Analyze Customer Segmentation Data from Spreadsheet
When you analyze customer segmentation data from spreadsheet exports, you work with purchase history, demographics, and behavioral flags that need grouping and comparison. This analysis finds which segments drive revenue, which are growing, and where retention falls off.
Upload your customer segmentation export and get segment performance metrics, churn risk distribution, and lifetime value comparisons without spending hours standardizing labels and aggregating transaction rows.
Drop your file in and ask a question — no account needed.
Upload your spreadsheet.xlsx .xls .csv — free to try, no accountOr start with
What this export contains
| Customer_ID |
| Segment |
| Total_Spend |
| Purchase_Frequency |
| Last_Purchase_Date |
| Acquisition_Date |
| Age_Group |
| Region |
| Churn_Risk |
| Lifetime_Value |
| Product_Category_Preference |
| Email_Engagement_Score |
Common questions
How do I compare segment performance when my export only shows current state?
Import both the current export and a previous period's file. Match customers by Customer_ID and compare their Segment values to identify movement. Calculate the percentage of customers who upgraded, downgraded, or stayed in each segment.
What if my segmentation data has one row per transaction instead of per customer?
Group rows by Customer_ID first. Sum Total_Spend, count Purchase_Frequency, and take the most recent Last_Purchase_Date for each customer. This creates the per-customer view you need before applying segment filters.
How do I find which segment characteristics actually predict high lifetime value?
Filter to customers with Lifetime_Value above your target threshold. Compare the distribution of Age_Group, Region, Product_Category_Preference, and Purchase_Frequency in this group against your overall customer base to identify patterns.
Can I analyze customer segmentation data if segment definitions are missing?
Yes. Create segments from the behavioral columns you have. Group customers by Purchase_Frequency ranges, Total_Spend quartiles, or days since Last_Purchase_Date. These behavioral segments often reveal more than pre-assigned labels.
How do I identify customers at risk of churning from the spreadsheet data?
If you have a Churn_Risk column, filter to high-risk customers and analyze their Last_Purchase_Date, Purchase_Frequency, and Email_Engagement_Score. If not, calculate days since last purchase and flag customers beyond your typical purchase cycle.
Try it with your own file
DataMimi reads the file you actually have — merged cells, headers below row one, totals pasted at the bottom — and shows which rows and columns every number came from.
Ask about your file