You can begin to see if discounts result in loyal customers, or are instead just losing revenue.
6. Restrict discount campaigns to non-converting leads ft. Stitch Fix
There are other ways cohort analysis can help eCommerce stores optimize discount campaigns.
When leads do not convert, they can be placed into a separate behavioral cohort. You are then free to give more aggressive discount offers without fear of giving away margin unnecessarily.
Stitch Fix provides a great example. This message is sent if a user completes their initial fit assessment but do not complete a purchase.
The offer is a clear, $35 credit that expires if unused.
Enriching profiles is a fundamental technique in improving cohort analysis' effectiveness.
New visitors are often anonymous. Fashion Nova proactively addresses this issue with their initial welcome pop-up. In addition to offering an aggressive incentive (30% off), they also allow users to select what their preferences are.
This data is then tied to the customer profile and used in cohort segmentations as well as personalizing future offers.
8. Build better welcome campaigns
The goal of welcome campaigns is to establish relationships and create sales.
We covered how to create a multi-step welcome campaign, along with a best in class example from Sephora here. However, to dial in your own triggered email campaigns, you should create AB tests and analyze them through a cohort analysis.
You can quickly chart how various segments performed over the next 30 and 60 day periods, and ultimately understand which welcome series does a better job of transforming new visitors to repeat customers.