Cohort Modeling, Explained

Cut through data clutter with cohort modeling

Companies are gathering more data than ever on everything from marketing to operational efficiency, yet many executives report feeling more confused than ever before.

This is because an abundance of data isn’t necessarily helpful if it’s disjointed and unorganized. Trying to digest all of this data at once is like playing pin the tail on the donkey with business strategy. Instead, analysts often use cohort models to group data into subsets and highlight the most important trends.

What is a cohort model?

A cohort model, also called a “cohort analysis,” uncovers insights from data by grouping it into subsets based on characteristics rather than looking at it all at once. The groups are called “cohorts” and can be determined based on any characteristic that’s important to the company.

Cohort modeling is often used by technology providers to reveal trends among customers or users. This makes it easier to target groups with services and offers they will find most relevant.

For example, the CEO of a SaaS company may ask their executive team to advise on the most profitable customers and focus marketing and sales efforts on prospects in that group. The executive team might ask the finance department for support with this task. An analyst could build various cohort models based on industry and company size, among other information, to determine which types of customers have the greatest lifetime value.

There are many kinds of cohort models, including:

Time-based cohorts

Analysts can examine behavior patterns based on when customers officially partnered with the company to understand trends that may have otherwise gone unnoticed. This method is often used to analyze churn rates.

For example, if many customers leave soon after onboarding, the company may assume that a competitor is targeting them, there’s an onboarding issue, or that there might be issues with the product.

Certainly, these possibilities should be considered. However, cohort models may reveal other trends. Perhaps the data reveals that customers leave after three years. Knowing these things could change the way the company operates to increase retention.

Segment-based cohorts

Segment-based cohort models group customers based on the product or subscription they have purchased. This may be especially useful for companies with a “freemium” business model in which a small portion of customers makes up the majority of revenue.

This helps tailor offerings to specific groups and their needs. For example, a company could learn that a majority of customers that subscribe to the basic plan cancel after three months, while those that subscribe to the mid-tier plan tend to upgrade to the most premium offer. This might imply that the basic plan needs more features to further entice customers.

Size-based cohorts

Customers can also be analyzed in cohorts based on company size. Companies of different sizes likely have different needs and understanding these needs makes it possible to serve them better. This can also help focus efforts on companies that have lower churn and generate the most revenue.

As you can see, cohort modeling is incredibly valuable to a company as a way to glean more specific data that can help focus efforts. Cohort models are a way for analysts to present executives, who may not have the time to examine every small detail, with the most important takeaways from a given dataset.

Cohort modeling vs. Regular modeling

Compared to regular modeling, cohort modeling gives a more granular view of a company’s data. Analyzing data in one massive group could mean missing outliers that could impact priorities for the company.

For example, one large chart showing overall revenue for the year may show growth. If analysts simply accept that at face value and celebrate a job well done, they could miss that one cohort, such as small businesses or companies in the manufacturing industry, actually had a large decline in sales.

Catching that information early on could prevent further decline that could cause an actual blow to the bottom line the following year. This is why maintaining and analyzing multiple cohort models on a regular basis is crucial for successful data interpretation.

The benefits of bite-sized data

With so much data at their fingertips, many executives find themselves overwhelmed. Department heads may be tempted to present all the information they have, rather than drilling data down into the most important insights.

A cohort model groups data into subsets based on characteristics, like customer size or industry, to give meaningful insights that drive conversions and can help direct the company’s goals. In doing so, cohort models cut through the noise and make it easier to highlight key takeaways.

A great way to start cohort modelling is by looking at integrations for your financial systems. Can you bring together disparate data into a single repository to build the right models with the right data? This is critical to cutting through the data noise and getting to results, and

Causal is getting top marks for its ease-of-use. Check out our integration and models to see how you can get to insights faster.

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