Optimizely is an American company that provides digital experience platform software as a service. Optimizely provides A/B testing and multivariate testing tools, website personalization, and feature toggle capabilities, as well as web content management and digital commerce.

Optimizely - Wikipedia

Optimizely | 113,683 followers on LinkedIn. The all-in-one system for marketing—purpose-built to help teams create &amp; optimize personalized digital experiences, fast | At Optimizely, we&#x27;re on a mission to help people unlock their digital potential. We do that by reinventing how marketing and ...

Optimizely | LinkedIn

Optimizely is a digital experience platform for unlocking digital potential, content, commerce, and optimization.

Optimizely - Crunchbase Company Profile & Funding

Optimizely was founded in 2009 by Dan Siroker and Pete Koomen. It became synonymous with A/B testing, subsequently building a broader suite of tools for marketers to experiment with and personalize their websites and apps, with more than 1,000 customers, including Gap, StubHub, IBM and The ...

Optimizely acquired by content management company Episerver | ...

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Welcome, an Optimizely Co

Optimizely has 5 employees across 23 locations and $259.63 m in total funding,. See insights on Optimizely including office locations, competitors, revenue, financials, executives, subsidiaries and more at Craft.

Optimizely Company Profile - Office Locations, Competitors, Revenue, ...

Creating digital experiences that transform your company takes data-driven decisions, continued experimentation and constant invention.

The home of exceptional digital experiences - Optimizely

Information on valuation, funding, acquisitions, investors, and executives for Optimizely. Use the PitchBook Platform to explore the full profile.

Optimizely Company Profile: Funding & Investors | PitchBook

Wealthfront also named Optimizely as one of &quot;100 Career Launching Technology Companies.&quot;​ Visit our Careers page to find out how you can be a part of this growing company. Headquartered in San Francisco, Optimizely also has offices in Amsterdam, Cologne, London and New York City.

Optimizely: Deliver relevant experiences driven by experimentation ...

57,804 companies use Optimizely. Optimizely is most often used by companies with 1-10 employees &amp; $1M-10M in revenue. Our usage data goes back 7 years and 11 months.

Companies using Optimizely and its marketshare

See what developers are saying about how they use Optimizely. Check out popular companies that use Optimizely and some tools that integrate with Optimizely.

Optimizely - Reviews, Pros & Cons | Companies using Optimizely

Information on acquisition, funding, investors, and executives for Optimizely (Acquired). Use the PitchBook Platform to explore the full profile.

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Check out this list of the top Optimizely companies. See company benefits, info, interviews and more at Built In.

Top Optimizely Companies 2024 | Built In

Optimizely does just what its company name says: help businesses improve the look of their websites and applications by giving them options that are tested

Optimizely | Company Overview & News

Find company research, competitor information, contact details &amp; financial data for Optimizely, Inc. of New York, NY. Get the latest business insights from Dun &amp; Bradstreet.

Optimizely, Inc. Company Profile | New York, NY | Competitors, ...

Optimizely - Cloud-based platform for digital experience management. Acquired by Episerver. Raised a total funding of $203M over 9 rounds from 60 investors.

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Find contact information for Optimizely. Learn about their Custom Software &amp; IT Services, Business Services market share, competitors, and Optimizely&#x27;s email format.

Optimizely Company Profile | Management and Employees List

Optimizely is an A/B Testing, Software, and Software Development company_reader located in New York, New York with $71 million in revenue and 1,398 employees. Find top employees, contact details and business statistics at RocketReach.

Optimizely: Contact Details and Business Profile

Optimizely is focused on unlocking digital potential and we are the recognized category leader in Digital Experience Platform (DXP) and created the category for A/B Testing and experimentation software

At Optimizely, we're on a mission to help people unlock their digital potential. We do that by reinventing how marketing and product teams work to create and optimize digital experiences across all channels. With our leading digital experience platform (DXP), we help companies around the world orchestrate their entire content lifecycle, monetize every digital experience and experiment across all customer touchpoints. Optimizely has 700+ partners and nearly 1500 employees across our 21 global offices. We are proud to help more than 10,000 businesses, including H&M, PayPal, Zoom, Toyota and Vodafone, enrich their customer lifetime value, increase revenue and grow their brands. At Optimizely, we live each day with a simple philosophy: large enough to serve, small enough to care.

