Finding answers to fundamental questions
With the help of Salesforce, fundamental questions could be answered: which doctors in the database prescribe Bayer Consumer Health products? Are there doctors who might primarily prescribe a specific product with whom Bayer has no contact? Data scientists used data from Sales Cloud, segmentation data, and doctors’ reactions to campaigns and supplemented them with geolocation data such as patient proximity to the nearest pharmacy. Salesforce’s data model now makes it possible to link all this data to drive the business - something that was impossible with the predecessor CRM system.
Salesforce serves as a common data platform that gives Bayer a complete 360-degree profile of all its users. Now, the company has visibility of the information it should collect. A database with many characteristics and data points has been created, making it possible to form segments such as channel preference, professional field of interest, and opinion leadership (preferred thought leader). In Marketing Cloud, these micro-segments are used to develop specific customer journeys and campaigns. This has enabled Bayer to acquire new customers and to encourage doctors who already recommend leading Bayer brands, such as Elevit, to prescribe additional products. To do this, Bayer has developed relevant content that underscores the importance of taking prescriptions regularly. “That was something we couldn't really do effectively before,” said Çağri Çaylak, Global IT Product Director and Head of Fragmented Markets at the Consumer Health division of Bayer.
Currently, the company is still in the early stages. But the more data is fed back from the doctors, the more preferences can be processed and used via Salesforce. This makes it possible to further optimise personalisation and launch more effective campaigns and journeys. To this end, the company has already integrated some functionalities of Einstein, Salesforce’s Artificial Intelligence (AI) solution, to improve newsletter subject lines and the distribution timing of messages, for example.