerous machines. Ray simplifies the process, enabling them to scale their ML workflows seamlessly while handling the complexities behind the scenes.
Another biggest example is OpenAI.
Scaling Complex Computations
Ray is not only useful for distributed training, but it also appeals to users because it can handle various types of computations that are important for machine learning applications.
Graph Computations: Ray has proven to be effective in large-scale graph computations. Companies like Bytedance and Ant Group have used Ray for projects involving knowledge graphs in different industries.
Reinforcement Learning: Ray is widely used for reinforcement learning tasks in various domains such as recommender systems, industrial applications, and gaming, among others.
Processing New Data Types: Ray is utilized by several companies to create customized tools for processing and managing new types of data, including images, video, and text. While existing data processing tools mostly focus on structured or semi-structured data, there is an increasing need for efficient solutions to handle unstructured data like text, images, video, and audio.
Supporting Heterogeneous Hardware
As machine learning (ML) and data processing tasks continue to grow rapidly, and the advancements in computer hardware are slowing down, hardware manufacturers are introducing more specialized hardware accelerators. This means that when we want to scale up our workloads, we need to develop distributed applications that can work with different types of hardware.
One of the great features of Ray is its ability to seamlessly support different hardware types. Developers can specify the hardware requirements for each task or actor they create. For example, they can say that one task needs 1 CPU, while an actor needs 2 CPUs and 1 Nvidia A100 GPU, all within the same application.
Uber provides an example of how this works in practice. They improved their deep learning pipeline’s performance by 50% by using a combination of 8 GPU nodes and 9 CPU nodes with various hardware configurations, compared to their previous setup that used 16 GPU nodes. This not only made their pipeline more efficient but also resulted in significant cost savings.
Image Credits: Anyscale
Use Cases of Ray
Below is the list of popular use cases of Ray for scaling machine learning.
Batch Interface
Batch inference involves making predictions with a machine learning model on a large amount of input data all at once. Ray for batch inference is compatible with any cloud provider and machine learning framework. It is designed to be fast and cost-effective for modern deep-learning applications. Whether you are using a single machine or a large cluster, Ray can scale your batch inference tasks with minimal code modifications. Ray is a Python-centric framework, making it simple to express and interactively develop your inference workloads.
Many Model Training
In machine learning scenarios like time series forecasting, it is often necessary to train multiple models on different subsets of the dataset. This approach is called “many model training.” Instead of training a single model on the entire dataset, many models are trained on smaller batches of data that correspond to different locations, products, or other factors.
When each individual model can fit on a single GPU, Ray can handle the training process efficiently. It assigns each training run to a separate task in Ray. This means that all the available workers can be utilized to run independent training sessions simultaneously, rather than having one worker process the jobs sequentially. This parallel approach helps to speed up the training process and make the most of the available computing resources.
Below is the data parallelism pattern for distributed training on large and complex datasets.
Image Credits: Ray
Model Serving
Ray Serve is a great tool for combining multiple machine-learning models and business logic to create a sophisticated inference service. You can use Python code to build this service, which makes it flexible and easy to work with.
Ray Serve supports advanced deployment patterns where you need to coordinate multiple Ray actors. These actors are responsible for performing inference on different models. Whether you need to handle batch processing or real-time inference, Ray Serve has got you covered. It is designed to handle large-scale production environments with thousands of models.
In simpler terms, Ray Serve allows you to create a powerful service that combines multiple machine-learning models and other code in Python. It can handle various types of inference tasks, and you can scale it to handle a large number of models in a production environment.
Hyperparameter Tuning
The Ray Tune library allows you to apply hyperparameter tuning algorithms to any parallel workload in Ray.
Hyperparameter tuning often involves running multiple experiments, and each experiment can be treated as an independent task. This makes it a suitable scenario for distributed computing. Ray Tune simplifies the process of distributing the optimization of hyperparameters across multiple resources. It provides useful features like saving the best results, optimizing the scheduling of experiments, and specifying different search patterns.
In simpler terms, Ray Tune helps you optimize the parameters of your machine-learning models by running multiple experiments in parallel. It takes care of distributing the workload efficiently and offers helpful features like saving the best results and managing the experiment schedule.
Distributed Training
The Ray Train library brings together various distributed training frameworks into a unified Trainer API, making it easier to manage and coordinate distributed training.
When it comes to training many models, a technique called model parallelism is used. It involves dividing a large model into smaller parts and training them on different machines simultaneously. Ray Train simplifies this process by providing convenient tools for distributing these model shards across multiple machines and running the training process in parallel.
