A few months ago, we wrote about the many ways in which cohort analysis can help businesses retain customers.
In fact, compared to other forms of data analytics, cohort analysis is one of the easiest ways for a business to run experiments or conduct A/B testing.
Take marketing activity specifically. Cohort analysis can become a primary tool to understand the quality and the efficacy of your business's marketing efforts. Not to forget its ability to forecast the future.
What is Cohort Analysis?
Observing the traits of a group of people (cohort) over a selected period can provide insights into their behavior today and in the future by identifying common patterns.
An example of cohort analysis would be consumers who signed up for a digital catalog after the COVID-19 pandemic broke out.
By using this concept, a digital marketer can track such a group over time to understand it by identifying some common patterns or behaviors.
B2B cohort analysis allows digital marketers to adapt campaigns and messaging based on insights gained from customer data.
Furthermore, they can tailor their marketing activities and strategies to a specific target audience. Marketing teams can use it to group sales leads based on the types of campaigns that interest them in the product, service, or brand.
This process can be used to understand consumer purchasing behavior by analyzing sales leads that respond positively to a specific inbound marketing method on a particular day (or week).
It can address questions like: How long does it take an average consumer to make a repeat purchase? Alternatively, what is the average time between purchases?
How many customers return to the brand after a different type of purchase? When were these customers last engaged with your brand?
These questions all address the proper use of customer cohort analysis to help marketers better target the right customer.
Cohort Analysis and the Business of E-commerce
What is a customer retention analysis in business? E-commerce is a highly competitive world. Brand recall is of paramount importance here, and so is brand loyalty. For both, cohort analysis can be of immense help.
E-commerce marketers can use it to understand the direction their brand is going and their customers. Cohort analysis tools are helpful to track the effect of customer retention campaigns.
Measure Customer Retention Rate Using Cohort Analysis >>> Read more
More specifically, here's how this form of analysis can be helpful for e-commerce marketers:
Conversion rate: To comprehend how fast your leads are converting into customers.
Product/service popularity: Some products or services are bought more often than others. These may also be better at creating long-term customers.
Marketing wisdom says that profit is generated by returning consumers rather than one-time buyers. That's where cohort analysis can help an e-commerce business differentiate between its "one-time" products or services that do not sell as well as those that "fly off the shelf."
Once this differentiation is made, an e-retailer can start to push popular products or services harder and ditch the under-performing ones.
Brand loyalty: It is a known fact that faithful customers spend over 50% more than new customers, on average. Again, marketing rules tell us that it is less expensive to hold on to loyal customers than it is to go out and find new ones.
Cohort Analysis Major Metrics for Customer Retention
How do you do that? Bring together a group of customers for their first experience with your brand.
After that, find out the revenue this particular cohort fetched for you in about, say, nine months.
This form of analysis will help you identify customers from this group who have purchased from your e-commerce business once or multiple times, thus pinpointing the most profitable ones.
Difference between Cohort Analysis and Customer Segmentation
We do see the words "cohort analysis" and "customer segmentation" being used interchangeably, but let us tell you they do not mean the same thing.
Segments and cohorts are also often confused. Customers can be segmented into groups based on certain shared commonalities, the most basic being demography. The RFM model, which stands for Recency, Frequency, and Monetary analysis, is a popular segmentation method.
Cohorts tend to be confused with general demographics that are typically used to segment markets. Demographic categories like income level, gender, and age do not qualify as marketing cohorts.
Demographics help segment mass markets, but they do not allow marketers to track the purchase behavior of individual consumers. However, demographic information can be combined with cohort data to tailor products and marketing campaigns.
Unlike segments, a cohort is a slightly more focused group of customers having the same characteristic. It's akin to putting "similar" clients in a bucket. A typical cohort is mostly a time-sensitive grouping.
For example, those customers who signed on during Christmas this year are being considered. Another differentiator is that when customer groups are not time-dependent, they can be called segments instead of cohorts.
Other typical forms of cohorts besides time-based ones are behavior-based and segment-based ones.
Cohorts are segments, but segments are not cohorts. Cohorts are specifically connected by similar events and periods of time.
