7 Ways Cohort Analysis can Optimize Company Performance and Results

BY DAN SCHOENBAUM AND EVAN KAEDING ON NOVEMBER 10, 2017

Businesses are constantly in search of useful tactics that improve their brand’s performance and bottom line. Cohort analysis is often overlooked, but it can yield insightful information and actionable advice to improve acquisition, retention and monetization.

By definition, a cohort is a group of people who have a common characteristic during a period of time. For example, people born between 1972 and 1988 who have been struck by lightning are a cohort.

However, as we’re talking about cohort analysis, we’ll need to get a bit more specific about the type of cohort we’re interested in tracking. In the world of cohort analysis for digital products, a cohort is a group of users who have performed a common action or set of actions during a specific timeframe on your website or app.

For successful implementation of an analytics program, rather than looking at all of your users as a single unit, cohort analysis breaks them into groups for analysis. By analyzing the behavioral differences between these cohorts, product managers can spot patterns at multiple points in the customer lifecycle. These patterns help guide product decisions to suit user needs more effectively and improve the user experience.

Your cohorts may look something like this:

  • Trial sign-ups in the past 30 days
  • Paying customers in May
  • Players acquired via social media ads

The important takeaway is that cohort analysis allows product managers to ask a very specific question, analyze only the relevant data, and take action on it.

Here are seven ways cohort analysis can optimize company performance and results:

1. Effects of Unique Behaviors

Identifying users by the individual actions they take is vital to retention. Using this information paints an invaluable picture of users and their journey with your product. For example, online gaming is one industry that is constantly analyzing user behavior to improve UX.

Gaming is a great use case because gaming companies are trying to understand UX on one mobile platform versus another. By measuring retention of cohorts on Android versus iOS, for example, they can identify slight differences and fine-tune experiences based on the operating system being used to prevent gamers from churning prematurely.

Another example in the digital space is an online eLearning company, ABA English, with both desktop and mobile versions of its application. Through cohort research of user behavior, its analysts noticed that students would get to a certain point within their lesson and then drop off, never to return. To understand why, it built two cohorts: primary desktop users and primary mobile users.

ABA English learned it wasn’t a difficult question or challenge with the lesson, but rather a UI bug that prevented the “skip” button from showing up on the mobile app. It was able to pinpoint the exact problem, fix it, and drastically improve its UX by analyzing unique user behavior.

https://lh4.googleusercontent.com/W4M2ohkANooBDN1j8rfhdcK4d33q4p03OvIdqOvTIYG4y7G5tWkQ95kWubKlJxYDzTK03DDhihwBN8S-OlnKbY2kNJvSdEPadYJSMvrlMYMrBVJltSzJ4TJbxZ6_oYvr7W_38bM5

(After rolling out the fix, ABA English was able to see retention stabilize across operating systems.)

2. Test Your Hypothesis

Let’s say you believe a user performs a particular behavior for a specific reason that results in increased trial sign-ups. As a much more sophisticated version of A/B testing, cohort analysis allows product managers to quickly and effectively test a hypothesis and get relevant feedback far more rapidly.

For example, if you assume a particular action taken, like a button or a pop-up, on your site or app may be the key to increasing trial sign-ups, you can define cohorts by platform, demographic, geography, and so on, and immediately compare results to see how each group responded to said action.

Sending traffic to a landing page versus trying to understand long-term effects of users who are acquired through different channels and interact through multiple touch points (email inbox, login straight to the website) are just a few of the valuable insights that can be gleaned when testing your hypothesis with cohort analysis.

The example below shows the results of a test where product functionality was altered among different cohorts, in this case, one-time users, top spenders (whales) and others, to determine if there were any significant changes in average session duration and time spent per user.

https://lh5.googleusercontent.com/xh5D9gV6c_M1axpfE1xWTvmNq0RWc_yuA055zR-o3i7SR3aDn7x0nXYkhwrQEEoeEEEnUTKoo8G9xeDgXbphInL2sWyLwqy99ughZWHFB2A4pgFfJvyj-2l9MAT1eb0LFIuJtnSc

3. Optimizing Conversion Funnel

In the example below, an ecommerce product manager wanted to understand how users from different traffic sources were interacting with their website. By analyzing the conversion funnel broken down by traffic source, they were able to determine that conversion rates for users coming from Bing were not converting at all.