Reinforcement Learning
RLlib is a free and open-source library designed for reinforcement learning (RL). It is specifically built to handle large-scale RL workloads in production environments. RLlib provides a unified and straightforward interface that can be used across a wide range of industries.
Many leading companies in various fields, such as climate control, industrial control, manufacturing and logistics, finance, gaming, automobile, robotics, boat design, and more, rely on RLlib for their RL applications. RLlib’s versatility makes it a popular choice for implementing RL algorithms in different domains.
In simpler terms, the Ray Train library makes it simple to manage distributed training by combining different frameworks into one easy-to-use interface. It also supports training multiple models at once by dividing the models into smaller parts and training them simultaneously on different machines.
Experience Blazing-fast Python Distributed Computing with Ray
Ray’s powerful capabilities in distributed computing and parallelization revolutionize the way applications are built. With Ray, you can leverage the speed and scalability of distributed computing to develop high-performance Python applications with ease.
OnGraph, a leading technology company, brings its expertise and dedication to help you make the most of Ray’s potential. OnGraph enables you to develop cutting-edge applications that deliver unparalleled performance and user experiences.
With OnGraph, you can confidently embark on a journey toward creating transformative applications that shape the future of technology.
Are your survey Programming not effective enough to drive accurate and reliable information? Beat complex survey challenges with our best survey designing tips to improve survey quality and get better customer insights.
Discovering your audience’s preferences throughout their customer journey can be effectively accomplished through surveys. These valuable tools enable you to swiftly gather feedback from a large number of individuals within a short span. The logistics involved are comparatively straightforward- you design and deploy the survey, respondents participate at their convenience, and you subsequently analyze the collected data.
Surveys provide a wealth of information concerning your audience, encompassing various aspects such as.
Purchasing process- What information do customers require to fulfill their needs? Where do they seek such information? What kind of details aid them in making purchasing decisions?
Website satisfaction- Why do customers visit your company’s website? Can they easily access the desired information? Are there additional resources or tools you can offer them?
Post-event feedback- What is your customers’ perception of your company’s trade show booth? What aspects did they find most valuable? Is there specific information they were seeking but did not find?
Surveys are crucial for gathering information, hence it’s worth investing our time and effort to make them more engaging and innovative to support their purpose. So businesses must not overlook poor-quality surveys and seek the help of experts to improve survey quality.
Why Business Must Improve Survey Quality?
Improving survey quality can bring numerous benefits across various domains. Here are some key advantages.
Reliable insights
Improving survey quality ensures that the insights obtained are representative and unbiased. It helps minimize errors, sampling biases, and methodological flaws that can distort the results. Reliable insights enable organizations to make informed decisions, formulate effective strategies, and address specific challenges with greater precision.
Enhanced credibility
High-quality surveys enhance the credibility and reputation of the organizations conducting them. When surveys are designed and implemented with rigor and transparency, stakeholders perceive the organization as trustworthy and competent. This credibility can foster stronger relationships with customers, clients, employees, and other key stakeholders.
Improved customer satisfaction
By conducting surveys with better quality, organizations can gain a deeper understanding of customer needs, preferences, and satisfaction levels. This information helps identify areas for improvement, develop more targeted marketing strategies, and deliver better products and services. Ultimately, improved customer satisfaction leads to increased loyalty, repeat business, and positive word-of-mouth.
These advantages can positively impact decision-making, organizational performance, and the overall quality of products, services, and interventions.
Below are the best and proven tips to improve survey quality within no time.
5 Proven Tips to Improve Survey Quality
Tip 1- Goal-specific Survey Improves Survey Quality
When creating surveys, it is crucial to have a clear purpose in mind. Whether you are seeking feedback on your customers’ experience or aiming to identify the main challenges faced by your target audience, every survey should serve a specific goal. Unfortunately, many companies make the mistake of sending out lengthy and vague surveys that cover a wide range of topics, from website experience to shipping prices. This approach is likely to annoy your customers.
To obtain meaningful answers to your questions, it is advisable to divide your surveys into shorter questionnaires, each focusing on a single objective. By doing so, you can gather the necessary data more efficiently and simplify the process of developing questions.
Maintaining a clear focus on your goals can also improve other aspects of survey design, such as avoiding ambiguous and overly complex questions. By ensuring your survey content is engaging and relevant to your customers, you increase their willingness to participate. For further insights and statistical references, you can refer to the related resources.
