What is a cohort?
A cohort is defined as a group of people who share one or more common characteristics, attributes, or behaviour, over a certain period. In eCommerce, cohorts can be groups of people who a) registered in May this year b) prefer a certain mode of payment at checkout c) were brought to your eCommerce website by Instagram ads, etc.
For example, using Haldiram’s as a case study, there are multiple aspects to consider. In India, it operates primarily as a snack maker, while overseas, the brand has a reputation for being close to the Indian way of life. In that sense, these two audiences already form a different cohort each. Further, Haldiram’s has a range of sweet and savory offerings, and frequent buyers of each can be considered different cohorts, as they have varying preferences. Now, during the festive season, sweet sales for Haldiram’s may shoot up as a gifting option, so this data allows them to design versatile gift boxes or plan for inventory during a festive period.
This is just one example of what cohort analysis can do. Both eCommerce retail businesses and D2C entities can benefit from cohort analysis, even if they sell just one or two categories of products.
What is the meaning of cohort analysis?
Cohort analysis forms a subset of behavioral analytics, specifically, one that focuses on the activities of a particular cohort. You can also compare two or more different cohorts over a period of time in the context of specific characteristics (apples-to-apples comparison).
Breaking the users into related groups based on desired criteria makes analysis easier and delivers richer, more actionable insights.
Cohort analysis is the preferred technique applied by companies to study the spending trends of their customers (the cohort) over various periods in time, beginning with when they made their first purchase, a.k.a. their “join date.” This can give valuable insights such as whether the business is acquiring or losing more of their target customer population.
Behavioral cohort analysis is another type of cohort analysis that tracks customer/user behavior and activities under a set of circumstances over a certain period. This can get granular or specific depending on the digital product it is being tracked for: whether it is an eCommerce website, online shopping portal, or health app, for instance.
Another branch of Customer cohort analysis can track the channels most successful at yielding customers who consistently spend more on an eCommerce website over the long term: whether they were referred to the eCommerce store by a friend, or a search engine, blog, or social media platform.
Cohort analysis examples: Customer retention and business growth
Another branch of Customer cohort analysis can track the channels most successful at yielding customers who consistently spend more on an eCommerce website over the long term: whether they were referred to the eCommerce store by a friend, or a search engine, blog, or social media platform.
Customer acquisition is important, but customer retention is vital. To this end, businesses must have one eye on the customer churn rate and see how best to retain them for longer. Cohort analysis helps make the distinction between entities such as customer engagement metrics and growth metrics by efficiently processing all incoming data.
Customer acquisition is important, but customer retention is vital. To this end, businesses must have one eye on the customer churn rate and see how best to retain them for longer. Cohort analysis helps make the distinction between entities such as customer engagement metrics and growth metrics by efficiently processing all incoming data.
For example, consumer engagement can be defined by the number of site sessions, plus average time on site, plus purchases frequency and recency. Growth, on the other hand, can be measured by studying new sessions, behavior during these sessions, and the likelihood that a customer makes a purchase during their very first session.
These are insights no eCommerce businesses can afford to miss out on, given the highly competitive market. Cohort analysis helps drive critical business decisions that use data to represent what is actually going on with the business.
How cohort analysis is performed and read:
Typically, cohort analysis is a process that needs experts to gather and read data. The steps involved in the process are data collection and organization, creating cohort identifiers, or the parameters that will define the cohort eventually, determining customer lifecycle stages, and using this analysis to create new retail processes.
Manual cohort analysis: Challenges and solutions
eCommerce is a fast-moving domain. Today, every consumer has access to multiple retail vendors vying for attention. Manual cohort analysis performed on such vast swathes of data takes up valuable time that can instead be used to ensure smooth operations and let the brand focus on revenue and better customer experience.
Data-driven decision making cannot be compromised upon, and manual processes create redundancies and take up time.
This is where Graas' AI Predictive Engine plays a role, offering rich insights and cohort analysis from data gathered through the entire eCommerce ecosystem.
The Graas Platform can connect to over 20 data sources across the eCommerce supply chain and deliver rich insights, thereby automating the process of creating cohorts specific to each business case.
Customer cohort analysis: Real-world examples and application
Customer cohort analysis has several benefits in addition to the ones already mentioned above:
Identifying how new feature adoption is working in a specific customer subset (i.e. cohort)
Buyers who engage with a brand during a sale period can be a cohort, demonstrating high affinity for discounts
Frequent coupon code users demonstrate a higher degree of loyalty and brand engagement
City specific cohorts can help with hyperlocal sales promotions and events during local festivals
In general, cohort analysis allows for more granular, qualitative insights than using a larger umbrella like a demographic survey. For example, cohort analysis can help determine buying patterns and future purchase behavior for each cohort, as well as determine the messaging that resonates most with them.
In general, cohort analysis allows for more granular, qualitative insights than using a larger umbrella like a demographic survey. For example, cohort analysis can help determine buying patterns and future purchase behavior for each cohort, as well as determine the messaging that resonates most with them.
Outside of purchases alone, cohort analysis can also be used to study pre-purchase behavior. Where do customers drop off most often? What is causing them to abandon their cart? Such data is very valuable for D2C brands that primarily garner audience engagement online.
Drawing from our Haldiram’s example earlier, the creation of a cohort that purchases more sweets during Diwali can further be qualified by geography, age group, shopping times online, and more, to help define
Where to stock extra inventory to deliver online orders on time during the festive season
What kind of messaging to use to appeal to the sensibilities and cultural needs of this cohort
When to spend money on paid advertising to bring people to the eCommerce site
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