Choosing the Right Mobile App A/B Testing Tool to Optimize Your In-App User Experiences
In today’s mobile-first world, it feels like every other blog post on the internet talks about how you can outdo your mobile app UI and UX. Continuously optimizing mobile app experiences for improved user engagement and retention is a no brainer. But, putting in optimization efforts for your mobile apps without having the right tool in your arsenal is like working on the presentation of a dish without understanding which equipment you need to cook it at the right temperature.
Choosing the right mobile app A/B testing tool can be overwhelming for product managers, app developers, and marketers alike. This is largely because zeroing in on the ideal tool or platform for your unique testing requirements depends on a plethora of parameters, some of which tend to get ignored if you take hasty or ill-informed decisions. Should you zero in on a tool that best addresses your use case or one that offers easy integrations with your other platforms? How can you ensure that the tool doesn’t impact your app performance? What about reporting – how can you be sure of the accuracy of the results your tool is producing?
In the following section, we discuss all factors you must consider before opting for a Mobile App A/B Testing tool.
If you’re looking for different tools available in the market, you can jump to this section containing the list of tools.
If you’d like to understand how to evaluate an experimentation tool, read on.
How to choose the right tool?
The ideal Mobile App A/B Testing platform is robust enough to offer extensive testing functionality that allows you to optimize your end-to-end in-app experiences as well as feature management capabilities so you can manage your entire feature lifecycle. Ultimately, the aim is to figure out the right variation of in-app experiences and features in order to optimize your app for improved engagement and conversions.
To select the tool best suited for your CRO roadmap, consider the following parameters.
Use case at hand
Mobile App A/B Testing has a myriad of use cases. For you to be able to select the right tool for your business, you need to first have a clear understanding of the use cases you want to tackle (at least the ones you wish to begin with). Once you are clear about that, you are automatically a step closer to narrowing down on the tool that offers maximum capabilities that cater to your requirements.
Some of the most common use cases of mobile app A/B testing include:
Eliminating friction in key user flows
For today’s on-the-go buyer who demands seamless shopping experiences, friction in user flows, especially one as critical as checkout, can lead to frustration and loss of interest, which ultimately increases your abandonment rate. In fact, did you know that mobile has the highest cart abandonment rate (beating tablets and desktop) of 85.65%? A/B testing your eCommerce app’s user flows can help you radically reduce drop-offs and abandonment rate, by paving the way for a delightful user experience.
Mobile app A/B testing tools allow you to create two (or more) variations of your user flows so you can pit them against each other and deploy the one that leads to the maximum improvement in your key app metrics. Furthermore, your tool must also enable you to segment your users based on their purchase and browsing behavior, and other demographic attributes so you can target them with the most relevant variation and figure out what works for which group.
Optimizing for the efficacy of search and product recommendation algorithms
Should your product recommendation algorithm be based on shoppers’ purchase history, trending items, or the most popular products from a particular category? How should your search algorithm categorize products, decide their relevance to a specific search query, and on what criteria should they be ranked on the search results page?
With mobile app A/B testing, you shouldn’t have to rely on guesswork or best practices to find the answers to the above questions. While testing UI-based changes is one use case that a robust tool caters to, it also allows you to experiment with your critical algorithms, including product recommendation and search, so you can strategically improve their efficacy. By testing multiple versions of your algorithms, you can figure out which one proves to be the most effective for your store, whether it is in driving upsell/cross-sell or fetching the most relevant search results.
Experimenting with in-app features before deploying universally
Universally deploying a new feature in your game can be quite tricky. You could either hit the jackpot and instantly watch your app usage and engagement levels jump up, or, on a more realistic note, it may or may not drive the results you thought it would. Therefore, mobile app A/B testing tools allow you to reduce the risk associated with launching in-game changes and updates by experimenting with them and rolling them out in stages to one or more of your user segments. If it performs well, you can go ahead and deploy it for all users; if not, you can always collect feedback, incorporate it, improve, and relaunch the enhanced version with confidence.
Mobile app A/B testing tools also offer extensive feature lifecycle management capabilities wherein you can roll out features in stages, test them out on a particular user segment, and even use feature flags to manage them at runtime and control and/or modify who gets access to it.
Streamlining in-app pricing strategy
To maximize engagement on your gaming app as well as revenue, you might have to experiment with multiple pricing strategies, for different user segments as the same model might not work for both disengaged and loyal gamers. Therefore, choose a mobile app A/B testing tool that allows you to test your dynamic pricing algorithm to figure out which one drives the best results for which segment.