Optimizely

By Director of Product Management Jon Noronha Photo by on rawpixel Unsplash This post originally appeared on the . , Hacker Noon’s weekly sponsor, is the world’s leader in digital experience optimization, allowing businesses to dramatically drive up the value of their digital products, commerce, and campaigns through its best in class experimentation software platform. Optimizely enables product development teams to accelerate innovation, lower the risk of new features, and drive up the return on investment from digital by up to 10X. Optimizely Blog Optimizely This is the final post in our Product Experimentation Pitfalls blog series, written by Optimizely’s Director of Product Management, Jon Noronha. See for more information on this 5-part series. here So far in , I’ve highlighted several pitfalls on the path to product experimentation. We’ve seen how some common mistakes can , , , or . But what if you already have a budding culture of experimentation and you’re seeing a steady stream of successful tests? this series slow down your testing stall your velocity send your team in circles lead you down the wrong path entirely Congratulations! You’re in for an exciting ride. Great experiments kick off a virtuous cycle: they inspire others to question their own assumptions, and that in turn locks in a culture of hypothesis thinking that spreads through an organization. I’m often amazed by how quickly a company can go from 1 experiment a month, to 10, to 100. Unfortunately, this progression is never perfectly smooth. As you run more tests, collect more data, and include more people in the process, a whole new set of challenges starts to appear. This final post is all about those pitfalls — the scaling challenges that can stop a growing testing culture in its tracks. To illustrate them, I’ll be sharing examples from two titans of the testing world: Booking.com and Airbnb. Experimenting in more places Booking is a company that’s famous for designing its entire development culture around constant experimentation. But like every testing program, they started with humble beginnings. The first version of their experimentation platform only supported running a handful of experiments at a time, and when they got started a decade ago the team couldn’t imagine the scale they’d eventually hit. Now fast forward to the present. Overall, on a daily basis, all members of our departments run and analyse more than a thousand concurrent experiments to quickly validate new ideas. These experiments run across all our products, from mobile apps and tools used by hoteliers to customer service phone lines and internal systems. Experimentation has become so ingrained in Booking.com culture that every change, from entire redesigns and infrastructure changes to bug fixes, is wrapped in an experiment…Such democratization is only possible if running experiments is cheap, safe and easy enough that anyone can go ahead with testing new ideas, which in turn means that the experiment infrastructure must be generic, flexible and extensible enough to support all current and future use cases. Through all this exponential growth, Booking has had to continuously revamp its testing infrastructure to support new kinds of experiments. At each stage of growth, new teams brought new touchpoints and new challenges. From website conversion optimization, they expanded to native mobile testing, and from there to experiments deep in the backend technology stack. Today, Booking has a team of over 40 developers and statisticians continuously working on improving their testing platform. At Optimizely, we’ve taken our own version of this journey. Like Booking, we’ve devoted 8+ years and tens of millions of dollars in R&D to expand our “experimentation footprint.” From simple website testing, we’ve followed the lead of the savviest teams to add and across . Along the way, we’ve had to work through maddeningly subtle problems. Experiments have to work everywhere, but they also have to be , , , , , and . If any one of those things goes wrong, in any part of the stack, it can instantly undermine years of trust built up in a testing culture. native mobile experimentation server-side testing 10 different languages rigorous performant secure compliant consistent reliable If there’s one lesson I can distill from that journey, it’s this: don’t choose your testing platform based on the first experiment you want to run, or even the tenth. Think about where your hundredth or thousandth test might run — and make sure that you’re technology that can scale far beyond it. building or buying Collecting more data Scaling experimentation doesn’t just mean supporting new use cases: it also means gathering more data. A more data. Enough to take your cleverly designed analytics pipeline and blow it up, over and over again. I’ve lived through this particular challenge three or four times now, so in a strange way, it was comforting to read about Airbnb’s long saga of lot scaling their Experiment Reporting Framework (ERF): The number of concurrent experiments running in ERF has grown from a few dozen (in 2014) to about 500…More impressively, the number of metrics computed per day has grown exponentially…Today we compute ~2500 distinct metrics per day and roughly 50k distinct experiment/metric combinations. The post lays out a series of challenges along the way: Early on, all experiment analysis happened through a simple script that ran once a day. This all worked fine, until one day it started taking more than 24 hours to run. Suddenly, they were dealing with “a swamped Hadoop cluster and dissatisfied end users.” (“Dissatisfied” is probably an understatement. When the same thing happened to us at Microsoft, we’d start a test on Monday and not see data until Thursday. Everyone went ballistic.) To unclog the pipes, they realized they had to rebuild the entire backend. So they made a whole new technology called Airflow — now a thriving . And it worked: analysis times went from over 24 hours to under 45 minutes. Problem solved! open-source project Just kidding: “Adoption immediately began to thrive. This led to more experiments and a huge influx of new metrics. Things quickly got out of hand with some experiments adopting 100 or more metrics.” Soon, it all got slow again, and on top of that, the UI became hopelessly overcrowded. So they switched to a new system that precomputed more data. Problem…solved? Not exactly. “It was hugely successful, but left users wanting to dive deeper into metrics. To accommodate this need, we launched…” — ok, I think we all get the idea. I love this story because at every stage, Airbnb’s platform was a victim of its own success. As experimentation got easier, users expected more from it! And as the data became more critical to running the business, analysts expected to get deeper insights and generate them faster than ever. At Optimizely, we’ve certainly felt this pressure. Our engineers have spent many sleepless nights helping sites like The New York Times and The Gap sustain experimentation through traffic spikes like Election Day and Black Friday. Through it all, we’ve learned that it’s essential to build infrastructure that can handle billions of incoming events — while also supporting open-ended exploration through , , and . And it has to be fast, which is why we’ve always insisted on real-time results. If something breaks on Monday, you can’t afford to wait until Tuesday to find out. Or as Booking says: segmentation advanced metrics other interactions Sometimes experiments introduce such severe bugs, removing for example the ability for certain customers to book, that the overall business is immediately impacted. Therefore, it must be possible to attribute, in real time (in our current system that means less than a minute), the impact of every experiment on both the overall business and the health of the systems. Adding more experimenters When I talk to engineers at Booking and Airbnb, they’re justifiably proud of the way they’ve overcome these technical hurdles. But when I ask where they still struggle, I hear the same answer: “It’s not about the technology, it’s about teaching people to use it.” No matter how solid the code is, it’s still so easy to and . At Airbnb, “consistency of experiments and avoiding mistakes is a big problem.” Booking has steered a large part of its effort from helping teams _run_tests to helping teams and them: pick the wrong metrics get tricked by statistics plan document Enabling everyone to come up with new hypotheses is key to democratizing experimentation and moving away from a product organization where only product managers decide what features to test next. Therefore, Booking.com’s experiment platform acts as a searchable repository of all the previous successes and failures, dating back to the very first experiment, which everyone can consult and audit. We’ve seen the same challenges at Optimizely, which is why we were so excited to join forces with last year. In January, we released to offer our customers their own version of Booking’s idea repository. Already, we’re seeing teams like and IBM turbo-charge their culture by allowing everyone in the organization to , them by impact and effort (and !), from past experiments, and measure . Experiment Engine Optimizely Program Management the BBC submit ideas score love record lessons program velocity More broadly, I’ve been struck again and again by how important usability and reusability are for sustaining a culture of experimentation. Before Optimizely, I would hack together A/B tests by writing my own randomized bucketing in code and sifting through the results by querying raw data. This approach works fine for a small, savvy team that knows what it’s doing. But it falls apart instantly the moment you want to expand to multiple teams testing in different places. If you’re a developer, the challenge usually isn’t building your own experiments, but building a repeatable process for to run experiments. That means not just easy code interfaces for building tests, but helpful user interfaces for of building a good test. And as a product manager, I’m consistently humbled by users who tell me, “My favorite thing about your product is the .” everyone else guiding experiments through each step documentation Conclusion As I read through these stories like Airbnb’s and Booking’s, I can’t help asking myself — “is it really worth all the trouble?” It feels like reading the story of early explorers or astronauts, going out into the unknown and hitting one challenge after another. At each point, they must have considered just pulling the plug on experimentation and going back to guesswork. It’s certainly easier. What’s remarkable, though, is that none of them gave up. Just the opposite: at each stage, they discovered a way to make experimentation radically easier, and in doing so, they unlocked the scientific method for a whole new branch of the business. Experimentation pitfalls became opportunities to innovate. And eventually, those innovations piled up to form a sustainable competitive advantage from experimentation. Not so long ago, this daunting path was only open to the most successful technology companies. At Optimizely, we’ve made it our mission to change that — eliminating one pitfall at a time. If you’re not already experimenting at scale, we’re here to help. This wraps it up for the first Optimizely blog series. Looking for more on Product Experimentation? Check out: Optimizely Full Stack product page Change the Game webinar series Getting Started with Product Experimentation whitepaper Product Experimentation Pitfalls live webinar, presented by Jon Noronha This post originally appeared on the . , Hacker Noon’s weekly sponsor, is the world’s leader in digital experience optimization, allowing businesses to dramatically drive up the value of their digital products, commerce, and campaigns through its best in class experimentation software platform. Optimizely enables product development teams to accelerate innovation, lower the risk of new features, and drive up the return on investment from digital by up to 10X. Optimizely Blog Optimizely

When you fail to scale across teams…

is a tool that lets you perform AB testing. Like all or most other client-side AB testing , it uses the following mechanism: Optimizely tools A script tag is included on your page which alters it on load for a variation group of your users The variation is compared to the unchanged control group to get insights on how well it performed The main sell of this model for our team is simple: no code changes are needed to run the tests. This lets product and marketing teams launch tests in production faster and without involvement of engineers. There is one problem however: Optimizely usage is inherently problematic with React apps. React re-renders components every time state or store are changed. So, Optimizely changes don’t stick — they are removed by React diffing mechanism on re-render. What’s already out there? There has lately been a number of articles that introduced different solutions for React+Optimizely usage. engineering team designed using Store to hold experiment information. Looks like a well structured approach for client side rendering where big changes are necessary (like removing/adding components). [UPDATE: Tilt blog seems to have since been taken down] Tilt a pretty intricate system ’s idea is less complex, easier to implement, and is querying Optimizely’s globally available Data Object directly to get active experiments. Read about it . Shesh here talks about a different angle to the problem — . Not just React, but any server side AB testing will pretty much always require a deploy. To me though, this approach seems to under-utilize Optimizely — I would rather use its GUI for client-side AB testing or not use it at all. Milan server-side rendered apps and using Optimizely REST API to fetch experiment information on the server A lot of good ideas here. However, it bothers me quite a lot that all of the above solutions require a deploy for every new AB test. React/Redux + Optimizely without deploy or code changes Here is a lightweight approach. Things to note: This is for small to medium UX tweaks. If your AB test is massive, then a code change might be a better path. The success of this method in your codebase depends on your specific React implementation. This works well with our React/Redux app, because it re-renders the top level component on every change to props and actions are used predominantly (rather than local states). Step 1 React re-renders components when state or store are changed. If you use React, you are definitely familiar with the . You can use several of these methods to call a global function which acts like a hook to trigger the for desired DOM alteration. lifecycle methods JavaScript What this looks like: would be a global function that gets called immediately after the component mounts on the page. Utilize your top level component’s and lifecycle methods, like so: window.optimizelyHook componentDidMount componentDidUpdate Step 2 In Optimizely’s variation JavaScript, define the function. Then, perform all DOM alteration inside of this function. optimizelyHook() is called once on the initial mount is called on every consecutive update of props componentDidMount componentDidUpdate The result will run on every props update, resulting in experiment that persists through re-renders triggered by Redux actions. optimizelyHook() Deploy once, and use many times across multiple AB tests. 🎉 Resources Understanding server-side vs client-side AB testing React lifecycle methods

