A Beginner’s Guide to Cohort Analysis in Google Analytics: Why is cohort analysis useful?

We interviewed over 100 small and medium-sized businesses (SMBs) on Humanlytics to understand their biggest digital marketing pain points. We found two recurring themes.

Vanity metrics don't add business value.

Vanity metrics are metrics that make you feel good about your business but don't actually help you make decisions (they are not actionable). Not only are they a waste of time, but they can actually mislead your business decisions.

The classic example of a "vanity metric" is an aggregate metric like "new sessions" on your website. Such metrics are very vague because when your number of new sessions increases, there is no way to tell if this is a good thing (attracting new users) or a bad thing (declining retention of old users)?

Short-term metrics are not representative of long-term results

Cohort analysis is critical for most eCommerce businesses because most of your short-term analyses do not reveal your profitability’s true nature.

For example, if you are a subscription business. 

A customer’s first order might only be $50, against a $100 cost of acquisition, which makes the activity of acquiring this customer unprofitable at first glance. 

However, as time elapses, you recover your customer acquisition cost gradually. As more of those $50 orders start to come in on a week-by-week basis, the customer returns a significant amount of profit to your business. 

Therefore, you should pay more attention to the long-term metrics or more valuable metrics to avoid misleading.

This is where cohort analysis comes in. The cohort analysis feature in Google Analytics is the antidote to both problems (misleading vanity metrics and misleading short-term metrics).

What is a cohort?

In a broad sense, a cohort can be something like "people born in 1990". 

However, for business analytics purposes, a cohort is usually a segment of users specifically segmented by acquisition date (i.e., when a user first visited your site), or a segment of users who performed certain actions during a selected time period, such as downloading your app during a specific month or finding your product via social media during the week.

What is cohort analysis?

Cohort analysis is a type of behavioral analysis that allows the data in a dataset to be divided into related groups prior to analysis. These groups or cohorts typically share common characteristics or experiences over a defined period of time.

Cohort analysis allows the company to clearly see the patterns in the entire customer (or user) life cycle, instead of blindly cutting all customers into thin slices regardless of the natural cycle experienced by the customer.

Conducting cohort analysis is one of the most useful ways to experiment for your business. As a marketer, you can run a time-limited ad campaign with certain features you want to test: ad content, marketing channels, target audience, targeted web design, etc. 

You can then compare metrics (such as reach, engagement and conversions) across these different campaigns to see which elements of the campaign are actually bringing value to your business and which are not.

What is cohort analysis good for?

First, focus on more valuable metrics, which are the true value and purpose of marketing analysis.

1)Cohort Analysis to calculate customer lifetime value 

Analyzing cohorts based on acquisition time periods, such as grouping customers by the month they signed up, allows you to see the value of a customer to your company over time. 

You can then further group these cohorts by time, segment and size to assess which acquisition channels deliver the best customer lifetime value (CLV).

2) Cohort Analysis to improve customer retention

Cohort analysis involves looking at the population over time and seeing how their behavior changes. 

For example, if we send an email notification to 100 people, some may buy on day 1, less on day 2, even fewer on day 3, and so on. 

However, if we send another email to 100 people, after a few weeks, they will buy the product on "day 0" while the first email sent may show its general lag effect on the purchase decision.

3) Cohort Analysis to understand user behavior 

With cohort analysis, you can see how the actions taken or not taken by people in your cohort translate into changes in business metrics, such as acquisition and retention. you can adjust your campaigns based on that feedback.

Here are some of the factors that may affect the user behavior you want to analyze through cohort analysis:

• Target audience

• Ad content

• Channels

• Ad campaigns/experiments

• Website redesign

• New product lines and service offerings

• Sales, discounts, promotions

4) Cohort Analysis to understand customer churn

You can collect data to evaluate your hypothesis about whether a particular customer action or attribute leads to another customer action or attribute, such as whether signups associated with a specific promotion lead to greater customer churn.

5)Cohort Analysis to Predict user purchases 

With cohort analysis, you can see how the actions taken or not taken by people in your cohort translate into changes in business metrics.

Understand the frequency of user purchases to predict the next purchase, so you can place your campaigns more appropriately based on the predicted time.

Cohort analysis allows you to isolate the impact of a variable. In web analytics, you can compare the performance of cohorts in traffic metrics (e.g. returning users), engagement metrics (e.g. average session duration), or conversion metrics (e.g. sessions with transactions).

Example of Cohort Analysis to Get You Started

For example, let's say you are a marketer for a SaaS company. You ran a Google Adwords campaign in August, and as a result, your metrics improved.

At first glance, you might be thinking to yourself, this is great, we've had a huge increase in volume, which must mean that Adwords is a great marketing channel for our company. We should be spending more on similar ad campaigns.

However, cohort analysis allows you to look deeper and possibly come to a different conclusion. It answers the following question, "Of all the users who downloaded your product in August, how many more engaged for 1 month, 2 months, 3 months, etc."

Let's say you have performed a like-for-like cohort analysis and find that for the month of August there is a huge drop off after each month. There could be several reasons for the lack of retention. For example, here are two potential factors.

Mis-targeting: This could indicate that your team is using ads to target the wrong audience. Your ad attracted traffic, but it may not have been the right traffic, meaning that the group of users downloaded the app and then fell off in a big way over the next few months.

Need to focus more on engagement: Perhaps your marketing team is putting a lot of effort into getting people to join the software product, but not enough effort into maintaining engagement.

Conclusion:

If you’re looking for a way to segment your data in a more manageable way, then the cohort analysis feature in Google Analytics is a great way to focus on specific audiences.

You can use it to learn more about the created audience segments (also known as cohorts), understand how their behavior differs from other audience segments, and use the feedback results to optimize business strategies and enable companies to obtain long-term development and profitability.