Clear survey purpose enables-
Appropriate question selection
Elimination of ambiguity
Focus on goal-oriented questions
Inclusion of highly relevant questions
Tip 2- Say No to Jargon
Different respondents and customers attempt surveys from novice to experienced persons. If you do not make sure that your language suits every person out there, you might lose your valuable customers and their thoughts. It is better that you speak everyone’s language. Connect with your audience by using their preferred language.
When crafting your questions, it’s important to tailor them to your specific target audience. Choose wording and terminology that your respondents will easily comprehend. Steer clear of using internal jargon that may confuse them. If you do use abbreviations, make sure to provide a clear explanation of their meaning. By ensuring your questions are easily understood, you enable your respondents to provide more accurate and meaningful responses.
For example-
In the below example, using “AR” might confuse many people as they might have different understandings. They will make assumptions that will impact their answers, resulting in vague results.
In the second part, where we mentioned “AR” as “ Augmented Reality” will clarify the scenario and people will be able to answer correctly, resulting in accurate results.
In most scenarios, it is better that you describe it clearly so that even the novice can answer in a better way.
Image Credits: Pollfish
Tip 3- Is your Survey too Lengthy? Trim it now!
Customers do not want to engage much time completing surveys. They get bored easily if they have to attend lengthy surveys. Result? Quitting the survey in the middle.
This might severely damage your results. To maximize respondent engagement, it is crucial to ensure that your survey is concise. It is important to have a clear understanding of the specific information you wish to gather.
According to Cameron Johnson, business segment leader at Nextiva, research suggests that surveys that can be completed within five minutes or less tend to yield the highest response rates. Conversely, surveys that exceed 11 minutes in length are deemed too long and are likely to result in higher rates of abandonment or, even worse, random responses.
In essence, if your survey is excessively long, individuals are likely to expedite their responses in order to complete it quickly. They may not thoroughly contemplate their answers and could potentially misinterpret the questions, thus negatively impacting the quality of your data. By keeping your survey concise, you can achieve improved outcomes and foster greater engagement from your audience.
Tip 4- Curate Survey Questions Smartly
Are your survey questions making sense? Making sense at all? Or making dual sense that might confuse customers?
Then you must work on your words and curate questions that present a clear picture of what exactly you are looking for. The choice of words can greatly impact the quality of responses and prevent biased or confusing answers. Below are some valuable tips for creating effective survey questions.
Use unbiased questions that avoid leading respondents to a particular answer. For instance, instead of asking, “When do you enjoy using this product?” which introduces bias, ask, “When was the last time you used this product?”
Image Credits: Forms.app
Pose one question at a time to avoid confusion and obtain clearer results. Often, surveys bundle multiple questions together, leading to muddled responses. For example, rather than combining “Do you prefer this product? If so, why?” into a single question, separate them. First, inquire about product usage and then ask for the reasons behind the preference.
Image Credits: Pollfish
Offer balanced response options to ensure unbiased results. Use answer scales that include an equal number of positive and negative choices, with a neutral option in between. A well-balanced scale could include options like Excellent, Good, Average, Below Average, and Poor.
Image Credits: dataforceresearch
If you’re uncertain about the clarity of your questions, enlist a test group to take the survey. Seek feedback from colleagues or friends to identify any potential confusion and make necessary adjustments.
Tip 5- Adding Question Variety to Improve Survey Quality
When it comes to gathering information, active engagement is the key. By incorporating a variety of question types, such as ranking, matrix, open-ended, or multiple-choice questions, you can captivate your audience and maintain their interest, even in a lengthier survey.
This requires a clear understanding of different types of survey questions and how can you use them to support your results, ensuring survey quality.
Open-ended or Close-ended questions
Image Credits: embedsocial
Multiple-choice questions
Image Credits: embedsocial
Dichotomous questions
Image Credits: embedsocial
Rating-scale questions
Image Credits: embedsocial
Such questions will keep the boredom away and help customers to engage throughout surveys irrespective of the length but do not exaggerate the survey length. Leverage the right survey creation tool that will turn your creative ideas into reality and improve survey quality.
Improve Survey Quality with OnGraph’s Customized Survey Creation Tool
Unleash the power of surveys to understand your audience! Customize questions to match research objectives and gather vital information. Write in a language that resonates with your audience. Simplify survey participation with diverse question types, logical flow, and skip logic. Seek a fresh perspective from a colleague to spot any confusion.
Elevate your insights and connect with your audience like never before with the right customized Survey Creation Tool at your fingertips. We have experts who can custom-build tools to turn your survey expectations into reality for better and more accurate results. That’s why businesses must not overlook survey quality for any reason.
If you have implemented other tips and tricks or something better in mind to improve survey quality, let us know via comments. We will be glad to hear.
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