Using Optimizely with React/Redux

The chemistry of converting raw data into golden opportunities for demand generation is a mastery sought by many but perfected by few. The intersection of data and analytics with demand generation represents not merely a trend but a fundamental shift in how businesses approach market engagement and customer acquisition. With precision analytics, companies are not just passively observing market behaviors; they are actively predicting and shaping future demands. Recently, I interviewed , a Demand Gen Lead at . Steve Armenti Google Can you share a bit about yourself? I’m from New Hampshire and lived most of my life in New England. After college, I landed a sales job at an accounting software company outside of Boston. I was an SDR making 100 phone calls a day trying to book meetings with CFOs. This was 10 years ago before marketing and sales automation was as prevalent as it is today. However, I taught myself how to manually segment my territory using spreadsheets and designed my own A/B email experiments to get meetings. Eventually, I stopped making phone calls altogether and was producing the highest numbers on the team purely from emails. I didn’t know it but I was using ABM + personalization. Being in sales taught me about the human element of marketing and sales. At the end of every marketing campaign is an actual person who is trying to solve problems. In marketing, we often forget that as we sit in our office buildings (or home offices) designing campaigns. I try to remember that there are real people on the other end, just like us, and they can smell your bullsh*t from a mile away. How does data-driven decision-making impact B2B demand generation? In my opinion, it’s everything. I’ve built most of my career on using data to inform a variety of decisions: small and large about B2B demand generation and growth. Data can remove human error and opinion from the situation. For example, let’s say marketing and sales are feuding about the quality of MQLs. MQL quality can be very subjective. What matters is alignment between marketing and sales on what accounts you’re targeting, who in those accounts you’re acquiring, and what are their pain points. Who is the ideal customer profile? All of those things can be measured with data. You can pull data to show that an MQL came from a target account and that the persona is precisely the proper seniority, title, and job function. The data is used to establish a baseline of what is true and what is opinion. If you don’t have the data to prove what is true, go get it before you get into a shouting match. What specific data points are crucial for understanding B2B buyer behavior? I like to first look at a combination of demographics, firmographics, and psychographics. You can get these data points from 1P and 3P research, persona development, your website and marketing data, your CRM, surveys, and focus groups. This will give you a basic understanding of who is in your total addressable market. Second, I want to look at my data. How do customers behave? This data requires analyzing the person-level data against all of your marketing activities. The question you want to answer is “How does [persona] engage with [marketing campaign] and what is the behavior they are taking as a result” my Doing this exercise at scale will unlock a trove of insights about your customer that you can then organize and segment to fit the needs of your campaigns. What role does predictive analytics play in B2B demand generation? Predictive analytics can play a big role in any B2B demand gen program. It requires a solid data analytics infrastructure though first. Assuming there is a data warehouse and process in place to clean and ingest data, predictive analytics can help marketers do many things: Identify the highest propensity accounts, personas, and segments. Predict the timing of purchase intent helping your sellers reach out at the most opportune time Personalize messages or notifications based on user behavior Predict the effectiveness of a campaign or activity And now, with AI, this entire process is speeding up. AI will become better at collecting and processing large amounts of data to identify patterns and trends that marketers can take action on in real-time. As the prediction engine runs, and AI is feeding results back even faster than ever, marketers shift their role to the entire operation versus managing the hands-on creation process of marketing campaigns. directing How can businesses effectively leverage CRM data in demand-generation efforts? CRM data is a goldmine of information. It’s the source code to what makes your customers tick and what incentivizes them to buy. CRM data can be used to identify which accounts and personas have the highest propensity to buy. CRM data can be used to segment your audience into different groups based on their needs, interests, and purchase behavior. CRM data can be used to optimize your campaigns and improve performance. At the end of the day, all marketing should be measured on its ability to drive revenue – and your CRM is one of the most critical data sources to be able to do that. What are the common challenges in collecting and utilizing data for demand generation? Data silos. When data is stored in different systems and applications, it becomes really difficult to access and analyze. The accuracy and completeness of your data is important once you break down the silos. As they say, garbage in garbage out. So if you organized the data but it’s still crap quality, you aren’t even close. Collecting and utilizing data for demand generation can be a resource-intensive process often requiring skilled data scientists. This is a challenge in itself. Proper data infrastructure is essential to good B2B demand generation. That said, I think a big challenge the industry is facing is a lack of analytical skills for the average marketer. The marketer of the future needs to be as analytical as they are creative. How do you ensure data quality and accuracy in B2B demand generation campaigns? It starts with cleaning the data. Make sure you have a process to clean and join together the various data sources you want to use. You might find you are missing certain quality markers so you will need to look at enriching the data through other 1P sources or by licensing 3P data. Once you have a complete picture of your data (often called a data dictionary), you will want to validate that the data is accurate either through testing and QA or with an actual validation tool. Could you explain how data segmentation enhances B2B lead nurturing? Data segmentation is all about dividing your leads and contacts into groups based on certain characteristics, such as industry, company size, job title, location, or historical behavior. You want to use segmentation so you can tailor your marketing and sales messages to each group, increasing the chances they will take the action you want them to take. Using segmentation allows you to improve your targeting and make it more precise, increase the relevance and personalization of your email, and improve the efficiency of email as a channel overall. You send less messages that produce better results. What tools and technologies are indispensable for data-driven B2B demand generation? There are a million of them but you will want a demand platform, marketing automation platform, CRM, customer data platform, web/SEO/content analytics, social media software, and A/B testing software. I’m partial to , , , , , and . , , , , and are also great products. Demandbase Marketo Salesforce GrowthLoop Google Analytics SproutSocial HubSpot Kissmetrics Mailchimp Optimizely Buffer How does personalization based on data improve B2B lead conversion rates? At a high level, when you personalize your message, you show people that you understand their needs and interests. This can make them more likely to pay attention to your messages and grow an affinity toward your brand. This will likely increase the chances that people will engage with your marketing and do what you want them to do. Personalization is a game of compounding value. As your audience continues to feel like you understand them and can meet their needs, they go deeper into the funnel evaluating your product or service. Marketing continues to personalize until the person raises their hand and takes action or wants to talk to sales. The personalization doesn’t stop here. Onboarding, sales enablement, and product discovery all need to continue the experience the person felt during the evaluation process. How do you bridge the gap between data analytics and actionable demand-generation strategies? In my opinion, it’s a linear process that goes like this: Get your house (data warehouse) in order. Collect, clean, and ingest all the data you want into one source of truth Build the proper data pipelines to fuel your teams. Data needs to flow from the warehouse into dashboards, CDPs, reporting, etc. so teams can review that data in real-time Automate insights and reporting. Most of the regular, day-to-day reporting you need to do is simply looking at the same KPIs weekly and/or monthly. Standardize those KPIs and the reports. Set calendar invites for when you want your team to look at it. Pull your top insights and develop commentary about what changed from the last reporting period and why. Run optimization sprints. After you’ve collected insights, rank the top ones using some priority method [e.g. Reach, Interact, Convert, and Engage (RICE)] and go and A/B optimizations. Do this in a sprint fashion so you get quick feedback and quick data. That feedback and data flows back into your pipelines and the cycle becomes a perfect loop. What emerging trends in data and analytics are shaping the future of B2B demand generation? AI, of course. I’m stoked about what AI is going to do for B2B demand generation. On the data side, I’m personally exploring how AI can automate the collection and analysis of data, improve insights and decision-making, and identify patterns and trends in data that go unseen by humans to better understand customers and prospects. On the creative side, I’m excited about personalizing our marketing to each person. Right now I use a framework that identifies target accounts, ICP (personas), and then segments. The next level of this is down to the actual person. I’m also really interested in AI for the use of predictive performance and analysis in demand generation. Launching campaigns is a time-consuming process and when it fails, a lot of resources, budget, and time are wasted. With AI, predicting the success rate of campaigns is possible. Additionally, prediction around which accounts, personas, and people are most likely to buy is incredible. This plus the personalization is like Michael Jordan and Scottie Pippen in the 90s – unstoppable. Also published . here

