E-commerce Brands

Find the customers who come back.

Stop paying for one-time buyers. Find the channels and products that create customers who come back.

Available

Which acquisition sources bring repeat purchasers?

Connect first-order sources to repeat purchases and returns.

Decision supported: Investigate sources associated with repeat demand.

Example / not a product result

Illustrative data

Compare first product → repeat purchase → return within equal observation periods, leaving missing sources unresolved.

Required source mapping (example): Shopify · Stripe · Google Analytics
Required capabilities: Customer journeys · Acquisition attribution · Customer value · Refund-aware financial reporting · Data Trust
Data Trust limitations: Validate order/payment matching, returns, consent and source coverage. No complete profit calculation.

Available

Which campaigns drive purchases but little long-term value?

Compare campaign purchases with later customer revenue.

Decision supported: Investigate quality before reallocating spend.

Example / not a product result

Illustrative data

A campaign with more initial orders may have less subsequent net revenue; investigate comparable customer cohorts.

Required source mapping (example): Google Ads · Google Analytics · Stripe
Required capabilities: Acquisition attribution · Customer journeys · Customer value · Refund-aware financial reporting · Data Trust
Data Trust limitations: Validate identifiers, attribution windows and equal cohort maturity. Incomplete costs prevent profit conclusions.

Available

Which customer groups spend more over time?

Compare observed customer revenue by first purchase or cohort.

Decision supported: Investigate valuable cohorts and offer sequences.

Example / not a product result

Illustrative data

Compare 90-day observed revenue for two acquisition cohorts rather than unequal lifetime totals.

Required source mapping (example): Stripe
Required capabilities: Customer journeys · Customer value · Refund-aware financial reporting · Data Trust
Data Trust limitations: Use equal observation windows. Missing history and identity errors understate value; future lifetime value is not guaranteed.

Available

How much revenue remains after refunds?

Separate gross revenue, linked refunds and defined net revenue.

Decision supported: Assess offers after refund losses.

Example / not a product result

Illustrative data

Illustrative: $24,000 gross − $1,800 assigned refunds = $22,200 defined net revenue, not profit.

Required source mapping (example): Stripe
Required capabilities: Revenue intelligence · Refund-aware financial reporting · Reconciliation · Data Trust
Data Trust limitations: Validate refund dates, tax, currency and order scope. Net revenue is not profit or payouts; costs are not assumed covered.

Available

How much of my attributed revenue is supported by available evidence?

Separate supported matches from unresolved revenue.

Decision supported: Investigate missing evidence before committing spend.

Example / not a product result

Illustrative data

Illustrative: 84 matched and 16 unmatched records out of 100; no claim that matched records prove channel causation.

Required source mapping (example): Stripe · Google Analytics
Required capabilities: Acquisition attribution · Reconciliation · Data Trust
Data Trust limitations: Matching is not causation. Missing evidence stays unknown; no invented confidence score.

Customer journey / Example 014

USD · Observed purchase records

Illustrative data

01Entry purchase+$97
02Program upgrade+$497
03Partial refund−$97

Cumulative net revenue

$497

Observed source: paid social. Not proof of causation.

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