Offer personalized gaming experiences
In today’s day and age, mobile app gaming experiences demand hyper-personalization, and rightfully so. To create an enticing gaming environment that keeps gamers hooked, you cannot possibly rely on a single strategy. Using a mobile app A/B testing tool, you can test all dynamic elements of your gaming app and deliver personalized experiences based on each gamers’ level in the game, engagement score, and other attributes. This way, you can constantly discover and deliver on what your users expect from you to keep them engaged.
The bottom line is that whichever use case you want to achieve with mobile app A/B testing, you want to be sure of it beforehand so you can make a strategic decision of choosing the right one based on your requirements.
Integrations and plugins offered by the tool
You want to make sure that whichever tool you opt for is the right addition to your tech stack, meaning that it integrates seamlessly with your other analytics, marketing, and sales platforms, so you don’t have a hard time accessing the required data and feeding it into your app optimization pipeline. For example, the most important one would be your analytics platform, so you can use it to generate insights around your website traffic and audience, which will then form the basis for crafting hypotheses.
For this, create a list of tools you currently use and look for the ones supported by the experimentation platform you are evaluating. If you own an eCommerce business, you might also want to ensure that whichever eCommerce platform your store is built on (such as Shopify or WooCommerce) is also supported.
Size, RAM usage, and performance of the SDK
The SDK supported by the platform deserves your attention as well as it can impact your app’s performance. Here are the parameters that you must evaluate it for:
- The SDK must be lightweight, so it does not have any major impact on the size of your app.
- Should not use a lot of RAM as mobile devices anyway have scarce RAM availability.
- Must perform well and be easily available at all times. VWO’s SDK for mobile app A/B testing is available even without an active internet connection and is tested extensively to get rid of all bugs that might negatively impact your app’s performance.
It’s important to pay heed to understanding the computation of A/B test results and generation of reports as it determines the impact of your experimentation. Statistics is the backbone of A/B testing, which is based upon calculation of probabilities. However, there are multiple approaches to interpreting probabilities in A/B testing – the most common ones being Frequentist and Bayesian models.
Make sure you find out whether the tool you have shortlisted uses the Frequentist or Bayesian statistical model. Traditionally, most tools used the Frequentist model wherein test results are based solely on the data from the current experiment, and does not take into account any previous data. The Frequentist model is based on running a particular test for a specific period of time and until statistical significance is reached so enough data can be collected to rightfully calculate the probability of one variation beating the other. However, it does not quantify the difference between the two variations keeping in account the uncertainty involved with the amount of data you obtained in a test.
The Bayesian statistical model, on the other hand, provides a natural way of learning by allowing you to feed in your beliefs from similar previous experiments into the model as prior, combines it with data from the current one, and then compute the test results. The probability of your hypothesis being correct is computed based on evolving data and informed by what’s happened up to that point.
VWO’s Bayesian powered statistics engine, SmartStats, helps you take smarter conversion optimization decisions by not only giving you the probability of one variation beating the other, but also the potential loss associated with its deployment. With SmartStats, you can move away from relying solely on reaching statistical significance or running tests for a set period of time, and can conclude tests faster and expect more accurate results. SmartStats helps you make intelligent business decisions, faster and gain a competitive edge over your competitors.
Imagine a scenario where you are not sure whether providing an add-on offer with your service can lead to more sales. You planned to do an A/B test to test this hypothesis by allocating one half of traffic to service with add-on (Variation A) and the other half without add-on (Variation B).
A traditional Frequentist test would only provide a yes/no answer if variation A is different from variation B. Also, the test results are valid only after you have obtained a sufficient number of visitors in your test.
However, VWO’s Bayesian powered statistics engine, SmartStats, provides you the odds of one variation beating the other and also the underlying potential loss in sales associated with each variation. Both metrics remain valid throughout the duration of the test.
With SmartStats, you can move away from binary outputs to more interpretable metrics.
Needless to say, your budget is a huge factor to consider in choosing a tool. Based on the specific use cases you want to tackle and the features you require, you will have to look for a tool that fits the bill as well as fits well into your budget so you can drive significant ROI from your experimentation program.
Especially if you are just starting out with mobile app optimization, opting for a comparatively expensive tool might not yield you a significant ROI. Instead, start with a tool that offers a free trial, so you can assess all its features comprehensively and decide whether it meets your requirements. VWO, for instance, offers a free trial that your team can utilize to run a few campaigns and figure out if your unique needs are met.
Support and assistance offered by the platform
When evaluating a tool, people often overlook the level and quality of support that the platform offers. However, it is a critical factor that plays a major role in determining the testing velocity and scale of your optimization program. If you receive dedicated, expert assistance throughout your journey, you will be able to achieve your goals more efficiently and grow your efforts with time.
