What are some tools or platforms that you use to conduct and analyze A/B tests?

- 📧 Mailchimp: Allows creation and sending of different email versions to a subset of the audience, automatically sending the winning version to the rest. Provides detailed reports on open rate, click rate, and conversions. - 📬 Campaign Monitor: Similar to Mailchimp but also supports testing more than two variants and uses various criteria like revenue or engagement to determine the winner. - 📈 Optimizely: A comprehensive A/B testing platform that supports email, web, and app testing with robust analytics and reporting features. - 🔍 Google Optimize: Integrates with Google Analytics to provide insights on A/B tests, supporting email and web testing with a user-friendly interface.

We mainly work with Kameleoon and for some clients with the newer software ABlyft (from Germany). KAMELEOON: Wide range of functions and integrations, easy to use, many analysis options in the tool itself (e.g. the results of tests carried out in parallel can be viewed separately from each other directly in Kameleoon) ABlyft: Easy to set up, easier to familiarize yourself with the tool, tends to be cheaper than Kameleoon - but also has fewer features.

- ✉️ Mailchimp: Allows creating and sending different email versions to subsets of your audience, then automatically sends the winning version to the rest. Provides insights on open rate, click rate, and conversions. - 📈 Campaign Monitor: Similar features to Mailchimp but allows testing more than two variants and using different criteria to determine the winner, such as revenue or engagement. - 🌐 Google Optimize: Conducts A/B tests on websites, offering detailed analytics and integration with Google Analytics for in-depth insights. - 💻 Optimizely: A comprehensive platform for A/B testing across websites, mobile apps, and other digital channels, providing robust data analysis tools.

One of the most popular tools for email A/B testing is Mailchimp, which allows you to create and send different versions of your email to a subset of your audience and then automatically send the winning version to the rest. Mailchimp provides reports and insights on metrics such as open rate, click rate, and conversions. Another tool for email A/B testing is Campaign Monitor, which offers similar features but also lets you test more than two variants and use different criteria to determine the winner, such as revenue or engagement. In my experience, using these tools has significantly improved our email campaign effectiveness, providing clear data-driven decisions.

As a QA Engineer, I recently worked on a project where we conducted email A/B testing using Mailchimp. We tested two email variants with different subject lines and call-to-action buttons to see which performed better in terms of open and click rates. Mailchimp made it easy to track these metrics, and we were able to quickly identify the winning version. The insights from Mailchimp’s reports helped us understand user behavior and optimize future campaigns. It was fascinating to see how small changes could significantly impact engagement. This experience emphasized the importance of thorough testing in marketing strategies.

Mailchimp: This popular email marketing platform has built-in A/B testing capabilities that allow you to test different subject lines, content, send times, and more. It provides detailed reports to help you understand which variations perform best. ActiveCampaign: This platform offers advanced A/B testing features, including multivariate testing and automation workflows. You can test different elements of your emails and landing pages to optimize your campaigns. HubSpot: HubSpot's email marketing tool includes A/B testing functionality that lets you test subject lines, content, calls-to-action, and more. It also provides insights into the performance of your tests.

I use various tools and platforms to conduct and analyze A/B tests. Some of the ones I rely on include Google Optimize, Optimizely, VWO (Visual Website Optimizer), Adobe Target, and Convert.com. These platforms offer a range of features for setting up experiments, creating test variations, and tracking key metrics. They provide valuable insights into user behavior and help optimize marketing strategies effectively. Additionally, I often complement these tools with web analytics platforms like Google Analytics and Mixpanel for deeper analysis and segmentation.

At Henkan & Partners, we've used email A/B testing to refine subject lines, send times, and content. For instance, we used Mailchimp for our newsletter, Briefings, to test different elements and increased our open rates by 25% through testing various subject lines and personalization techniques. Mailchimp allows us to design emails, segment our audience, and track campaign performance, providing direct feedback on what engages our audience and enabling us to make data-driven improvements.

