Introduction
In the realm of digital marketing, A/B testing has emerged as a very popular technique for both product and marketing optimization. As marketers have more methodologies at their fingertips to analyse data, the main goal of this blog post is helping growth marketers answer the following question: When should we opt for A/B Testing, and when is Cohort Analysis the more suitable choice? The overlap between A/B Testing and Cohort Analysis can create ambiguity, leading professionals to search for clear guidance on the optimal approach.
What is A/B Testing?
A/B test, also known as split testing, is a method of comparing two versions of a webpage, app, product feature against each other to determine which one performs better in terms of a specific metric. The goal of A/B test is to identify changes that increase a particular outcome by running an experiment limited in time (ie. 7-14 days) where users are randomly divided into two groups.
After a sufficient amount of data has been collected, you analyse the results to determine which version (control or variant) performed better. The statistical significance remains the primary goal of conducting an A/B testing. Conducting an experiment we are obtaining results with 95% accuracy (with an accepted standard of 5% significance level).
A/B testing is about direct comparison and immediate impact, testing one change against another to see which is more effective. It does not take into account how a specific group of people behave over time.
What is Cohort Analysis?
A cohort is a method of grouping the users who performed a particular task and tracking their behavior over time. It is possible to use Cohort analysis in different business context like product management, marketing and people management.
In general it lets us compare customer behaviors between different groups of people and see how their metrics change over time. Diverse cohort types can be utilized to grasp your users’ actions:
- Acquisition Cohort
- Retention Cohort
- Revenue Cohort
- Behavior Cohort
How can cohort analysis be applied in product management? One can utilize Behavioral or Retention cohort analysis to assess product metrics following a product update. In such scenarios, teams will rely on data from new users introduced to each iteration of the enhanced product. Essentially, we’ll be contrasting metrics between the initial and subsequent cohorts, each having experienced distinct product features.
Cohort Analysis vs. A/B Testing. When to Opt for Cohort Analysis?
Cohort Analysis and A/B Testing are both valuable tools in understanding user behavior, product and marketing performance. However, they serve different purposes and are best used in different scenarios. See below some examples:
A/B Testing
P
- You want to test specific changes, like a new feature or a redesigned interface.
- You’re looking for immediate, measurable results on specific metrics (KPIs).
- You want to understand the direct impact of specific changes on user behavior.
Cohort Analysis
P
- You want to understand the long-term behavior or retention of users who started using your product at a specific time. For example to measure Product Market Fit.
- Measure the impact of broader changes, like a major product update or marketing campaign.
- You’re looking to identify patterns over time, such as drop-offs in user engagement after a few weeks or months.
To be able to use the Cohort analysis users in the cohorts should be similar (similar locations). Other factors, like seasonality or technical issues in one version, shouldn’t influence the metrics of users registering for different app versions.
Why it is important in Health Tech?
The Digital Health sector is witnessing a surge in innovation and user base, thanks to advances in technology and increased focus on personal health and wellness. However, this growth brings its own set of challenges, particularly in engaging users and ensuring their continued use of health apps and platforms. Unlike EdTech, where engagement might center around content relevance and learning outcomes, Digital Health faces unique challenges like ensuring user trust in handling sensitive health data, providing personalized health insights, and integrating with other health services and devices seamlessly.
Key Insights Cohort Analysis
For example, let’s take a Mobile App in Digital Tech to illustrate the application of a Cohort Analysis. The KPIs of interest are the number of monthly app downloads and their retention rates for assessing user engagement and app stickiness.
- In Q1, with a 40% increase in Google Ads spent (compared to the previous quarter), there was a notable increase in Monthly Active Users (MAU) by 35% and an improvement in retention by 28%.
- In Q2, despite a 70% increase in Google Ads spending, the MAU growth remained at 35%, but retention dropped to 15%.
In Q2, upon increasing the budget in Q2 due to positive MAU and retention trends, performance unexpectedly declined. The team had to analyse the behavior of users who downloaded the app following each campaign.
The investigation aimed to identify reasons behind the drop-off in user engagement and what segment of users did use the digital app less often. Also what impacted the reduced overall usage rate?
Cohort Analysis in the context of Digital Health helped the team in understanding how different groups of users engage with the app over time, influenced by specific features, updates, or marketing campaigns. This insight is invaluable for improving user interfaces, and tailoring marketing strategies to enhance user retention and engagement.
A/B Testing complements this by offering a direct way to test changes in the app on user behavior to test specific changes.
By combining Cohort and A/B tests can provide digital health startups with a robust strategy for user engagement and retention, addressing the sector’s unique challenges head-on.
Conclusion
In conclusion, cohort analysis offers invaluable insights into user behaviors and patterns, serving as a cornerstone for strategic decision-making. By segmenting users based on shared characteristics and tracking their interactions over time, businesses can gain a deeper understanding of customer lifecycles, refine marketing strategies, and enhance overall user experience. In an ever-competitive landscape, utilizing methods like cohort analysis and A/B Testing is essential for maintaining your startup’s relevance.
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Bibliography
Makeup & Breakup. (2020, August 7). A/B Testing & Cohort Analysis. DIGITAL MARKETING – Connecting the Dots
GoPractice. (2023, April 3). Retention: how to understand, calculate, and improve it.
Govindan, G., Baig, M. R., & Shrimali, V. R. (2021). Data Science for Marketing Analytics: A Practical Guide to Forming a Killer Marketing Strategy Through Data Analysis with Python. Packt
Gurram, S. (2022, June 21). How to improve user engagement and retention in EdTech? Upshot.ai
Shiu, A. (2016, August 11). 3 Ways To Measure User Retention. Amplitude.
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