Retail media yield optimisation: test the assumptions behind growth

Start with observed delivery, then model a specific change. Keep assumptions visible, avoid double counting shared inventory and judge yield alongside commerce contribution. A scenario helps frame a test; it does not establish the result.

Start from a reconciled baseline

Yield optimisation asks what could change in an existing media operation. Start with a defined period and consistent measures: eligible opportunities, displayed ads, billable events, gross media fees and retained income. Keep observed inputs distinct from proposed improvements.

Use the same exposure basis when comparing scenarios. Higher traffic, new placements and better performance on existing placements are different explanations for growth.

Change one assumption, then inspect combinations

For a CPC scenario, gross media fees equal delivered ad impressions multiplied by CTR and average revenue per billable click, provided event definitions align. Coverage can change delivered impressions; it does not necessarily change CTR or demand.

Using the separate synthetic example above, assume delivered impressions increase from 300,000 to 360,000 and CTR from 1% to 1.2%, with £0.70 per billable click unchanged. Modelled fees become £3,024, compared with £2,100: an increase of £924, or 44%.

Both assumed inputs increased by 20% relative, so the combined arithmetic is 1.2 × 1.2, not an addition of the percentages. This does not prove those improvements are achievable, independent or caused by a product change.

Protect the outcome you actually want

More eligible inventory can dilute average relevance or encounter insufficient funded demand. Better CTR can change auction economics. Show the joint assumptions rather than presenting separate upside estimates as automatically additive.

Review retained media income alongside conversion, product contribution and shopper guardrails. Use controlled measurement to establish the realised effect; the model only describes the scenario.

Keep a decision record

Save the baseline period, input definitions, scenario changes and the evidence still needed. Compare the scenario with subsequently observed delivery and explain material differences. A useful model makes assumptions easier to challenge as well as easier to present.