Optimizely: Offers robust A/B testing capabilities with easy setup for experiments. It provides detailed analytics that help understand user behavior and preferences. Google Optimize: Integrates seamlessly with Google Analytics, making it straightforward to set up tests and analyze results directly within your analytics dashboard. VWO (Visual Website Optimizer): Features a user-friendly interface for creating and running A/B tests. It also includes heatmaps and visitor behavior analysis tools to enhance test interpretations. These platforms facilitate precise A/B testing and provide valuable insights into the impacts of different variables on user engagement.

At Henkan & Partners, we've used tools like Facebook Ads Manager to test different ad creatives, copy, and targeting options for promoting our own services. We ran A/B tests Facebook ads to promote our CRO services. We tested one ad featuring a case study of our work with Accor, highlighting our achievement of 200 A/B tests in three years, against another ad showcasing our awards and recognitions. The case study ad performed better, resulting in a 40% increase in click-through rates and generating more qualified leads. These tools enable us to segment our audience, analyze performance metrics, and continuously refine our approach based on real-time feedback, ensuring we deliver the most compelling content to attract new clients.

As a QA Engineer, I was involved in a project where we conducted social media A/B testing using Facebook Ads Manager. We tested two different ad creatives targeted at similar audiences to identify which visuals and messaging would generate higher engagement. Facebook's split testing feature helped us run the tests smoothly, providing clear insights on how each variant performed. We also leveraged Buffer to experiment with different post timings and formats across platforms like LinkedIn and Twitter. The analytics provided by both tools allowed us to refine our strategy and optimize content for better performance in future campaigns.

Facebook Experiments: Facebook's native tool allows you to test different ad creatives, audiences, and placements to determine what works best for your campaigns. It provides detailed insights into performance of each variation AdEspresso by Hootsuite: This platform offers a user-friendly interface for creating and managing A/B tests across multiple social media platforms, including Facebook, Instagram, and Google Ads. It provides comprehensive reporting and analytics to help you make data-driven decisions AB Tasty: This tool is not limited to social media but offers a wide range of A/B testing capabilities for websites, apps, and social media. It provides advanced features such as multivariate testing, personalization

For a more powerful approach, consider dedicated A/B testing platforms that: Unify data across platforms: They consolidate results from various platforms, allowing you to compare performance and identify trends across the entire social media landscape. Test beyond platform limitations: These tools offer more flexibility, enabling you to test variables outside a single platform's control, like scheduling times or content formats. By moving beyond platform-specific A/B testing, you gain a broader perspective and can optimize your social media presence for maximum impact.

For social media A/B testing, Facebook Ads Manager is a powerful tool that allows you to create and test various versions of your ads and posts across Facebook and Instagram. Utilizing Facebook's split testing feature within Ads Manager helps analyze different variables like audience targeting and ad placements to determine the most effective approach. Another valuable tool is Buffer, which facilitates scheduling and publishing multiple versions of posts across platforms like Twitter, LinkedIn, and Pinterest. Buffer's analytics offer insights into post performance, helping refine your social media strategies for better engagement and reach.

Platforms such as Facebook Ads Manager, Hootsuite, and Buffer enable you to experiment with different post formats, audiences, and ad creatives. They offer performance metrics like engagement, reach, and click-through rate for in-depth analysis. Social media tools let you target specific demographics. This helps tailor content for maximum impact on each platform.

For social media A/B testing I would recommend: 1. Sprout Social - fantastic A/B testing features and detailed analytics. Great guide to understand which content resonates best and boosts engagement. 2. Optimizely - would be my favorite to test and experiment with social media testing. It also uses AI to help identify variables for hypothesis you want to test.

Here are some social media A/B testing tools with their pros and cons: Hootsuite Pros: Easy to use, integrates multiple social platforms, scheduling, and analytics. Cons: Limited A/B testing features, expensive for larger teams. Sprout Social Pros: Strong reporting, AI-driven insights, and post-performance tracking. Cons: Pricey, limited testing automation. Buffer Pros: Simple interface, affordable, scheduling with basic A/B testing. Cons: Limited advanced testing options, no multivariate tests. Each tool offers varied levels of A/B testing capabilities depending on your needs.