What Google Can Teach Us About Effective B2B Demand Generation

With the growing demand for Next.js developers, job seekers need to prepare for Next.js interviews. In this article, I have arranged commonly asked into three sections based on difficulty level: beginner, intermediate, and expert. Next.js interview questions and answers Are you looking to ? ace your Next.js interview If so, this guide is for you. Difficulty Level: Beginner 1- What is Next.js, and how is it different from React? Next.js is a React-based open-source framework that helps developers build server-side rendered React applications. The key difference between and Next.js is the way they handle routing. React uses client-side routing, meaning the page transitions are handled entirely on the client-side using JavaScript. React In contrast, Next.js provides server-side routing, which means that the server handles the routing and sends the pre-rendered pages to the client, resulting in faster page loads and better SEO. Next.js also provides additional features like automatic code splitting, static site generation, and dynamic imports. 2- What are the advantages of using Next.js over React? Next.js offers several advantages over React, including server-side rendering, automatic code splitting, static site generation, dynamic imports, optimized performance, and easy deployment. Additionally, Next.js supports built-in SEO and analytics, making it easier to optimize your application for search engines and track user engagement. 3- How do you create a new Next.js application? To create a new Next.js application, you can use the command. For example, you can run the following command in your terminal: . This will create a new Next.js application with the name . create-next-app npx create-next-app my-app my-app 4- What is server-side rendering, and why is it important? Server-side rendering (SSR) is the process of rendering a web page on the server before sending it to the client's browser. SSR is important because it allows search engines to crawl and index your website's content, which can improve your website's SEO. Additionally, SSR can improve the initial page load time and improve the user experience for users with slow internet connections or devices. 5- What is client-side rendering, and how does it differ from server-side rendering? Client-side rendering (CSR) is the process of rendering a web page on the client's browser using after receiving the initial HTML, CSS, and JavaScript from the server. The key difference between SSR and CSR is that SSR sends a fully rendered HTML page to the client's browser, while CSR sends an empty HTML page that is populated by JavaScript. JavaScript 6- What is static site generation, and how does it differ from server-side rendering? Static site generation (SSG) is the process of generating a static HTML, CSS, and JavaScript file for each page on your website at build time. The key difference between SSG and SSR is that SSG generates a static file that can be served from a content delivery network (CDN), while SSR generates the HTML dynamically on the server and sends it to the client's browser. 7- How do you configure routing in a Next.js application? Next.js uses file-based routing, which means that you can create a page by creating a new file in the directory with the corresponding URL path. For example, to create a page with the URL path , you would create a file called in the directory. pages /about about.js pages 8- What is the purpose of the function in Next.js? getStaticProps The function is used to fetch data at build time for static site generation. This function is called during the build process and can be used to fetch data from an external API or database. The data returned by is then passed as props to the page component. getStaticProps getStaticProps 9- How do you pass data between pages in a Next.js application? Next.js provides several ways to pass data between pages in a Next.js application, including URL query parameters, the API, and state management libraries like Redux or React Context. You can also use the function to fetch data on the server and pass it as props to the page component. Router getServerSideProps 10- How do you deploy a Next.js application? Next.js applications can be deployed to a variety of platforms, including cloud services like AWS, , and Microsoft Azure, as well as platforms like Vercel and Netlify. To deploy a Next.js application, you can use the command to export the static files for SSG or use a platform-specific deployment tool like Vercel's CLI or AWS Elastic Beanstalk. Google Cloud Platform next export Difficulty Level: Intermediate 1- What is serverless architecture, and how does it relate to Next.js? Serverless architecture is a cloud computing model where the cloud provider manages the infrastructure and automatically scales the resources based on demand. Next.js can be used with serverless architecture by deploying the application to a serverless platform like AWS Lambda or Google Cloud Functions. 2- What is the difference between and functions in Next.js? getServerSideProps getStaticProps The function is used to fetch data on the server at runtime for server-side rendering, while the function is used to fetch data at build time for static site generation. getServerSideProps getStaticProps 3- What is the purpose of the function in Next.js? getStaticPaths The function is used to generate dynamic paths for pages with dynamic data. This function is called during the build process and can be used to generate a list of possible values for the dynamic data. The data returned by is then used to generate static files for each possible value. getStaticPaths getStaticPaths 4- How do you configure dynamic routes in a Next.js application? Next.js uses square brackets to denote dynamic segments in a URL path. For example, to create a dynamic route for blog posts with the URL path , you would create a file called in the directory. [] /blog/[slug] [slug].js pages/blog 5- How does Next.js handle code splitting, and why is it important? Next.js automatically splits your code into smaller chunks that can be loaded on demand when the user navigates to a new page. This helps to reduce the initial page load time and improve the performance of your application. 6- What is the purpose of the file in Next.js? _app.js The file is used to wrap the entire application and provides global styles, layout components, and context providers. This file is called on every page request and can be used to add common functionality to your application. _app.js 7- How do you implement authentication in a Next.js application? Next.js provides several options for implementing authentication, including JSON Web Tokens (JWT), OAuth, and third-party libraries like NextAuth.js. You can also use server-side rendering and session management to implement server-side authentication. 8- What is the difference between a container component and a presentational component? A container component is responsible for managing the state and logic of a component, while a presentational component is responsible for rendering the UI based on the props passed down from the container component. 9- What is the purpose of the hook in React, and how does it relate to Next.js? useEffect The hook is used to perform side effects in a functional component, such as fetching data from an API or updating the document title. In Next.js, the hook can be used to useEffect useEffect perform client-side data fetching using the API or third-party libraries like Axios or SWR. fetch 10- How do you handle errors in a Next.js application? Next.js provides several options for error handling, including custom error pages, server-side error handling with , and client-side error handling with React error boundaries. You can also use third-party libraries like Sentry or Rollbar for error monitoring and reporting. getInitialProps Difficulty Level: Expert 1- How do you implement internationalization (i18n) in a Next.js application? Next.js provides built-in support for i18n through the library. You can use this library to create translations for your application and switch between languages based on the user's preferences or browser settings. next-i18next 2- What is the purpose of the function in Next.js, and how does it relate to the function? getServerSideProps getInitialProps The function is used to fetch data on the server at runtime for server-side rendering, while the function is used to fetch data on the client or server at runtime. In Next.js 9.3 and later, the function is deprecated in favor of . getServerSideProps getInitialProps getInitialProps getServerSideProps 3- How do you implement server-side caching in a Next.js application? Next.js provides built-in support for server-side caching through the header. You can set the cache duration for each page using the function or by setting the property in the page component. Cache-Control getServerSideProps cacheControl We can also use caching libraries like Redis or Memcached to cache API responses or database queries. Options like CDN caching or edge caching can also be implemented to improve the performance of static assets and reduce the load on the server. 4- How do you optimize the performance of a Next.js application? There are several strategies for optimizing the performance of a Next.js application, including code splitting, lazy loading, image optimization, server-side caching, and CDN caching. You can also use performance monitoring tools like Lighthouse or WebPageTest to identify areas for improvement. 5- How do you implement serverless functions in a Next.js application? Next.js provides built-in support for serverless functions through the feature. You can create a serverless function by creating a file in the directory with the desired endpoint name and implementing the server-side logic. API Routes pages/api 6- How do you implement a headless CMS with Next.js? You can implement a headless CMS with Next.js by using a third-party CMS like Contentful, Strapi, or Sanity. These CMS platforms provide APIs for fetching and updating content, which can be integrated with Next.js using the or functions. getStaticProps getServerSideProps 7- How do you handle SSR for complex data models or nested data structures? We can use GraphQL or REST APIs to fetch data from the server and pass it to the component as props. Libraries like or can also be used to handle data fetching and caching. swr react-query 8- How do you implement A/B testing in a Next.js application? We can use third-party tools like Google Optimize or Optimizely to create experiments and track user behavior. We can also use custom solutions like feature flags or conditional rendering to test different variations of the application. 9- How do you handle real-time updates in a Next.js application? To handle real-time updates in a Next.js application, you can use server-sent events, web sockets, or third-party libraries like Socket.io to establish real-time connections between the client and the server. You can also use libraries like or to handle real-time data updates in the application. react-use redux 10- How do you implement testing and continuous integration in a Next.js application? To implement testing and continuous integration in a Next.js application, you can use testing frameworks like Jest or Cypress to write and run tests. You can also use continuous integration services like CircleCI or Travis CI to automate the testing and deployment process. Additionally, you can use code quality tools like ESLint or Prettier to ensure code consistency and maintainability. Conclusion I made these Next.js interview questions and answers to be concise and to the point. This article will act like a quick cheat sheet for anyone who is preparing for a Next.js interview. But, remember, you must already have good experience working with Next.js to be the right fit for the job. Good luck!

