retail-cpg
Analytics
representative

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.

Runs onBodham

digital marketing campaign Return on Ad Spend (ROAS) increased: 28%digital marketing campaign Return on Ad Spend (ROAS) increased
customer cohort analysis turnaround accelerated: 3 weekscustomer cohort analysis turnaround accelerated

How the work runs

The pressure that made this worth automating, the steps the system runs, and what came out the other side.

Pressure & Trigger Points

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.

The run · 5 operational steps

Click any step to inspect telemetry signals, model reasoning, and governance gates.

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Autonomous Multi Table Cross Database Joins

Bodham joins marketing attribution, ecommerce transaction, and return log tables automatically based on customer identifiers.

Input Signal:

Real-time operational telemetry & queue

Reasoning Pattern:

MCP grounded vector inference

Governance Gate:

Policy constrained with audit write-back

Verified Business Outcomes

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.

Capabilities this relies on

nl to query
system connectors
workflow orchestration
evidence audit trail

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