Church & Dwight
daily causal measurement wired directly into media buying, reallocating budget across retail media campaigns
Church & Dwight, with WPP Media, Incremental and Skai, wired daily causal incrementality modeling directly into its retail media buying, with weekly budget reallocation: the Skai case study of May 2026 reports 292% more media efficiency for Batiste at Target, 161% for TheraBreath at Target and 32% revenue growth for Arm & Hammer Laundry on Instacart, with no increase in working media budget.
Key points
- Church & Dwight wired daily incrementality measurement directly into its retail media buying.
- Three players: WPP Media steering, Incremental for the causal model, Skai for automated execution.
- Batiste on Target: 292% more media efficiency, with spend unchanged.
- Figures published by the vendors and picked up by the trade press, never confirmed by Church & Dwight.
Objective
Move retail media past last click and past the ROAS each platform reports on itself, in order to know which spend actually produces sales that would not have happened otherwise, and to redeploy the existing budget toward those campaigns without asking for more.
The deployment
The starting point is an arbitration problem. On retail media networks, each platform grades its own campaigns and reports a ROAS that also counts sales that would have happened without advertising. Church & Dwight was therefore judging its campaigns on last click, on metrics that arrive late and do not say where to send the next dollar. Three vendors were assembled to answer that question. Incremental produces a daily causal reading at campaign level: the model breaks total sales into three parts, the organic baseline, the effect of digital shelf movements (price, promotions, coupons, ratings and reviews, search position), and what is left, attributed to advertising. That residual share divided by spend gives iROI, incremental return on investment, which Incremental's documentation defines as incremental profit over spend. The method combines econometric modeling, dedicated experiments and quasi-experimental techniques of the difference-in-differences type. Skai brings the other half. A direct API connection between the two platforms pushes Incremental's daily recommendation down into Skai's optimization rules and bidding algorithms, which execute it across thousands of campaigns with no spreadsheet in between. WPP Media, through Wavemaker, holds the strategic guardrails and arbitrates. The most visible operational consequence is the shift from a frozen monthly budget to a weekly reallocation: investment moves each week toward campaigns with high incremental return and pulls back from the rest. Campaign structures were rebuilt to allow granular testing, by SKU, by page type and by product category. The setup was applied to the Batiste, TheraBreath and Arm & Hammer brands, at Target and on Instacart. It won the Grand Prix at The Drum Awards for Commerce Media on April 27, 2026 in Miami, along with the Commerce Media Partnership of the Year award, and Incremental was recognized by the Adweek Tech Stack Awards 2026 in August 2026 on the basis of this case.
Results Proof B
The most precise source is the case study published by Skai on May 22, 2026, which names the brand, the three products, the two retailers and gives the figures with their exact scope. It is an official source, but an interested one: Skai sells the activation platform used in the setup. Incremental, the other vendor, publishes its own figures on the same case. Two trade publications pick up the results, The Drum on April 28, 2026 after its jury, and Adweek on August 4, 2026 in an awards list. None of these texts is independent of the setup it describes: both articles report on award entries. Church & Dwight, for its part, has never confirmed these figures, neither in a press release nor in its financial results. Two discrepancies justify not going higher. First the wording: Skai speaks of media efficiency at Target, while The Drum and Adweek speak of incremental return on investment for the same 292%, with no source saying whether the two measure the same thing. Then the orders of magnitude: Incremental's methodology page claims up to 122% iROI gains across TheraBreath, Batiste and Arm & Hammer Laundry combined, which does not reconcile with the 292% Skai reports on Batiste alone. So the two vendors on the same project do not publish the same figure for the same thing. Church & Dwight's organic growth and group revenue are not counted here: nothing in the sources ties them to this setup.
How it works
Documented architectureThe stack in detail
- plateforme Incremental Ensemble causal model: econometrics, dedicated experiments and difference-in-differences, with daily campaign-level output
- plateforme Skai Omnichannel activation, optimization rules and bidding algorithms on retail media networks
- infra Connexion API Skai-Incremental Direct feed of the daily measurement into media buying workflows, with no manual re-entry
- integrateur WPP Media (Wavemaker) Strategic steering and orchestration of media buying
How it runs, concretely
For ops teams-
1Rebuild the campaign structures Agency commerce activation team
Campaigns are re-cut to allow granular testing by SKU, by page type and by product category. Without that split, measurement has nothing to compare.
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2Daily decomposition of sales Incremental causal model
The model isolates the organic baseline, then removes the effect of prices, promotions, coupons, reviews and search position. What is left is attributed to advertising.
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3Compute incremental return Incremental causal model
The residual share over spend gives iROI per campaign, with response curves showing where spend saturates and where it still has room.
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4Transmission by API Skai-Incremental connection
The day's recommendation flows straight into the optimization rules and bidding algorithms of the activation platform, with no manual re-entry.
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5Automated execution Skai activation platform
Bids and budgets are changed across thousands of campaigns. The sources state that decisions that used to take weeks are now made and applied within hours.
