Reckitt
genAI marketing agents across the concept-adaptation-analysis chain
Reckitt deployed with BCG a suite of genAI marketing agents to more than 500 marketers across four markets, claiming up to 60% less time on concept, about 30% on ad localization, and up to 90% on post-campaign media analysis.
Key points
- Suite of genAI marketing agents (concept, adaptation, analysis) co-built with BCG.
- Custom GPTs trained on Reckitt data, industrialization with EPAM.
- Concept -60%, ad localization -30%, post-campaign analysis -90% with quality doubled.
- Deployed to more than 500 marketers across 4 markets, evidence B, status confirmed.
Objective
Reduce the time spent on repetitive marketing tasks (concept, ad adaptation by market, post-campaign analysis) while keeping or improving quality, and extend the use to several hundred marketers across several markets.
The deployment
Reckitt built with BCG a suite of marketing-focused genAI solutions rather than scattering them across isolated pilots. The tools rely on custom GPTs trained on Reckitt data and cover three chained tasks. Product concept generation, where Reckitt reports up to 60 percent less time with improved quality. Ad adaptation and localization, with about 30 percent less time. Post-campaign media analysis, where time spent drops up to 90 percent with quality doubled. These results were first presented by CMO Fabrice Beaulieu at the Cannes Lions festival in June 2024, on a four-month pilot covering brands such as Gaviscon and Finish. Reckitt then deployed a suite of marketing agents to more than 500 marketers across four markets, with an announced doubling, working on industrialization with EPAM. The claimed approach targets tasks that represented 30 to 40 percent of the teams' time.
Results Proof B
Figures published by Reckitt (official release and the CMO's presentation at Cannes Lions), corroborated by BCG, co-builder of the platform, and by specialist press (Digiday, EPAM). No isolated figure in financial results, hence B.
How it works
Inferred typical approachThe internal detail is not public. Here is a proven approach that leads to the same result, to adapt to your stack.
The stack in detail
- llm GPT custom Custom GPTs trained on Reckitt data for concept, ad adaptation, and post-campaign analysis; the exact hosting infrastructure is not public.
- plateforme Plateforme genAI marketing interne Reckitt Suite of marketing-focused genAI solutions, co-built with BCG, including multimodal tools and agents.
- integrateur Boston Consulting Group (BCG) Co-building of the platform and the genAI marketing solutions.
- integrateur EPAM Industrialization and deployment of agentic marketing AI at scale (more than 500 marketers, four markets).
How it runs, concretely
For ops teams-
1Concept generation marketing / AI
The marketer produces product concepts via a genAI interface, up to 60 percent faster.
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2Adaptation and localization marketing / AI
Ad variants and localization by market via multimodal tools, about 30 percent faster.
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3Post-campaign analysis AI / data team
Automatic synthesis of media performance after a campaign, up to 90 percent less time.
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4Scaling up marketing / EPAM
Rollout of the agent suite to more than 500 marketers across four markets, with an announced extension.
The quality and freshness of the custom GPTs trained on Reckitt data (brand, campaign history). If the data corpus drifts, localized adaptation and post-campaign analysis lose relevance.
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
- usable brand corpus and campaign history
- creative assets for adaptation and localization
- structured post-campaign media data
Org prerequisites
- focus on one domain (marketing) rather than scattered pilots
- change management with the marketers
- quality governance of the custom GPTs
Possible stack
- custom GPTs trained on internal data
- multimodal genAI tools for adaptation
- integrator for industrialization (BCG, EPAM type)
The plan, step by step
- Step 1Choose a chain of repetitive tasks (ad adaptation and localization by market) and measure the reference time and quality.Deliverable: Time / quality baseline per task.
- Step 2Build the custom GPTs on the brand corpus and campaign history, with quality governance guardrails.Deliverable: GenAI tools operable by marketers on a pilot scope.
- Step 3Run the pilot with a group of marketers on 2-3 brands, measuring before / after.Deliverable: Quantified measurement of time gains and perceived quality.
- Step 4Extend by market with training and change management, concentrating the effort on the marketing domain rather than scattered pilots.Deliverable: Rollout to a broader population with quality governance in place.
First step: Choose a chain of repetitive marketing tasks (ad adaptation by market) and measure time before / after.
The same pattern, proven in other industries
adidas
AI-driven audience prospecting and bidding (ARO) across the full funnel
Allwyn
AI-driven omnichannel programmatic buying with retail data activation
Best Choice Products
real-time AI optimization of bids, budget, and placement on retail media
Sources
- S1 Reckitt's first GenAI results demonstrate changing face of marketing during Cannes Lions Primary archive pending
- S2 How Reckitt Reinvented Innovation-to-Launch with AI Interested party archive pending
- S3 How Reckitt is beating the AI odds with its approach to pilots Established press archive pending
- S4 This Is Not a Pilot: Lessons from Reckitt's Global Rollout of Agentic AI Secondary archive pending
An error, newer info, a source?
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