Moreover, if you’re new to mobile app A/B testing, you might need some help in setting up the first few campaigns and getting your questions answered. So, make sure you opt for a tool that offers best in class support (quick response time, maximum availability, sufficient self-help resources, omni-channel support, CSAT, and so on) so you can not only get up to speed, but also drive the intended results effectively.
Even if you are somewhat experienced and well-versed with A/B testing your mobile app experiences, you might need extensive support immediately after signing up for a new tool. To that end, make sure you opt for a tool that offers dedicated support, quick TATs, and effective resolution to help you troubleshoot all your experimentation roadblocks.
Truth be told – you need a tool that’s all-encompassing. There isn’t one factor mentioned above that’s less important and you shouldn’t have to compromise on the quality of testing or your requirements.
VWO Mobile App Testing – A comprehensive platform for all your in-app optimization needs
VWO Mobile App Testing is a robust solution for mobile app optimization. From experimenting with multiple variations of in-app user experiences (both UI-based and server-side experimentation) to testing key features pre and post launch, you can do it all with ease. Whether you wish to test basic UI changes such as CTA or banner copy, color, and placement, or make drastic optimizations to your search engine algorithms, game experiences, and beyond, you’re well equipped to steadily grow your app’s engagement, usage, retention, and conversions.
You can also combine mobile app A//B testing with VWO Insights that offers heatmapping, session recording, and form analytics capabilities so you can gather actionable insights on your app’s user experience and convert them into optimization opportunities.
VWO offers advanced options of segmentation and targeting that allow you to segment your users based on their behavioral attributes and target them exclusively. VWO also integrates with all major analytics platforms so you can capture and analyze the relevant data required to make informed experimentation decisions.
VWO’s SDK for mobile app A/B testing is open-source and light-weight (approx 200KB for Android and 285KB for iOS) that only uses about 100KB or 300KB of RAM for Android and iOS.
Apart from this, VWO offers 24*7*365 support (& exceptional response time) with optimization experts assisting you throughout your journey to ensure you yield the desired results from your campaigns. With a CSAT of 98% (as compared to the industry average of 94%), VWO’s support team takes complete ownership of resolving all pitfalls you may come across, thus ensuring you make the most of your mobile app optimization program
If you have apprehensions about how VWO lives up to these promises, how about you try it out for free and assess it for yourself? If you have questions regarding the same, you can request a demo by one of VWO’s optimization experts and get them answered.
Other mobile app A/B testing tools
Here’s a list of a few other popularly known tools for mobile app experimentation along with their pricing.
Optimizely offers a cross-platform solution for feature flagging and experimentation that allows you to run UI-based as well as server-side experiments and also mitigate risk while launching features. You get access to full-stack and multi-channel experimentation capabilities, phased feature rollouts, the option to make instant app updates, and more with Optimizely’s mobile optimization offering. They offer a free rollouts plan valid for 7 days that allows you to evaluate their basic capabilities.
LaunchDarkly offers feature flag management and experimentation capabilities at scale along with granular control. You can manage your entire feature lifecycle – right from its production and testing to deployment and performance analysis. You get complete control over each feature so you can minimize risk and launch it confidently.
You can get started with a free trial or avail the starter plan at $75/month (limited to one member) to try out its basic functionalities. However, this pack does not include experimentation features, for which you will have to upgrade to a higher plan.
AB Tasty offers UX analytics, experimentation, personalization, and feature flag management capabilities that allow you to optimize end-to-end experiences on your mobile app. Using these, you can create user segments, offer unique experiences for various segments of your user base, and experiment with features before rolling them out. You can avail a custom quote from their website based on your unique users/month and other requirements.
Target is a testing and personalization platform from the house of Adobe. Primarily used for personalization of in-app experiences at scale, Target integrates seamlessly with Adobe Analytics and Adobe Audience Manager. It can be used for optimizing your app experiences based on your user behavior to improve engagement. However, Target does not offer feature management capabilities, so you might have to opt for a different tool for that.
Firebase A/B Testing
From the house of Google Optimize, Firebase A/B Testing provides both experimentation and feature management capabilities. Since it’s offered by Google, it integrates seamlessly with all other tools from Google, so sourcing data and drilling insights for your campaigns will not be an issue.
Choosing the right tool that best aligns with your experimentation goals is only the first (although extremely crucial) step towards improving your app’s key metrics. Leveraging the tool successfully means closing the optimization loop by investing time and effort in everything from benchmarking your KPIs to documenting your learnings and feeding them back into your testing roadmap. Sign up for a free trial with VWO to do this with ease.