From my experience, Facebook Ads Manager is a great tool to begin with. It allows you to create and test different versions of your ads and posts on Facebook and Instagram, helping you identify which variations perform better. Facebook's split testing feature is also valuable, randomly assigning your audience to different ad sets and comparing results based on a single variable, such as creative, audience, delivery, or placement. _Buffer for Social Media A/B Testing_ Buffer is another excellent tool, allowing you to schedule and publish different versions of your posts on multiple platforms, including Twitter, LinkedIn, and Pinterest. It also provides analytics and recommendations to optimize your posts based on their performance.

Facebook Ads Manager: Enables A/B testing of ad creatives, audience targeting, placements, and bidding strategies. Twitter Ads: Allows marketers to run A/B tests on tweets, audiences, and campaign objectives to optimize engagement and conversions. LinkedIn Campaign Manager: Offers A/B testing for ad variations, targeting options, and bidding strategies to improve ad performance.

Google Analytics: While not strictly an A/B testing tool, Google Analytics can be used to track and analyze the results of your tests. It allows you to create custom reports, segment your audience, and compare the performance of different variations. Optimizely Stats Engine: This powerful statistical engine is built into Optimizely's experimentation platform. It helps you determine the statistical significance of your results and make confident decisions based on data. VWO Insights: This tool provides a comprehensive view of your A/B test results, including statistical significance, confidence intervals, and revenue impact. It also allows you to segment your audience and analyze results by different demographics.

I am surprised nobody has mentioned analysing tests in tools like Hotjar or Contentsquare which make it much easier to visualise the on-page data and user journey. Personally, I have always found them much more insightful for finding the less obvous reasons of WHY a test was flat/losing and extract learnings for iterations.

As a QA Engineer, I once worked on optimizing a client’s website through A/B testing. We used Google Optimize to run different experiments on page layout and CTA buttons. The tool's integration with Google Analytics allowed us to closely monitor user behavior and conversion rates. By analyzing statistical significance, we discovered that a simpler design with a prominent CTA outperformed the original. Additionally, we experimented with audience segmentation in Optimizely, adjusting content for different demographics. These insights led to a 20% increase in user engagement, proving how valuable the right tools can be for data-driven decisions.

To analyze A/B test results effectively, Google Analytics is widely used for its robust reporting and integration capabilities with email and social media platforms. Google Optimize complements this by facilitating website A/B testing integrated with Google Analytics. Optimizely is also valuable, offering features for testing and optimizing web, mobile, and digital products with advanced segmentation and personalization options.

While all-in-one platforms are convenient, specialized tools might be more effective. Imagine A/B testing a specific social media ad element. Tools like Facebook Ads Manager's built-in split testing allow for granular control and optimization within the social media platform itself. Choosing the right tool depends on factors like budget, technical expertise, and the specific needs of your A/B test. Don't be limited by the "big names" - explore the diverse A/B testing ecosystem to find the perfect fit for your next marketing campaign.

The simplest way is obviously to go with your default A/B testing tool. I've always found Kameleoon to have the greatest test analysis features, whether it's the design with the radar diagram or the ease of breaking down your results with different dimensions or applying different calculations like CUPED. Nevertheless, if you want a strong stack to analyze your tests, you need: - Enterprise-grade A/B testing tool - DX tool like Contentsquare for behavior flows to understand new user journeys, session replays to see how they interact with your components, and heat/click maps, even though I always felt they were optional - Nice to have: GA4 or Amplitude, which I feel are much better for segmentation

For analyzing the results of your A/B tests, PickFu is your go-to tool for quick, actionable feedback on your experiments. But if you're diving deeper, Google Analytics is a must-have for its comprehensive integration with various platforms, providing detailed reports and dashboards. Pair it with Google Optimize for seamless A/B testing on your website, leveraging the power of Google’s ecosystem. Optimizely is another powerhouse, allowing you to test and optimize across web, mobile, and digital products with advanced features like audience segmentation and personalization. With these tools, you can design, run, and analyze A/B tests more efficiently and effectively, ensuring your campaigns are always optimized for success.