30 Next.js Interview Questions: Get Ready for Your Dream Job

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Customer education is a win-win for customers and businesses. The customer gets more pleasure from the product (because he or she understands it better), and the business gets more profit due to better feedback, outreach, and purchases. Many companies use customer education as a tool. growth marketing A prime example of how customer education helps businesses and customers is Slack. Workplace, launched in 2013, has shown tremendous growth over the years. At the , Kristen Swanson (VP of Customer Insights at Slack) discussed how customer education at Slack works. Here are some interesting quotes from her story: Bay Area Customer Success meetup Education was the core of Slack as a product. If you're just getting started with customer education, find one ticklish problem of your customers and try to remove it. Feel the rush of energy, create the solution and see how it takes root. I see customer education as the jet fuel for CSM. We do what we can to help build better and faster customer relationships. What about sales: as customers move from a free product to a paid product, the more mature they become in their use, the more likely they are to say, "Yeah, let's invest [in the paid version]. That's why it's natural to support educational programs for sales and marketing departments. Of course, customer education isn't just crucial for big SaaS companies like Slack, Checkr, Box, and Optimizely. Here we can even discuss whether they would be big customer education... WITHOUT But that's a topic for another discussion. The fact is that customer education is important and necessary for companies of any profile and size. What companies need customer education? Customer education is content designed to attract, engage and retain new and existing customers. It is delivered programmatically through in-person and on-demand channels. : Businesses should invest in customer education if they have Complex product that users need to learn to understand the value Product that requires users to change existing behaviors, processes, or workflows Diverse user base in terms of needs and roles Products that are regularly updated with new features and functionality Assortment of pre-sales and cross-sales products Products that require extensive support For all these cases, customer education is crucial. How to create a customer education strategy for your company? Working on customer education involves several steps: Auditing the current state of customer education Searching for problems Selection of tools Allocation of resources Developing and testing solutions Let's discuss each in more detail. An audit of the current state of customer education One way or another, you're already talking about your product and how to use it somehow, even if that process isn't formally called customer education. Before you create something new, it's important to delve into what's already happening. Focus on these questions: So, how do you audit the current state of customer education in your company? What educational resources do users have right now? Where are users finding answers to their questions right now? What tools do you use for customer education? Do you provide on-demand guides? How is the learning process structured at each stage of the customer journey? Regarding the last question, write everything, step by step, from the first email you send after signing up (step 1) and beyond. Then look even further back to step 0: how is the training built for users who haven't yet become customers? How do you inform them about the product? How do you explain that this is the right product for them? At this point, it's clear that customer education involves a lot of company resources. Which teams are involved in customer education? These are tech support, the sales department, and the marketing department. Talk to these teams and find out what they do for customer education: What tools are used to educate customers? Is there a training management system and/or customer support center? What information do users come in for, and how does it happen? The most important thing at this stage is to put all the information on two shelves: What we already have and how it works What we don't have and whether we need it Calculate each tool you have and determine its impact on customer success. At this stage, you'll probably have a pile of information: records of support calls, lists of questions from online chats, statistics on the most viewed articles and guides in the blog and knowledge base, and personal opinions after interviews with marketers and sales managers. Identification of problems in customer education As you gather information, you will notice some patterns, repetitive phrases from different specialists, and typical questions in documentation and conversation records. Usually, at this stage, it becomes apparent that there are problems in the customer journey that need to be solved. For this, it's important to capture the results of your research. First, simply collect in one file all the problems you can identify. Next, you will need to analyze, prioritize, perform risk management, and plan the implementation. But for now, we're at the identification stage, so... Examples of typical problems in customer education: : high churn rate 3-7 days after signup (it can mean users are not adapting due to onboarding issues) For SaaS : low percentage of cross-sales and pre-sales (it can mean users are not aware of other products offered (for smartphone - adapter, protective case, speaker) or services (reflashing, software installation)). For eCommerce : a high level of support requests after using a product or individual feature. For any product The goal at this point is to find problems in your specific product (or part of it). When you have a list of problems, it's time to develop an action plan to solve them. To do this, you can use a special matrix: it divides users into segments based on the audience volume (for how many users it is relevant) and risks (how critical the problem is). The result is 4 segments: Large audience and high risk Large audience and low risk Small audience and high risk Small audience and low risk Based on this segmentation, you can understand what type of customer education is needed and prioritize the work on it. . High volume, high risk - it is when you have a lot of end users in a regulated environment. Options for customer onboarding with customer education: Case 1 Training with a certified program, for example, as in Google products - Google Ads, Google Analytics, etc Constantly available knowledge base for refresher training as people use your product Training led by a virtual instructor to explain more complex topics in a more interactive format Customer onboarding is one of the most challenging aspects of the path to purchase. . High volume, low risk - it is when you have a high volume of end users, and the end goal is broad product adoption. Options for customer onboarding with customer education: Case 2 Series of training led by a virtual instructor, which is especially useful when the geography is wide Step-by-step guide inside the product (with pop-ups or other types of prompts) without separate customer onboarding content (as a knowledge base) Short videos with micro-tips and tips for individual tasks and situations, easily consumable and digestible You can develop effective training by carefully analyzing your user base and determining their position in the matrix. . Low volume, high risk - it is when you have professional users whose mistakes are very risky for the brand. Options for customer onboarding with customer education: Case 3 Online calls or mini-conferences to model situations and solve specific problems Webinars with experts with narrow custom training (for example, how to use solution X specifically for online stores with an assortment of 500-1000 items or for of car dealers) corporate websites Certification programs with specifications (Google Advertising training is a great example). Effective training will work for your business and solve current problems. . Low volume, low risk - it is when you have a narrow segment of users with custom queries but not critical to the brand (for example, users who use SEO tools for PPC). Options for customer onboarding with customer education: Case 4 A knowledge base with a section for custom tool use Segmentation and subsequent training through an email course specialized exclusively on the demands of the audience Documentation for technical support to quickly resolve current issues Practical training can not only solve current problems but prevent new ones. Choosing tools for customer education When you know your customers' pain points, it's time to develop a plan to help them overcome them. To do this, you'll need the right tools and resources. Customer education tools you may need: Learning Management System (LMS) Blog In-product guides In-product notifications Email newsletters Knowledge base outside the LMS Customer success team Metaverse learning The format of content for customer education will depend on the goals, audience, complexity of the product, and level of user knowledge: - great for getting familiar with the product interface or when it would be helpful to ask a person to explain a complex topic Webinars - ideal for sharing step-by-step instructions for complex customizations or interfaces Recorded screen demonstrations - an opportunity to rework existing marketing content for high-level overviews or quick facts Infographics (PDF) - an opportunity to upload previously used content during in-person training Presentations (slides) - they help measure comprehension and help engage and retain users in the training content. Tests and surveys For example, SaaS customer education might include: - initial navigation about what the service can do, how it can help, and where to find more information Post-registration email - step-by-step prompts in the interface when the user first opens it In-product tooltips with the most common questions and articles on effective use In-product knowledge base via email newsletter a few days after signing up Regular live webinars or sending webinar recordings with more in-depth customer onboarding and reminders of where to go for support Follow-up email Visible and fast in-product support Chatbot Now let’s discuss the resources you need to implement your customer education strategy. Resource allocation for customer education You can start designing future customer training when you've figured out the possible tools and formats. And that's where the problem begins... Where to get the resources for implementing a customer education strategy? The most common (and understandable and natural) problem in any company is limited human, time, and financial resources. Where to get the money and time to design and develop your own learning management system (LMS) from scratch? You need a project manager, a business analyst, a learning specialist, a designer, a content creator, a frontend developer, a backend developer, a QA expert... The answer, as usual, is simple. Find the point where you can get the most out of your efforts. This is one case where you don't have to hit hard but know where to hit. For example, if you're losing a significant number of clients in the first few weeks, develop a set of adaptation courses that address those problems in the first place. Or, if you notice the same recurring support request, develop documents for agents to respond to it directly, and put it in a prominent place on your FAQ or blog. That is, you should develop and that first solves customer education's biggest problem. That problem will be different for each company. Still, an expert with business analysis skills will help you find the best point to effectively use company resources while getting the best return on customer education. promote content Create, test, and repeat! Customer education is not a program you create, run and never touch again. It's an iterative process that repeats in cycles, capturing and optimizing new processes each time. It should be seen as a living, dynamic part of the company that needs to be constantly improved, updated, and reevaluated. Just like marketing, sales, and other processes. To work on customer education, you can use any convenient framework already implemented in your project management and involves iterative processes. For example, the is the simplest example of cyclical work on improvement implementation. PDCA model Like all agile methodologies, the PDCA model is based on lean philosophy. So in terms of resource-intensive for the company, it's a fairly advantageous model: Step 1: Analyze and draw up requirements. Step 2. Make a plan for the first iteration. Step 3. Launch and test, detect errors and weak points. Step 4: Correct errors and adjust requirements and workflow if necessary. And repeat! Project management for customer education follows the same principles as project management in any other area and uses the appropriate approaches. I also recommend reading: Why Network Security is an Integral Part of Any Internet-Connected Business How Russian Games Propagated the War The Life and Times of Adobe Flash Player Gaming The 7 Craziest Financial Stories of 2021: Investing in Nothing How Russia is Creating a New "Great Patriotic War (WWII)" With Ukraine