The difference between cohorts and segments is that segments are based on commonalities across a broader range of factors.
For example, an entire class of high school seniors graduating in the same year could be one cohort, but they are also one segment because they share age, location, and school affiliation.
Segmentation is essential for marketers because it allows them to understand their customers better, predict behavior, and identify areas for improving services or products.
Most businesses begin by looking at their customers based on age, race, gender, income, and other demographics. While these are essential factors to consider, they don't tell the whole story.
Segmentation allows you to make better decisions by understanding where your customers are coming from, what they value, and how they behave.
In cohort analysis, before proceeding, you need to decide three things: how to define the cohort, which metric to use, and the period over which to measure.
How Marketers Can Use Cohort Analysis
Before a marketing team starts to use cohort analysis, here are some basic questions it needs to ask:
- Do I want to know what's working and what's failing in my marketing campaigns?
- Do I need insights to change my marketing strategy?
Then, it can embark on the cohort analysis journey by first creating a cohort (s).
The latter could be of the following or any other:
For those who have signed up, create a cohort with first-time signups and observe their subsequent actions.
Those who have repurchased over time: Form a cohort of those clients who have repurchased over a specific period of time.
Those who have been retained: Find the customers your business acquired over a period of time, and then check how long they have kept (or are) doing business with your brand.
These cohorts will allow you to understand one or more of the following:
- Just how well your business is at retaining new clients
- Whether your landing pages or signup forms are working
- Why does a specific cohort of customers stop purchasing
- Which product categories are making people come back
- Which marketing channels are popular with your customers
Digital marketers can use cohort analysis to track their marketing campaign's performance. Marketers can scientifically determine which of these are converting and which are not.
It can also be used to determine your consumer retention rate and help you understand whether you need to invest more in retention efforts.
Every stage of a customer lifecycle can be monitored to ensure customers receive sufficient attention at each step in the funnel.
2 Types of Cohort Analysis
- Acquisition cohorts
- Behavioral cohorts
Acquisition Cohorts
These clubs' customers were acquired or had signed up for a particular service. This kind of user acquisition can be monitored according to a set time frame, such as daily, weekly, or more frequently.
Using cohort analysis in marketing, you can determine the effectiveness of your different acquisition channels.
If you acquire users at a specific rate per week, you can look at how many users are active each week after the acquisition. Is the trend increasing or decreasing?
If it's increasing, maybe you need to improve your acquisition efforts. If it's decreasing, you may want to reevaluate your acquisition strategy.
Behavioral Cohorts
On the other hand, behavioral cohorts segment consumers based on their activities over a period of time.
A behavioral cohort analysis is a segment of users defined by the actions they take over your product.
Behavioral cohorts are the best way to group your users to observe different behaviors related to any event or conversion you want to track.
Take, for example, when Google ran an ad campaign to increase its signups in the Photos app.
It was identified that there were two critical behavioral cohorts within the Photos app: users who took selfies, and users who took screenshots.
Because Google knew that selfies and screenshots were important to track for this campaign, they could target users who take selfies to drive signups in the Photos app.
Users with similar activity across your app are most likely to be interested in the same things. They will likely be the best targets for the ad campaign because they are most likely to interact with it.
A behavioral cohort is created by defining the activities that constitute a cohort and then grouping users based on their activity across your app.
Now, to help you understand how cohort analysis can help marketing, here's an example by our data analyst, Pankaj Katkar.
In this illustration, Pankaj has taken the "Online Retail Data Set" from the UCI Machine Learning Repository.
This is a transaction data set containing all the transactions between 12/01/2010 and 12/09/2011 for a UK-based online retail firm.
Using this data, he has done a cohort analysis on the number of active users starting from a particular month.
Here, we are conducting a cohort analysis based on the user's transactions for the month, as the dataset spans one year.
To download this example, fill out this simple form.
Conclusion
Marketing analytics tells you what's working and what's not. Cohort analysis will tell you how to adjust your marketing activities and which marketing activities your team needs to focus on. It helps you answer questions like what your users' retention rate is and when users start to churn, among others.