Table of Content
- Reducing Churn with Cohort Analysis Gradually
- Cohort analysis and reducing churn rate
- How can you reduce your churn rate?
Reducing Churn with Cohort Analysis Gradually
Marketing and product analytics aims at improving three key metrics: Acquisition, retention, and engagement. Oftentimes, businesses focus disproportionately on acquisition at the expense of retention and engagement. This imbalance perhaps does make some sense; acquisition reflects on “vanity metrics” such as page visits by day and new users by day, and the effect of acquisition campaigns is often more palpable and much quicker than retention campaigns. However, the truth is that retention matters far more than acquisition. Focusing on retention offers a higher ROI: retention campaigns are 5 or more times cheaper than acquisition campaigns - that’s because they target customers who already used and were convinced with the value of your product -, a 5% increase in retention rate may lead to a profit increase of up to 95%, since older and more loyal customers have a greater customer LTV than newcomers, and retention boosts word of mouth - that is, when customers recommend your product to others- which is the most effective form of marketing! Hence, it is financially sound for any business to focus on retention by reducing one key metric: Churn rate.
The churn rate is the rate at which customers stop engaging in any form of business with the company. This can be manifested by unsubscribing, or generally ceasing to visit the company’s platform. Churn can be analogous to having a leaky bucket, no matter how much you fill it, your efforts remain in vain! Although a churn rate of zero is impossible to reach, companies still aim to make that figure as low as possible. However, without enough knowledge and insights, this is not easy to achieve. The churn rate alone does not provide any clarity on the types of customers leaving, and when exactly they left during their customer journey. This is where cohort analysis comes into play.
Cohort analysis is a powerful tool in the product analytics toolkit: it consists of using customer data tracked on the website or mobile app and grouping similar customers into segments or cohorts based on various characteristics (behavioral, demographic, etc). In relation to customer retention, cohort analysis can be useful to create cohorts of users based on when they first started using the product, and hence, it can unearth how old users are engaging with the product compared with new ones and adds more clarity and perspective to retention reports.
Cohort analysis and reducing churn rate:
Cohort analysis is relevant in reducing churn rate since it can answer the following questions:
- When do users churn along their customer journey? And what is the best for re-marketing to re-engage the users?
- Why do users churn? How can I change the user experience to lower the churn rate?
- What factors lead to higher engagement and retention? Where can I include them?
If you have an idea about what’s driving users away and when you already have the tools to solve the problem to reduce churn and increase growth.
How can you reduce your churn rate?
To use cohort analysis to reduce churn it is important to create cohorts based on when users joined the app. On B2Metric IQ, this report is automatically generated on the North Star Metrics page. The period that you choose to group users (daily, weekly, monthly) depends on your business model.
The cohort analysis report is highly informative: read horizontally, the report shows the retention over the user lifetime, vertically, it shows the retention rate across the product lifetime.
The report also shows when users churn in high percentages. For example, if the retention rate suddenly drops from 75% to 15% in week 3, you will know that this is the point where marketing strategies should be deployed to make your users fall in love with your product again.
However, dividing up the cohorts based on time of acquisition is often not enough; ideally, you would like to know the shared characteristics of the customers who stay and those you don’t based on their behavior and engagement with the product.
You can navigate to the Cohort tab and make custom cohorts depending on user properties and events performed. You can then save this cohort and use it for further retention analysis. At this stage, you might discover insights such as:
- Customers who had to complete an onboarding process were more likely to churn
- Customers in a certain age group (e.g. Gen Z) were more likely to stop using the product
After such information is discovered, you can start hypothesizing why this is the case and coming up with solutions:
- Perhaps the onboarding process is too long and cumbersome, which makes users frustrated with the app. A solution would be to shorten it to the bare minimum, or, if possible, drop it altogether
- The app could be too bland and unengaging for Gen Z users, who are inclined to prefer shorter content, preferably in photo or video format
The insights that can be discovered and the solutions that could be deployed are endless, ranging from more personalized emails and offerings, better targeted ads and better timed marketing campaigns.
Wrap-up:
Cohort analysis arms your business with the necessary insights and knowledge to know exactly who your customers are, what drives their engagement and what causes their departure. Using this information, you can craft better marketing campaigns to boost retention rates and company growth. Truly, knowledge is power!
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