Customer Education: How to Educate Customers Profitable for Business

The term growth hacking reflects the processes and experiments carried out in a company to boost its customer base and revenue. These changes and effective strategies aim to increase the conversion rate. If you’re amending, testing, and updating your content, you are already growth hacking! If you wish to learn growth hacking in-depth, this guide will help you understand the concept well. Let’s begin! What is Growth Hacking? Growth hacking is highly suitable for start-ups desiring escalated growth but with a limited budget. They follow a sequence of steps to discover some bottlenecks on the website and aim to improve it. However, beginners in this field may be ambiguous about its implementation. Luckily, we have ‘Pirate Funnel’ or the used by most growth hackers to conduct a deep analysis of a website. Here is what AARRR means: AARRR framework What’s your reach? Awareness: : What’s the traffic on your site? Acquisition Number of visitors who become users (either sign up, purchase your product, install an app, etc.) Activation: Number of people coming back to your site Retention: Number of users paying and the total amount they are paying Revenue: People feeling about your brands to others Referral: This framework is open to your own interpretations and usages. You can follow the best steps that suit your brand and leave others. Simply, the **Pirate Funnel **helps find the blockages in a site impeding its growth! Advantages of Growth Hacking Growth hacking comes with a plethora of advantages. It can help boost your leads and revenue. Cost-effective and improves the ROI of the business. Understand the bottlenecks of the growth of a company. Find out scalable marketing strategies. Build new business models to improvise current strategies. Generate brand awareness and build a loyal customer base. Add value to customers for increased overall conversion It helps to know your target audience and get leads. Growth Hacking Strategies Here are some steps to build effective growth hacking strategies: Conduct surveys to gain customer feedback It is important to conduct surveys and know the customers' reviews and feedback about your product. Many businesses use them to determine whether their product benefits the customers. After all, only customer satisfaction will result in more leads. So, you must assure that your brand aligns with the demands of your customers. In addition, you can formulate these surveys as per your company's needs. Either ask for the details of your target audience or ask them about your product. This strategy will give you insights into what you are lacking or what your customers like! Know your target audience A business must know its target audience. For that, you can consider demographics like location, age, gender, interests, and profession and create buyer personas. Moreover, it is always beneficial to interact with the target audience and understand their needs to best serve them. Choose correct metrics to evaluate performance There are plenty of metrics to analyze your brand's performance and make relevant improvisations. But it is crucial to choose the most relevant metrics that suit your strategies and direct you towards your objectives. For that, there are many data analytics software available, both free and paid. Take a look at the list of some key metrics to understand your performance: Conversion rate Cost per click Number of impressions on your site Number of engagements Cost per landing page view Choose relevant marketing channels There are many marketing channels and platforms recommended for successful growth hacking campaigns. Therefore, understand your target audience and choose the right marketing channel. If done right, this decision can generate more ROI and immense growth for your brand. Below are some marketing channels: SEO (Social Engine Optimization) Email marketing Search Engine Marketing Guest blogging Influencer marketing Offline advertisements Trade shows Networking events (B2B) Social Media Marketing Conduct testing There are two most used tests, A/B test and multivariate test. Growth hackers usually conduct these tests on different elements of a site, such as blog posts, content, videos, podcasts, emails, and more. These tests help to make changes to these elements and bring out the absolute best results in your favor. Analyze and optimize Analyzing the statistics and setting goals to improve further is crucial. In addition, jot down the strengths and weaknesses of the company and work on its improvement. This careful examination will lead you to prepare optimized marketing budgets and target users efficiently. 10 Best Growth Hacking Tools With the right growth hacking tools, you can soar high. Here is a list of some of the best growth hacking tools divided into 3 categories: For Email Marketing MailChimp SendGrid Vero For Customer Acquisition * CrazyEgg
* Optimizely 
* VWO
* Amplitude For Market Research Market research is an integral part of understanding customers and their needs. For that, here are some top tools available in the market: Olark Typeform Qualaroo For Email Marketing Email marketing is a powerful tool to reach more audiences and discuss your brand. actively check their emails in a day. Hence, emails are a good way to capture users and spread awareness of your business. Check out these growth hacking tools to incorporate into your strategy. 49.1% of internet users MailChimp Mailchimp is one of the most widely used email marketing tools. It helps provide email marketing analytics and send timely automated emails. The top benefits are mentioned below: Simple and customizable interface. Detailed analytics include insights into subscribers' activities, most-clicked links, the number of people who unsubscribed emails, subscribers who opened the mail, and so on. Free basic plan for less than 2000 subscribers. SendGrid It’s an excellent email service provider for business communications. The email marketing campaigns are automated in nature. Further, the reports and analytics help the team track the subscribers' activities. The top benefits are mentioned below: High-volume email marketing campaign access. Detailed reports and insights into the mail performance. Automated transactional and responsive promotional emails. Vero This platform helps send emails to people based on their behavior on your website. Moreover, it personalizes and tests your campaigns. If you are from a non-IT background, Vero is highly suited. The top benefits are mentioned below: A/B tests your emails. Audience segmentation Workflow automation For Customer Acquisition It’s crucial to bring new clients for your company to increase revenue. This is what customer acquisition is! Some of the best tools in the market can help you ace this. Let’s see. CrazyEgg CrazyEgg is widely used by people worldwide. It’s an excellent analytics platform that tracks and optimizes your website’s visitor behavior. The top benefits are mentioned below: Heat map reports A/B tests Understand the customer’s behavior while navigating your website. Optimizely This platform provides ways to escalate engagement and conversion rates on your website. The top benefits are mentioned below: Perform multivariate and A/B tests easily. Track key metrics to understand the user’s behavior and make appropriate changes. Offers a 30-day trial plan. VWO VWO is a great tool for customer acquisition. It helps conduct A/B and multivariate tests on your site in a simplified manner. The top benefits are mentioned below: Tests multiple versions of your site using split URL testing. Conducts multivariate testing and A/B testing. Free 30-day trial. Amplitude Amplitude is a self-service digital analytics platform. This tool helps businesses understand users’ behavior diligently to boost growth and, ultimately, revenue. The top benefits are mentioned below: Real-time analytics. 15 metrics to track customer acquisition, such as engagement, retention, activation, and monetization. Easy process to reach out to the customer based on gathering insights from each step of the funnel. Olark Customers using Olark have seen more conversions, accurate user insights, and lower call volumes. The top benefits are mentioned below: Chat in real-time with customers, answer their questions, and gather feedback. Use live chat insights to optimize your business’s services and products. Custom pre-chat forms for quick conversations and getting details to preview the customer’s needs. Typeform Typeform is a platform that allows you to create interesting forms and surveys for customers. These questions are engaging and prompt the users to take part in filling out the forms. The top benefits are mentioned below: Integrated analysis tools to gain insights and understand customers’ demands. Excellent user experience. Free plan at first. Users can avail Pro by subscribing to it at $25 per month. Qualaroo It’s a credible platform to conduct surveys and analyze visitors to your website. Here are the top benefits mentioned below: AI-powered analytics and reports. Get website and app feedback from people. Insights into the product's functions and understand your customer’s demands and requirements. Your Ultimate List of Growth Hack Resources It is crucial to analyze the current strategies of your business and formulate a plan that directs you toward growth. For that, you can implement various changes to your site and analyze the results. Here we are mentioning the top growth hack resources to help you achieve your goal. Upgrade the blog content The blog content of a website holds a lot of potential to increase conversion rates. As it is said, content is the king, it becomes essential to present polished and informational data in front of the users. According to , approximately 77% of people read blogs online regularly. Therefore, it’s a great way to capture users. Here are some tips: statistics Keep the data crisp and easy to understand. Do not use complicated words, it can increase the bounce rate. Add multimedia content to attract more visitors. Moreover, another growth hack is to add videos to your blog posts. It can be a live stream, tutorial, or any such option that may interest users. As a matter of fact, it is easier for users or readers to get quick information and save time. Therefore, such clips can attract them by providing beneficial information quickly. Do you know than any other type of content? Moreover, the viewers grasp 95% of the content while watching the video. video content is 1200% more successful Establish connections with industry experts Users always look for the credibility and authenticity of a brand. For that, connect with industry experts. In addition, you can take their interviews, hold a live session, or talk about any topic that may be of user interest. These experts have spent more time in the respective industry, and their experience can benefit your audience immensely. This exercise can attract many users and increase your brand's value. Update old blog posts/ articles Make sure to update your old articles regularly. You can add new information or statistics related to the content. This growth hack increases the chances of improvement in your ranking on different SERPs. According to a , updating old posts get 270% more results than the posts that stay dormant. survey Include social media posts on your website Add social media posts to your website. For instance, add reviews of customers, testimonials, or tweets to your site to reflect the credibility of your business. This practice helps to earn the trust of users. Moreover, it can also bring more followers on social media channels. Create multiple versions of an ad As you know, ads are an excellent way to reach your target audiences and increase your conversion rates. There are different sections of audiences based on age, gender, preferences, etc. So, to fulfill users' needs and target multiple sets of audiences, publish more than one version of an ad. Some ad versions can result in better performance, and some won’t do well. Hence, you can discard the underperforming ads and invest wisely! Use keywords in YouTube videos Keywords are very effective for a business’ growth. If you are publishing a YouTube video, it is obvious that you would wish to see it in the search result! If that’s the case, you must use the correct keywords. This growth hack can help your YouTube videos show in the ‘suggested content’. Further, it will increase your brand's reach and attract more audiences, as YouTube accounts for a staggering worldwide! 2.1 billion monthly users Work with Email Experimentation Email marketing is one of the most popular and powerful ways of digital marketing. , here are some of the most effective strategies for email marketing campaigns: According to HubSpot Message personalization (used by 72% of marketers), Subscriber segmentation (used by 78% of marketers), Email automation campaigns (used by 71% of marketers). The growth hack here is to create different versions of an email and test them. For instance, try changing the tone of the mail in your emails and analyze which email gets more responses! Know about the here for better email marketing. best email testing tools Final Remarks Every business aims to grow in a significant period of time. That’s the ultimate aim, right? Well, growth hacking targets to fulfill this purpose. It is obvious that it can be intimidating initially, however, with time, experimentation, approach, and resources, you can excel in this field! The results require patience and effective strategies. There are multiple growth hacks, but it’s important to experiment and see which aligns with your brand the most. It’s not necessary for what works for one business, will work for yours too. Therefore, you must give it some time to see the best results. I hope this growth hacking playbook and the growth hack resources will help you bring the expected results for your business! Good luck!

