Bayer Consumer Health
machine learning demand forecasting on search trends to drive media
Bayer Consumer Health built a model on Google Cloud that forecasts cold and flu search peaks by region, driving a paid search program to +85% click-through rate, -33% cost per click, and 2.6x traffic year-on-year.
Key points
- ML model forecasting cold and flu search peaks by region.
- Built on Google Cloud with Google Trends and weather/climate data, activated on paid search.
- Paid search: +85% CTR, -33% cost per click, 2.6x traffic year-on-year.
- Evidence level B, mixed-signals status (Australia case, global rollout targeted).
Objective
Forecast cold and flu product search peaks by region, to give marketing teams time to plan and activate campaigns at the right moment, and serve the right offer to consumers looking to relieve their symptoms.
The deployment
The Bayer Consumer Health team in Australia built, in early 2022, a forecasting model on Google Cloud's machine learning technology, combining Google Trends data with open external data such as weather and climate, to anticipate cold and flu trends by Australian region. These forecasts served as data-driven marketing triggers to plan and activate media campaigns earlier. On paid search, Bayer reports an 85% increase in click-through rate year-on-year, a 33% reduction in cost per click, and a 2.6x increase in traffic to its site. The project proved convincing enough for the team to aim for a global rollout.
Results Proof B
The media results (85% CTR, 33% cost per click, 2.6x traffic) are documented and quantified by the Google case study (Think with Google), corroborated by a second source on Bayer Consumer Health's AI marketing strategy. Quantified platform case study = B.
How it works
Documented architectureThe stack in detail
- plateforme Google Cloud (machine learning) ML technology on which the forecasting model was built; the source does not specify the exact service (Vertex AI, BigQuery ML).
- outil Google Trends Main model signal: cold and flu search trends by region, crossed with open weather and climate data.
- plateforme Google Ads Paid search activation channel triggered by the forecasts, where CTR, cost per click, and traffic are measured.
How it runs, concretely
For ops teams-
1Signal collection Data team
Google Trends data and open external data (weather, climate) are gathered by region.
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2Forecasting AI
The machine learning model anticipates cold and flu search peaks by region.
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3Media planning Marketing
Marketing uses the lead time gained to plan and calibrate the campaigns.
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4Activation Marketing
The paid search campaigns are triggered at the right moment in the affected regions.
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5Measurement Data and marketing team
CTR, cost per click, and traffic are measured and compared to the previous year.
Search trends by region crossed with weather and climate. Without up-to-date search data and per-region history, the model loses its ability to anticipate the peaks and the timing advantage disappears.
How your customers perceive this type of use
Sourced studiesC'est la famille la moins acceptee : 68% des Americains jugent inacceptable un score financier personnel calcule par algorithme et 67% l'analyse video automatisee d'entretiens d'embauche (Pew Research, 2018). La demande d'explication et de recours est massive : 83% veulent savoir quelles donnees l'IA utilise et 91% veulent pouvoir corriger des donnees erronees (Consumer Reports, 2024). A l'echelle mondiale, seuls 46% se disent prets a faire confiance aux systemes d'IA et 70% jugent une regulation necessaire (KPMG / Universite de Melbourne, 2025).
Acceptance conditions
- Transparence sur les donnees utilisees : 83% des Americains la reclament (Consumer Reports 2024)
- Droit de correction des donnees erronees : 91% le demandent (Consumer Reports 2024)
- Explication de la logique de decision : 44% des consommateurs sont plus enclins a utiliser un agent IA si sa logique est clairement expliquee (Salesforce 2024)
- L'acceptabilite depend du contexte de la decision : 50% des Americains jugent equitable un score de risque criminel pour la liberation conditionnelle, contre 32% pour un score financier applique aux consommateurs (Pew Research 2018)
Red lines
- La decision opaque et sans recours sur l'emploi, le credit ou le logement : 45% tres mal a l'aise pour l'embauche, 39% pour le pret, 39% pour le logement (Consumer Reports 2024)
- Le scoring des personnes a partir de donnees comportementales : 68% le jugent inacceptable pour les offres financieres (Pew Research 2018)
Sources: Pew Research Center 2018 · Consumer Reports 2024 · KPMG / Universite de Melbourne 2025 · Salesforce 2024
How to replicate
Inference, not sourcedData prerequisites
- search trends data by region
- open external data (weather, climate)
- media performance history
Org prerequisites
- marketing data team
- access to an ML platform
- coordination with media activation
Possible stack
- ML platform (Google Cloud, Vertex AI, BigQuery ML or equivalent)
- Google Trends or a trends source
- paid search platform
The plan, step by step
- Step 1Gather the search trends history by region and the open weather data, then align them with the media performance history.Deliverable: Regional dataset covering 2-3 seasons.
- Step 2Train a model to forecast search peaks by region and backtest it against the previous season.Deliverable: Model with forecast error measured on the history.
- Step 3Define the trigger thresholds and the anticipated media planning process with the activation team.Deliverable: Activation calendar driven by the forecasts.
- Step 4Activate on a test season in 1-2 regions and compare CTR, cost per click, and traffic to the previous year.Deliverable: Quantified season assessment against the history.
- Step 5Extend to the other regions and automate the refresh of the forecasts each season.Deliverable: Seasonal forecasting pipeline in production.
First step: Assemble a search trends dataset by region crossed with weather, and test it on one season against the media history.
The same pattern, proven in other industries
Kellanova
predictive scoring of creative effectiveness before delivery
Rakuten
prediction of future buyers for ad targeting
Rovio
LTV prediction to steer acquisition (pLTV)
Sources
- S1 Predicting demand through search trends (Bayer, Think with Google) Interested party archive pending
- S2 Why Bayer Has Turned to AI to Transform Consumer Self-Care 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.