There are many vendors out there that provide testing capabilities on both app and web and both client side or server side. Some examples would be in no particular order: Kameleoon, LaunchDarkly, Growthbook, VWO, Optimizely, ABTasty, AB Smartly, Convert. For many years the market leader was Google Optimize, which sunset last year. That created a big shift in the experimentation market. Experimentation is a state of mind, so once you secure the most obvious platforms such as email and paid media (some, paid search, display), content, web, app. You should look at other opportunities for testing at the company. What about your PIM system, or CMS, or customer operations and chat? Opportunities for testing are endless.

In my experience going straight to a live test without doing some level of pre-discovery will result in re-testing and the need for countless iterations. Before going live with an AB test it is important to define if those options are in fact the right ones. For this reason I prefer to run smaller AB tests in platforms like User Zoom, User Testing, and Hot Jar before going into the wild.

When you're using A/B testing tools, is good to know that the native tools are usually the best for organic traffic and third party tools are more focused for paid UA

The tooling question is rarely the toughest part when trying to adopt A/B Testing or experimentation in general. It is way more interesting how to make transform and change your organisation to become more data or experiment-driven. Once you have some initial buy-in or momentum, I would recommend to assess the current sophistication-level of your organisation and to immediately assess the baseline metrics like amount of experiments run, etc. Once you know where you are it is also worth to get in touch with companies at a similar stage as yours and discuss how they approach their change process. And only then it makes sense to assess which tool is the one that can help the most.

When discussing a tech stack for A/B testing, it's essential to include a tool to centralize all your test plans. Options like Excel, Airtable, or Trello can be used, but I personally use Notion. Ultimately, you need to find what works with your current organization, so there isn't a one-size-fits-all solution.

When choosing a tool, consider compatibility with your tech stack, budget, and ease of use. Think about data privacy and integration with analytics tools like Google Analytics. Choosing the right tool ensures a smooth testing process and reliable insights. Aligning tools with your needs maximizes the effectiveness of your A/B testing efforts.

When conducting and analyzing A/B tests, it's important to consider the following aspects to ensure the accuracy and relevance of your experiments: 1. Statistical Significance and Sample Size Example: If you're testing two versions of an email, you need a sufficiently large sample size to ensure that your results are statistically significant. Insight: Underpowered tests can lead to misleading results, so it's crucial to ensure your sample size is adequate. 2. Testing Duration Example: Running a test for too short a period can result in seasonal or time-based biases. Conversely, running a test for too long can lead to "peeking" and incorrect conclusions.

Integration Capabilities: Look for tools that integrate seamlessly with your existing marketing stack (e.g., CRM, analytics platforms) to streamline data flow and analysis. Ease of Use: Choose tools that are user-friendly and provide intuitive interfaces for setting up, running, and analyzing A/B tests. Data Security: Ensure that the tools comply with data protection regulations (e.g., GDPR) and provide adequate security measures for handling sensitive customer data. Support and Documentation: Consider the availability of support resources (e.g., customer support, knowledge base, training materials) to assist with setup and troubleshooting.

Experimentation platform is not everything. It’s just one piece of the puzzle in company’s architecture. You first and foremost need to ensure that you have skilled talent that is capable of making the most out of them. When starting from scratch it’s important to approach AB testing holistically as a program, focusing on management buy-in and change management.

For us, the best tools to use are: - Dynamic Yield (personalization powered by AI) - for our company this is the best tool which can make our website, shop, app much more effective - it also help us to make more tests than even before ;) - CrazyEgg (A/B testing)

Simply A/B Testing is not enough. It should primarily be used to gain insights into who your users are and why they are using your app. We always start with Meta Assumptions about our users. For example Sustainability is important to our users. With that Assumption in mind we then create several Hypothesis like if we display a sustainability badge in the product page, users will buy more because they know it's sustainably produced. Those hypothesis turn into tests and once those tests are evaluated, we use that to challenge our assumption. Did several hypotheses related to our assumption turn into winning tests? If not, what other reasons could there be? That way, we learn more and more about the user! :D

To further enhance A/B testing analysis, consider using tools like: - VWO for end-to-end testing optimization - Crazy Egg for A/B testing and error tracking - Leanplum for mobile website and application A/B testing - Apptimize for feature release management and A/B testing By leveraging these tools and platforms, you can design, run, and analyze A/B tests more efficiently and effectively, ultimately improving your email and social media campaigns and achieving your business objectives.