With this understanding, they confronted the marketing department to identify the gap in the customer experience. It turns out, the marketing department had been testing ads for products that weren’t even listed on the website yet. No wonder this traffic wasn’t converting!

https://lh3.googleusercontent.com/w2cJ0n5r-RjCfzXVEiSY3fryZa6R_t8VaDTk1IW7EqsDixFe-iL7rWukzzoPICpha4yfNz4vghIeLkHkAJyfT1eiU57x-wJnmv0ol-wDpQSAtMkgYFSpf3kjIQ1ZhFnWoPesudH03OvJjw2K6w

4. Customer Acquisition by Channel

Many of the companies we work with are looking to analyze inbound traffic among their various channels. This becomes increasingly difficult when those onboarding cycles are lengthy. One particular company with a sales cycle of three-to-six months found it very difficult to understand the effectiveness of its marketing campaigns, and ultimately, the value of potential opportunities. In order to solve this, they turned to the product team for help.

Their goal was to understand within the first month of a launch which marketing campaigns were (and were not) working. By using cohort analysis to segment customers by acquisition channel they were able to identify differences in retention very early on in the lifecycle. Customers from Facebook and Outbrain had significantly higher one and two-day retention, a leading indicator that these campaigns may be more effective in driving customers ready to upgrade from free to premium more quickly.

This allowed them to double down on productive efforts, and loosen resources for less successful rollouts. It was a clear win for the product team as well since they were able to focus their time on converting highly engaged users to premium customers.

 

https://lh5.googleusercontent.com/-NJEMEbGSjQ7KJ3EH67SVDBYZ1uCkMaH0kR1BN1b3FnRT6xi_Qll5AePOUo3aJbsV_QlAXN5f_BH6EKizrNfFGGS8E6xnL41_SOiP4Ji4_V8X-3YVo4k4HAmNlz4rTQDksD_0zwUi_1BxYdJ4A

5. Monetization

Getting existing customers to pay, or pay more, is a considerable concern for product managers. The key to successful monetization is to identify the behaviors associated with the users who are most likely to convert to premium services. Today’s online companies, especially in SaaS and gaming, have realized it is much easier to capture users with a free trial and encourage them to upgrade down the line.

In a monetization driven strategy, product managers need to analyze the behavior of those users who upgraded just prior to the point in which they upgrade. It’s common to find that users hit a specific paywall in a product’s functionality that drives them to upgrade. By tracking and storing all of the behavioral actions of users who convert from free to premium, you can identify exactly which actions within your product are leading to successful conversions and make them prominent features in your customer’s experience.

6. Analyze Users’ Time to Take Desired Action

Analyzing your best customers produces retention and conversion insights. This information allows you to continuously improve the time necessary for a user to take the desired action.

The Fintech industry, for example, is known for its competitive culture and has a lot to gain from behavioral cohort analysis. Its inherent nature of long-term engagement provides the perfect environment for analyzing appropriate behaviors and building out user patterns. By examining cohorts, Fintech companies can behaviorally segment users who complete certain desired actions, and if those actions resulted in retention or churn. This specific information yields the opportunity to improve customer engagement and retention cycles.

One Fintech company, Leverate, recently integrated behavioral cohort analysis into its Forex trading platform to help its customers better understand how the currency market is affecting the behavior of traders. For example, as soon as currency takes a dive, some traders want to jump on it, while others are more timid. Based on user behaviors, Leverate was able to use this intelligence to communicate and interact with traders on a more personal level.

7. Improve Retention

Let’s say you have a poorly performing channel that, with improvement, could achieve increased retention rates. Segmenting users by different channels is beneficial when the goal is to enhance retention.

SaaS brands often launch new products and platform upgrades while using analytics to provide critical churn/retention insights. Cohort analysis allows brands to separate and analyze users by those who signed up after a new product launch or upgrade detailing fundamental behavior differences.

Cohort analysis is a powerful tool for companies to understand their customer’s behavior, in order to increase user acquisition, retention and monetization. With the right platform to gather and analyze user behavior, you can raise retention rates in poor performing channels, put more extensive resources behind channels that produce. Resulting in the identification of user patterns and trends that are otherwise missed opportunities.

Dan Schoenbaum and Evan Kaeding

About DAN SCHOENBAUM AND EVAN KAEDING

Dan Schoenbaum, CEO of Cooladata, a business intelligence, and behavioral analytics platform, is using cohort analysis to bring advanced analytics solutions to fast-growing digital companies that rely heavily on user behavior insights. _________________________________________________________________________________________ Evan Kaeding is a Data Strategist at Cooladata. Leveraging a background in finance and investments, Evan has led teams to use data to make investment decisions and improve operational efficiencies. Proficient in both R and SQL, Evan enjoys building dashboards for teams of all sizes and empowering individuals to decide with data. In his free time, he enjoys reading The Economist, tasting micro-batch chocolate and going outside.

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