Multi Table Customer Cohort Retention & Campaign Attribution in Ecommerce
A direct to consumer omnichannel apparel brand with 2.5 million customer profiles, 15 digital marketing channels, and complex multi touch purchase journey logs.
Run on Bodham · Conversational Analytics
The challenge
Marketing managers struggled to calculate true 90 day customer lifetime value (LTV) across acquisition channels due to separated marketing, order, and return databases.
Evaluating cohort retention required custom multi table SQL scripts spanning millions of rows, taking weeks of data engineering sprint cycles.
Marketing budget was over allocated to low retention ad campaigns that generated high initial clicks but heavy 30 day return volumes.
How Bodham solves it
Autonomous Multi Table Cross Database Joins
Bodham joins marketing attribution, ecommerce transaction, and return log tables automatically based on customer identifiers.
Conversational Cohort Matrix Generation
Marketers type natural language prompts to compare 90 day repeat purchase rates and net return adjusted LTV across ad channels.
Interactive Visual Cohort Heatmaps
Renders dynamic retention heatmaps, customer migration flows, and acquisition payback curves in real time.
AI Driven Attribution Insights
Highlights specific ad campaigns that attract high value loyal customers versus campaigns driving unprofitable single purchase returns.
Budget Reallocation Modeling
Simulates the revenue and gross margin impact of shifting ad spend across paid search, social, and influencer channels.
The outcome
- digital marketing campaign Return on Ad Spend (ROAS) increased
- 0%digital marketing campaign Return on Ad Spend (ROAS) increased
- customer cohort analysis turnaround accelerated
- 0 weekscustomer cohort analysis turnaround accelerated
Digital marketing campaign Return on Ad Spend (ROAS) increased by 28%.
Customer cohort analysis turnaround accelerated from 3 weeks to under 30 seconds.
Reduced unprofitable acquisition ad spend by $420,000 in the first two quarters.
Representative scenario: a modelled industry situation showing how this accelerator applies. Figures are projections, not a named client result.
Capabilities used
Every accelerator is assembled from the same capability layer, so a workflow you adopt here shares its foundation with the rest of the portfolio.
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