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6Weekly reallocation Agency, based on the model's output
Investment moves each week toward high-return areas and pulls back from low-return ones, with the overall envelope unchanged.
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7Arbitration and guardrails WPP Media with the brand teams
The agency sets what the automation is allowed to do, which brands and which retailers fall in scope, and which limits are not crossed.
Incremental return per campaign, produced by the causal model. It assumes a daily SKU-level sales feed from the retailer and a reading of digital shelf movements. Without that feed, the model cannot separate the organic baseline from the advertising effect, and the system falls back on the ROAS each platform reports, which is the problem it started from.
How your customers perceive this type of use
Sourced studiesLe pricing algorithmique est le terrain le plus inflammable : 68% des consommateurs disent se sentir leses quand les marques utilisent le pricing dynamique et 80% jugent plus dignes de confiance les marques aux prix constants (Gartner, 2024). L'equite percue varie selon le secteur : le pricing dynamique n'est juge juste que par 33% a 40% des repondants selon qu'il s'agit de concerts ou de cinemas (YouGov, 17 marches). Le prix personnalise par les donnees individuelles est le plus rejete : 47% des Americains s'y opposent fermement (Consumer Reports, 2024).
Acceptance conditions
- La constance des prix comme signal de confiance : 80% jugent plus fiables les marques aux prix stables (Gartner 2024)
- Le secteur conditionne l'equite percue : le pricing dynamique est mieux tolere pour les cinemas (40% le jugent juste) que pour les concerts (33%) (YouGov 2024)
Red lines
- Le pricing dynamique percu comme abus : 68% se sentent leses (Gartner 2024)
- Le prix individualise a partir des donnees personnelles : 47% d'opposition ferme (Consumer Reports 2024)
- Les frais caches et hausses imprevues, vecus par 79% des consommateurs sur un an et associes a la perte de confiance (Gartner 2024)
Sources: Gartner 2024 · YouGov 2024 · Consumer Reports 2024
How to replicate
Inference, not sourcedData prerequisites
- A daily SKU-level sales feed from the retailer, not a monthly report
- The history of digital shelf movements: prices, promotions, coupons, ratings and reviews, position in internal search
- Detailed media spend and delivery by campaign and by retailer
- A campaign split fine enough for measurement to have something to compare: by SKU, by page type, by category
- Enough historical depth for the model to establish a credible organic baseline per SKU
Org prerequisites
- Accepting to judge media on incrementality rather than on the ROAS each platform reports, including when the verdict is less flattering
- A media buying platform with a write API on the campaigns of the retail networks in scope
- A commercial agreement with the retailers on sharing sales data
- Governance that allows weekly budget arbitration across brands and retailers, instead of the monthly plan
- A clear mandate on what the automation is allowed to decide on its own
Possible stack
- A third-party causal incrementality measurement vendor, independent of the ad platforms
- A retail media buying platform steerable by API
- A connector between the two, or a native integration
- A data warehouse for retailer sales and digital shelf signals
- A feed of price, promotion and review data by SKU
The plan, step by step
- Step 1Start by checking what sales data the target retailers agree to deliver, and at what frequencyDeliverable: A feasibility map retailer by retailer, saying where the setup is possible and where it is not
- Step 2Re-cut the campaigns so that measurement has comparable unitsDeliverable: A campaign structure by SKU, page type and category
- Step 3Establish an organic baseline per SKU and separate the effect of prices, promotions and reviewsDeliverable: A decomposition of sales into baseline, shelf and advertising
- Step 4Validate the model with dedicated experiments or geographic holdouts before trusting it with budgetsDeliverable: A measured gap between model and experiment, saying whether it can be relied on
- Step 5Wire the model's output into the buying platform, first read-only then write on a limited scopeDeliverable: A measurement-to-activation loop running on a subset of campaigns
- Step 6Move from a frozen monthly budget to weekly reallocation, on that limited scopeDeliverable: A weekly arbitration ritual and a record of budget moves
- Step 7Extend to the other brands and the other retailers once the loop is stableDeliverable: An extended scope with written guardrails
First step: Go and ask a retailer what SKU-level sales data it agrees to deliver, and at what frequency. As long as the answer is monthly or aggregated, no daily causal model holds and the rest of the setup is pointless.
Sources
- S1 How WPP Media Drove Up to 292% Efficiency Gains for Church & Dwight by Replacing Attribution with Incrementality Interested party archive pending
- S2 Why WPP's Church & Dwight work took the Grand Prix - and what it says about commerce media now Secondary archive pending
- S3 How to Prove Incremental ROAS: A Working Methodology (Incremental) Interested party archive pending
- S4 ADWEEK's 2026 Tech Stack Awards: Because Data Is Only as Good as Marketers' Ability to Act On It Secondary archive pending
- S5 Revealed: The Drum Awards for Commerce Media winners 2026 Secondary archive pending
An error, newer info, a source?
This page lives on its accuracy. If a figure has moved, if the deployment has changed, or if you have a higher-quality source, tell us. Every sourced correction is verified before publication.