To improve customer experience and revenue, businesses must understand customer behavior. Cohort analysis can help comprehend engagement and retention, leading to actions like improving account creation and creating campaigns for high-volume months.
Analyzing digital performance is crucial to understanding how customers interact with a business and what actions can be taken to improve their experience. In this blog post, we will explore how to use cohort analysis to better understand customer behavior.
A client is looking to understand Digital performance over the last 2 years and is considering developing a loyalty program to drive digital performance. For starters, we can look at the current site mix between new and repeat users to understand how the two groups of visitors behave today.
Current Digital Performance
To understand the stickiness of Digital channel and opportunity to drive repeat purchasers, the next question to answer is: What is the volume of visitors that are returning to the site every month in 2022?
Cohort analysis is a useful tool for understanding how cohorts engage with a brand over time. To start, we determined what questions to answer:
How to set up the Cohort Table
Create a segment based on the ~1,000 Visitors and learn more about them:
Key months highlight opportunity to drive retention based on volume:
Repeat analysis for Orders to understand Repeat Purchasers
Since this client is looking to understand the value of a Loyalty program, the next step in the analysis included adding in the Login success event as an Inclusion metric to the Cohort.
Caveat: Cohort analysis cannot be used for calculated metrics (such as Conversion Rate) or non-integer metrics (such as Revenue). Only metrics that can be used in segments can be used in Cohort Analysis, and they can only be incremented by >1 at a time.
Is the site more likely to retain users that are logging in?
What would be the impact if we could get more users to login? Is that a stickier experience?
Investigate site user experience for getting users to create an account during Checkout
Custom Dimension Cohort: Create cohorts based on the selected dimension, rather than time-based cohorts (default). Many customers want to analyze their cohorts by something other than time and the new Custom Dimension Cohort feature provides you with the flexibility to build cohorts based on dimensions of their choosing. Use dimensions such as marketing channel, campaign, product, page, region, or any other dimension in Adobe Analytics to show how retention changes based on the different values of these dimensions. The
Custom Dimension Cohort segment definition applies the dimension item only as part of the inclusion period, not as part of the return definition.
After choosing the Custom Dimension Cohort option, you can drag and drop whichever dimension you want into the drop zone. This allows you to compare similar dimension items across the same time period. For example, you can compare performance of cities side by
side, products, campaigns, etc. It will return your top 14-dimension items. However, you can use a filter (access it by hovering on the right of the dimension that was dragged on) to display only desired dimension items. A Custom Dimension Cohort cannot be used with the Latency Table feature.
Custom Dimension Cohort table highlights products that are driving higher retention rates than the average. This table helps identify your top products to drive internal and external marketing campaigns featuring top attention-worthy products.
In Feb: 3 products standout with higher retention rates
Cohort analysis and Custom Dimension Cohort are powerful tools for understanding customer behavior and improving digital performance. By analyzing retention rates, login rates, and the impact of specific products, businesses can make data-driven decisions to improve the customer experience and drive growth.
This document was written by:
Jennifer Yacenda, Senior Director at Marriott
Adobe Analytics Champion