Cohort Analysis
Analyzes various customer cohorts to extract valuable insights into their behavior. Learn how Bloom helps with this crucial process and the benefits you can get.
Bloom provides comprehensive Cohort Analysis for merchants to understand trends and patterns in customer behavior, enabling them to accurately gauge a customer’s lifetime value to their Shopify store.
Note: A cohort is a group of customers that is created based on certain criteria. There are industry standard cohorts as well as custom ones.
Cohort Analysis lets you:
Compare key metrics across cohorts to understand shifts in customer behavior
Analyze the behavioral changes to uncover the reasons behind them
Make informed, strategic business decisions that align with evolving customer preferences and behavior.
With the insights into customer behavior provided by Bloom’s Cohort Analysis, you can:
Estimate the lifetime value offered by customers
Determine the budget to acquire new customers
Gauge the effectiveness of promotional campaigns
Identify opportunities for business strategy changes and market expansion
Types of Cohort Analysis
Bloom offers four cohort analysis metrics:
Accumulated Sales per Customer
This analysis zeroes in on total sales per customer within a chosen time frame. It is crucial for gauging the revenue each customer contributes over time.
Customers
Focusing on customer retention and new customer gains, this analysis tracks the count of customers in each cohort through time, offering insights into how well you're maintaining and growing your customer base.
Cohort Sales
By monitoring the sales success of various cohorts over time, this analysis helps reveal their purchasing trends and behavior, essential to develop various business strategies for targeting them.
Cohort Transactions
Delving into each cohort's transaction numbers over time, this analysis sheds light on customer engagement levels and how often they're purchasing, providing a deeper understanding of buying habits.
How to Use Cohort Analysis
To use the Cohort Analysis feature:
Navigate to the Cohort Analysis section of the app.
Choose the type of cohort analysis you want to perform (e.g., Accumulated Sales per Customer, Customers, Cohort Sales, Cohort Transactions).
Specify the time period.
Analyzing Data for a Single Cohort
The following example details an analysis report for a cohort of customers who initiated their first purchase in May 2023.
Cohort: The first cell in each row identifies the cohort under analysis. In this case, it’s customers with their initial order in May 2023.
New Customers: Represents the total number of customers in the cohort who made their first purchase in May 2023.
First Order: This column provides an initial estimate of the average value a new customer brings to the cohort.
It’s calculated by dividing the total sales from the first order by the total number of customers in the cohort.
Ex: Customers in the May 2023 cohort had an average first order value of $104.
Evolution of Customer Value Over Time: The columns with numbers as titles represent the number of repeat purchases made by cohort customers during the monitored period. They help estimate customer value over time.
The formula applied is: (Total first order sales + Total additional sales from the cohort in the same month) / Total customers in the cohort.
Ex: Three months post their first orders, the May 2023 cohort's customers had an increased average value of $211.
Use Cases
Identifying Valuable Customers
You can leverage the data to identify characteristics common among customers who make repeat purchases, such as the marketing channel used, geographic location, and participation in a loyalty program, etc.
Employ filters and cohort comparisons to accurately categorize and engage high-value customers.
Understanding Customer Behavior
Monitor trends in average customer value over time to single out any stagnation in customer spending. Compare these findings with earlier cohorts to identify consistent patterns or notable variances. Adjust the analysis period and apply filters to gain deeper insights into customer purchasing behavior.
Analyzing Data Across Multiple Cohorts
Here is a look at how Bloom assists in comparing data across multiple cohorts, identifying shifts in customer behavior for strategic business decisions.
Comparison Within Columns
Effectively compare cohorts by analyzing individual columns in the cohort analysis report to see how customer behavior changes over time.
Example Analysis
First Order Values: Comparing the initial purchase values across cohorts provides insight into the first-time buying behavior.
Ex: The report indicates consistent first order values across all cohorts.
Repeat Sales Over Time: Post the first order, sales across cohorts are evaluated for trends in customer retention and engagement over the monitored period.
Ex: The May 2023 cohort exhibited higher repeat sales in the months following their first purchases compared to subsequent cohorts. A trend of decreasing repeat sales is observed across newer customer groups.
Customizing Your Cohort Analysis
Bloom offers a range of customization options for cohort analysis. Alter your cohort analysis process to better fit your requirements and understand your customers in-depth.
1. Set Your Cohort Date Range
Date Range Selection: By default, the analysis reflects the past six months data. Choose a predefined range or specify a custom one using the “Date Range” field.
Using Shorter vs. Longer Periods:
Shorter Durations: Best suited for businesses with rapidly changing customer dynamics or for evaluating new cohorts.
Longer Durations: Ideal for enterprises with consistent customer behavior over prolonged periods.
Cohort Grouping Options: Categorize customers into cohorts based on when they first purchased. Options include weekly, monthly, quarterly and yearly groupings.
Choosing Cohort Sizes:
Smaller Cohorts (Weekly/Monthly): Recommended for quickly analyzing emerging customer trends or for businesses with dynamic customer bases.
Larger Cohorts (Quarterly/Yearly): Suitable for insights into customer patterns that remain stable over time.
2. Selecting Metrics for Analysis
Bloom provides four distinct metrics to enrich your analysis. While the standard metric presented is the total sales per customer, additional metrics can be accessed via the dropdown menu.
The metrics available are:
Accumulated Sales per Customer: Tracks the total revenue generated on average by a customer within a cohort over the monitored period.
Customers: Counts the individuals from each cohort who have made repeat purchases over time.
Cohort Transactions: Details the number of transactions attributed to each cohort during the monitored period.
Cohort Sales: Shows how cumulative sales volume of each cohort increases with time.
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