The Ultimate Growth Hacking Playbook

As a product designer, A/B Testing is an important process you should know, because it replaces assumptions with quantifiable data, and helps you gain actionable insights into user behavior to improve usability and make user-centered design decisions. A/B Testing, also known as split testing, is a user experience research method, where you split your audience to compare two variations "A" and "B" of your product design to determine which is more effective. from images layouts, interfaces, buttons, and many more. It basically replaces guesswork with data-informed decisions for positive results. You can test varieties of elements with A/B testing, to But just how do you conduct effective A/B tests? And how do you analyze the data and convert it into actionable insights? Like any design endeavor, effective A/B testing should follow a clearly defined approach. This guide defines that approach, and how you can use A/B testing  for product design improvement. 1. Research and Identify a variable for testing The first step is to conduct research to understand the issues and problems users have with your product design, and what would appeal to them instead. A/B testing is informed by research, so you have to analyze how  users interact with your product to know the aspect that most needs optimization, and use the data to determine the direction of your A/B test. For example, if you found out only a few users are using the new feature you launched. The problem could be the , due to the fact that poor navigation affects . navigation system usability So let's assume the problem is the , and that's the variable we are testing for. navigation system 2. Define the objective of the test Before carrying out your A/B test, you need to have a goal for the test, i.e, you have to set key performance indicators that'll determine the success of the test. In our case, what are we looking to improve with the test? We are looking to increase the , by redesigning the navigation system. feature adoption rate So our objective here is to redesign the navigation system and check if the new variation increases the adoption rate. 3. Formulate your hypothesis Before we formulate a hypothesis for our A/B test, let's understand what it  means.  Put simply, a hypothesis is a testable statement that predicts a suggested outcome. It is basically an assumption that can be verified by testing. From the example above, our  hypothesis will be: "If  we redesign the navigation system, the feature adoption rate will increase" However, in hypothesis testing, we have to make two hypotheses: i. Null hypothesis This hypothesis states that there is no relationship between the two variables being tested, and the outcome of the test is due to pure chance, i.e there's no real effect behind the data our A/B test has produced, and our new variation doesn't make any difference. The null hypothesis in our A/B test example will be: " A new navigation system will not increase the new feature's adoption  rate" But if the results provide enough evidence against the claim that the A/B test didn't have any effect, then we can reject the null hypothesis. This hypothesis challenges the null hypothesis with the claim that our A/B test hypothesis will prove to be true. ii. Alternative hypothesis The alternative hypothesis in our A/B test example will be: "A new navigation system will increase the new feature's adoption rate" Now we'll have to collect enough evidence from our A/B test to reject the null hypothesis and prove that the alternative hypothesis is true. 4. Create your Control group and Test group The next step is to decide the group of users that will participate in the test. In A/B testing, we have two groups – The Control group, and the Test  group. The Control group is the group of users that'll be shown the existing version of the navigation system during the A/B test,  and the Test group will be shown the new variation throughout the A/B test. You have to create the two groups by randomly selecting the users using a method called random sampling to eliminate bias. You also have to choose a before conducting the test to eliminate undercoverage bias, i.e a bias that occurs due to sampling few users. To avoid this bias, use a sample size calculator from , , , other to calculate the sample size your A/B test will need. sample size Optimizely VWO Google Optimize or A/B testing software 5. Conduct the A/B test You can run the test by measuring the performance of the two  variations daily. In our case, we'll measure the daily "adoption rate" of each navigation system, or you can use any of the A/B testing software mentioned above to track the performance . This software ensures that each variation is automatically shown 50% of the time to eliminate bias. manually automatically The testing period might last several days or weeks, depending on how long it takes to ensure that each variation has been shown to the appropriate sample size. The  interaction of each user will  then be measured and analyzed to determine the performance of  each variation. If you run your test for a longer period of time, more users will be involved and this lowers the risk of results being down to chance. 6. Analyze result This is the most crucial stage of the A/B test because this is the point that determines which variation has the higher user engagement. Let's say we analyzed our A/B test example, and the result states that  the test group has a 15% adoption rate and our test group has 45%. Does this mean the new variation is doing better than the former? , we can't draw conclusions without testing the validity. Regardless of whether an A/B test has a positive or negative outcome, you have to calculate the statistical significance of the test to prove the reliability of your results. The answer is no Statistical Significance Of The Test Statistical significance measures the certainty of the result obtained from an A/B Test. The higher its value, the more you can trust that your result is not due to error or random chance. To fully understand  Statistical significance, you have to be familiar with these terms: (i)Significance Level (alpha): Significance level or α, is the probability of rejecting the null hypothesis when it is true, i.e the test results could have occurred by chance. You can choose a significance level of 5% or less. This means that there is a 5% probability that your results occurred by chance. We'll compare our significance level with the terms below to determine our result's statistical significance. (ii) Confidence Level: Confidence level measures the degree of certainty or uncertainty of an A/B test. The confidence level is equivalent to  (1 – α) significance level ). So, if your significance level is 5%, the corresponding confidence level is 95%. This means that there's only a 5% probability that your results occurred by chance. Then we can say that you're 95% sure that variation A is better than  Variation B" (iii) P-value: P-Value is the probability that your results occurred as a result of random chance. If your P-value is lower than your significance level (α) , then your results are  statistically significant. The lower your p-value, the higher the statistical significance of your A/B test. A P-value greater than 5% means your null hypothesis is true, and your results occurred by chance.  (P-value ≤ α = Statistical Significance) ✅ Calculate your P-value using this formula: Cuemath You can use either P-values or confidence levels  to determine the statistical significance of your results, but using a is much simpler and more efficient than manually doing the calculations. You can use a statistical significance calculator from Optimizely or Google Optimize to calculate your statistical significance. If your result is statistically significant, you can go ahead with  your new variation, and continue to improve your product design with A/B testing. statistical significance calculator Stop Guessing! Start Testing A/B testing provides clear data to support or debunk product design decisions, so you can ensure every implementation produces positive results. It also helps you understand how certain elements of your design impact user behavior so you can improve the areas that need optimization. The sooner you start testing, the sooner you can identify areas for improvement, and the better your product design. Illustration by Wingify Also published . here

