Cohort Analysis – All You Can Do with Acquisition Cohorts
Digitization has changed how customers shop and helped e-commerce businesses gain momentum. This has led to the rise in competition. And to sustain this, organizations are on the constant lookout for the techniques and strategies that can help them gain a sustainable advantage over their competitors. One pertinent way is to understand the nuances of customer behavior, buying patterns and plan strategies accordingly to match the chord. Cohort analysis is a great tool that helps achieve this.
What is Cohort Analysis?
A cohort is a group of people that carry a common action within a specific period (for instance, everyone who shopped for the first time in January 2021). Cohort analysis refers to the practice of studying the activities or habits of specific cohorts over a set period.
Some of the metrics used for doing cohort analysis are repeat rate, orders per customer, the time between orders, average order value (AOV), revenue, customer lifetime value (CLTV).
Cohorts are broadly categorized as below:
- Acquisition Cohorts: Groups divided based on when they made the first purchase.
- Behavioral Cohorts: Groups divided based on their behaviors and actions on the website over a time frame.
While acquisition cohorts help to determine the who and the when, behavioral cohorts enable one to dive into the why.
Here we discuss the major uses of acquisition cohorts in e-commerce.
Find channels/ campaigns which bring high-value customers:
As a marketer, one might not have much trouble in acquiring new customers–but what matters is the quality of acquisitions (in terms of the total amount spent by the customers). If two-thirds of the customers are onetime purchasers and AOV is low, it adds to the cost of acquisition and marketing efforts instead of adding to the top line. Hence it is crucial to identify high-value customers and understand where they came from. This helps in focusing future marketing efforts on those channels and campaigns to bring in more such customers. Doing a cohort analysis of customers against AOV over the last quarter, split by acquisition channel/ campaign, gives these insights.
For instance, if customers coming from both organic search and paid search spent the most per unit transaction, one can run more paid search campaigns for the same keywords and invest in improving the website’s SEO to gain high-value customers.
Similarly, if customers coming from referrals or emails stagnate after one purchase, it means that these channels are not contributing to bringing high intent customers and one needs to fine-tune the target criteria.
Discover retention drivers:
It is crucial to identify the factors that motivate customers to make repeat purchases and facilitate them to improve retention rates. One can discover the factors affecting retention by comparing different cohorts as below:
- By the month of purchase: One can see if month-long campaigns, site changes, or seasonality (an upcoming holiday, new season launches, etc) impact retention rates in certain months.
- By first product bought: One can look if there is any correlation between the first purchased product, retention rate, and promote such products in combination. In most cases, it is the first purchase that affects customer’s satisfaction and their intent to place future orders.
- By coupon used for first-order: This kind of analysis reveals what kinds of promotions work best with customers. For instance, deep discount coupons might not attract loyal customers but only one-timers. This information can help one in devising promotional strategies to attract more loyal customers.
Identify churn patterns:
A high churn rate is a big setback for all e-commerce businesses as it adds to the cost of customer acquisition. One can attempt to persuade customers to stick around longer, even become loyal, by identifying the friction points in the customer life cycle and rectifying them. Doing a cohort analysis of customers against repeat purchase rate over past year split by acquisition month can give these insights.
For instance, if most of the customers drop off in the second month after the first purchase, one can motivate them to stay longer and make purchases by sending promotional offers or up-selling emails.
Separate growth trends from customer acquisition trends:
Another important problem solved by cohort analysis is that it allows marketers to get a clear picture of customer acquisitions. When looking at plain numbers, a growing customer base can easily mask the lack of desired activity by a group of customers or vice versa.
For instance, doing a cohort analysis of customers against revenue generated over the last 6 months, split by acquisition month, can give insights into the growth patterns and the quality of acquisitions.
From the above table, one can see that there is a considerable increase in the overall revenue across months. But the revenue from the cohort of customers acquired in each month does not tell a positive story. In October 2020, the total revenue generated is 9,491 where 4,000 came from new customers. But in March 2021, the total revenue generated is 11,932 where only 1000 came from new customers. The revenue growth from older cohorts is masking the insight that the value of newer cohorts has been decreasing over time since October (either from acquiring fewer customers or making less per new customer).
While cohort analysis is complex and it requires a lot of time to look through multiple metrics, the result of this exercise will be incredibly useful.
Acquisition cohorts enable one to dive far deeper into the customer data and come up with insights that are critical and not masked by growth. They give answers to ‘why’ as well as ‘who’, so that one can easily find answers to ‘what’. Only by doing so, one can have a truly comparable base to optimize for growth that is sustainable.