In my post on how to estimate lifetime value for a subscription business, I uploaded a sample cohort analysis that others can use as a template.

I’ve been asked several times how the analysis would differ for an ecommerce business, so I finally got around to uploading a sample cohort analysis for an ecommerce business. Please note that this is a SAMPLE only. Data is dummy data, so you should not use it for benchmarking purposes. I have not allowed editing to the Google doc so that the spreadsheet will be useful to anyone who finds it, but you can download it and edit it offline as you see fit.

For an ecommerce business, rather than focusing on the  percentage of retained subscribers per cohort, instead you focus on the net revenue (after discounts, returns and refunds) from that cohort in a given period. This revenue has to be normalized by dividing by the number of (original) buyers in each cohort so that you can make meaningful comparisons. You should focus on revenue per (original) buyer in each period for each cohort as the raw data from which you can build a lifetime value analysis. Then you should average across cohorts to understand “typical” revenue per sub in period 1, 2, 3 etc, where period 1 is the first month (quarter or year) when you see a buyer make a purchase.


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In my post on how to estimate lifetime value for a subscription business, I uploaded a sample cohort analysis that others can use as a template.

I’ve been asked several times how the analysis would differ for an ecommerce business, so I finally got around to uploading a sample cohort analysis for an ecommerce business. Please note that this is a SAMPLE only. Data is dummy data, so you should not use it for benchmarking purposes. I have not allowed editing to the Google doc so that the spreadsheet will be useful to anyone who finds it, but you can download it and edit it offline as you see fit.

For an ecommerce business, rather than focusing on the  percentage of retained subscribers per cohort, instead you focus on the net revenue (after discounts, returns and refunds) from that cohort in a given period. This revenue has to be normalized by dividing by the number of (original) buyers in each cohort so that you can make meaningful comparisons. You should focus on revenue per (original) buyer in each period for each cohort as the raw data from which you can build a lifetime value analysis. Then you should average across cohorts to understand “typical” revenue per sub in period 1, 2, 3 etc, where period 1 is the first month (quarter or year) when you see a buyer make a purchase.

You still typically see a steep drop off in revenue per buyer after the initial period. But a well-run ecommerce business that does a good job of retention marketing and line expansion should see stable revenue per buyer after the initial drop off. This is in contrast to subscription businesses which typically continue to see attrition over time. If you do see continued drop off, you should model that in a similar way that I do it for subscription businesses, but if you see relatively stable out month revenues per buyer, it’s okay to model that in the out months.

Lifetime Value is calculated as the cumulative contribution of an average customer, so you have to multiply lifetime revenue by contribution margin. Contribution margin should include all variable costs except one time acquisition costs. This typically includes COGS, packaging, shipping and handling, reverse logistics, inventory obsolescence/write offs, customer service, credit card charges, hosting costs, fraud accruals, etc. It would not include fixed costs such as photography, production, site development, merchandising, or other overhead.

The two most important metrics that I look at to gauge the health of an ecommerce business are LTV/Customer Acquisition Cost ratio and payback period. This is why I highlighted these two metrics in the spreadsheet.

I like to see LTV/CAC > 2.5 (which tells you that you have a robust long term business with enough margin to cover overhead) and Payback periods under 12 months.

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