How to Use A/B Testing for Product Design Improvement

A Human-centered Approach TL;DR 👑 Shipping is queen. 🔭 Visualize and define success early on. 🤹 Don’t forget to include performance monitoring and KPI analysis. 🎯 Principal engineer is a leadership role. Think about your involvement in the project. For me, operating as a principal software engineer is all about bringing engineering, business, and people close together. Usually, you’re one of the first technical representatives in the organization to hear about a project as a principal engineer, and you need to make an internal dialog to either pursue or discard it. There are many things to consider in terms of time budget, resources, stakeholder communication, and even your own involvement when it comes to planning. So I want to share with you how I think about planning as a principal engineer and what are the important questions to ask in the planning phase of the project. Is It The Right Project? You’ll hear about the project in different maturities depending on how your organization normally generates business ideas. It could range from an observation of a potential user need to “we need it in three months”. Regardless, a simplified train of thought looks like this: Do or don’t do? If “do”, is it the right timing? What’s the engineering complexity? How’s the effort? Do we have the resources? Do or don’t do? There are many more factors. What I’m suggesting is simply: Follow your gut😎 Usually, you’ll have an intuition, excitement, or an energetic feeling, to say yes to the project. To develop the intuition over time, there are a few exercises I find very useful to formulate my engineering opinion: Learn your industry and the competitive landscape. Learn your organization’s strategy. (If you’re like me not really knowing what “strategy” means in the business world, I highly recommend the book “ ” by A.G. Lafley and Roger L. Martin.) Playing to Win: How Strategy Really Works Align the project with the organization’s s. OKR Talk to the designers about the user needs and pain points in their research. Talk to the project managers(PM) or product owners(PO) about the market research. Visualize your organization’s project portfolio. Just by looking at it, you’ll get a better sense of the balance between short-term and long-term commitments, team/project allocation, and buffer for adaption. If you are not sure about how to prioritize it, you can work on a with your team to figure out where the project stands. A Prioritization Matrix looks like this: Prioritization Matrix Note that is critical in the process because it will reveal how people in different disciplines think about the importance and urgency of the projects. It’s a invisible third axis of the matrix. Voting based on expertise What Does Success Look Like? Visualizing what success looks like early on helps you in many ways: Solidify your gut feeling about the project. Communicate the goal and alignment with OKRs to others more clearly. Design KPIs to measure the progress and impact. I like to use to put my thoughts in a . It is a great way to put people in the same frame of mind just by “seeing” what happens after the finish line. Amazon’s “Working Backwards” approach press release I use the press release as the starting point to invite conversations and consolidate the references to the documentation. Usually includes: Project epics or overview. UI Design mockups. Engineering system designs. KPI analytics. How to Ship It ASAP? “Ship to learn.” It became my guiding principle ever since I read about this quote. shares the same idea in their leadership principles and I think the mindset is the foundation of lean and agile software development because it’s: GitHub human-centered and user-focused. powered by iterations of feedback and learning. measurable. I like to use as a framework to think about the problems and how to iterate throughout the project. Combining with the press release and KPIs, it’s a great way to validate your assumptions and stay in the course. Design Thinking is another great tool to crack a problem and kick start the exploration. Most importantly, it’s fun🤩 Design Sprint When you’re facing a complex project, make sure your system design proposals include alternatives that are simpler and quicker to build. It’ll allow the teams to iterate faster. The takeaway here is to really focus on , instead of implementing a complete solution. delivering the project to the users’ hands What about KPI Analysis? It’s very useful to look at business KPIs and UX KPIs side by side to get a better sense of the data. It’s important to dive into the analysis with PMs and designers to find out the direction of the next iteration. There are a few basic concepts in statistics I learned that are useful to collaborate with Business Intelligence and Design teams. Permutation Testing p-value A/B Testing A/B/n Testing Multivariate testing Multi-armed bandit When To Do Multivariate Tests Instead of A/B/n Tests The Multi-Armed Bandit Problem and Its Solutions Is Performance Monitoring in Place? Technical performance is another opportunity to inject engineering context into discussions for the next iterations. In general, there are : two types of monitoring Real User Monitoring (RUM) Synthetic Monitoring RUM is about live data. It tells a story of real user behavior and how your system performs over time. Synthetic monitoring reports data of your system in a controlled environment so it’s suitable for , , and . static analysis regressions testing performance testing Here are some useful tools for monitoring: Grafana Google Lighthouse autocannon Cypress K6 Performance API Clinic.js Should I code as A Principal Software Engineer? I believe you should, but the bottom line is to empower engineers and make sure your involvement doesn’t block or interfere with the iterations. Many principal engineers have but I truly believe that if and you love it, you should code. The question is more about you should be coding. different opinions and experiences coding is your craft when and where My introduced a model to me called . I use it to think about my involvement in projects. It looks like this: leadership coach Situational Leadership The model suggests that there are 4 types of leadership styles and you can fluidly choose the styles to lead based on the situations. For situations of supporting and coaching, I think the best place and time to code are: Prototyping. Hackathon. Tooling for developer experience. Data visualization. Your intention is to maintain a certain level of involvement and visibility to the projects to provide support when needed. The situation happens usually in important projects with a longer time frame. They are also the type of projects that you spend time on mentoring and sponsoring engineers. For situations of directing, I think it’s better not to code because you’re highly invested in the direction of the project. You’ll be doing a lot of analyses and communications. Hands-on work often distracts you from your focus. Final Thoughts We touched on the questions I find useful in planning: Is it the right project? What does success look like? How to ship it ASAP? What about KPI analysis? Is performance monitoring in place? Should I code as a principal software engineer? It’s important to know that there’s no specific order to these questions. You can think about them in different sequences depending on your situation. What’s important is that at the end of the planning, you have a clear idea of how to iterate through your assumptions and bring the project live to the users. References Articles Amazon’s Working Backwards approach — Quora Amazon’s Working Backwards Press Release Template and Example — Ian McAllister Design Thinking 101 — Nielsen Norman Group Why Design Thinking Works — HBR Design thinking, explained — MIT The Design Sprint — GV How we use “ship small” to rapidly build new features at GitHub — Dev Community The Permutation Test: A Visual Explanation of Statistical Testing — Jared Wilber Multi-armed bandit — Optimizely The Multi-Armed Bandit Problem and Its Solutions — Lilian Weng A/B Testing — Optimizely A/B/n Testing -Optimizely Multivariate testing — Optimizely When To Do Multivariate Tests Instead of A/B/n Tests — CXL p-value — Wikipedia Situational leadership: 4 styles and qualities Real-time monitoring of Formula 1 telemetry data on Kubernetes with Grafana, Apache Kafka, and Strimzi — Paolo Patierno Set Goals with OKR — re:Work Using Prioritization Matrices to Inform UX Decisions — Nielsen Norman Group What are objectives and key results (OKRs)? — asana Objectives and Key Results (OKRs) — GitLab What metrics and KPIs do the experts use to measure UX effectiveness? — UserZoom Key Performance Indicators (KPIs) for Optimization Engineering Function Performance Indicators — GitLab Being Glue — Tanya Reilly What do Staff engineers actually do? — StaffEng Performance Monitoring: RUM vs synthetic monitoring — MDN Software performance testing — Wikipedia Performance Testing vs. Load Testing vs. Stress Testing — Blazemeter Everything You Need To Know About Stress Testing Your Software — DZone Regressions Testing — Wikipedia Static Program Analysis — Wikipedia From Frontend Developer to Principal Software Engineer — Daw-Chih Liou Time management — Wikipedia Books Staff Engineer: Leadership beyond the management track — Will Larson Playing to Win: How Strategy Really Works — A.G. Lafley, Roger L. Martin Tools Clinic.js autocannon — GitHub Performance API — MDN Cypress.io Testing Library Grafana Google Lighthouse K6 — GitHub This article was originally posted on . Daw-Chih’s website

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