Accelerating Formula 1® Fan Engagement with AWS and Salesforce

F1 captivates speed enthusiasts worldwide with its thrilling races. The brand has a long history of using pioneering technology. “It’s part of our DNA. The tech produced in F1 appears in everyday life, whether that be in buttons on steering wheels, hybrid car technology, or even washing machines,” says Matt Kemp, senior CRM manager at F1. “That said, we have traditionally been a business-to-business organization, and our business-to-consumer journey is relatively young.”
 
Back in 2017, F1 had a massive global fan base of 500 million people, but only a fraction—less than 1 million—was accounted for in its database. To bridge this gap, F1 initiated direct-to-consumer products, and in 6 years (2017–2023), the brand grew its fan base by 20 times. Over the same period, F1 increased its global audience from 500 million to 700 million people, with an ambition to reach 1 billion by 2027. With initiatives such as F1 TV, F1 Sim Racing, and F1 Fantasy, F1’s fan base has also become more diverse. “When we started to understand the profile of our fans, that opened up new markets for us,” says Kemp. “The benefit of growing and diversifying the fan base was clear.”
 
To achieve its goals of reaching 1 billion fans and doubling engagement, F1 needed to develop a technical infrastructure to support that volume of users. Then, F1 could engage and retain both new and existing fans and connect with them where they are, which is often far from the racetrack. In 2018, F1 began working closely alongside AWS and Salesforce to transform its business in the cloud. “Our history of using AWS to manage the track data has given us great confidence that it is the right solution for us” says Kemp. In 2023, F1 was ready to develop its Fan Personalisation Programme (FPP), which aims to create a unified 360-degree customer view in addition to innovative fan experiences and personalisation at scale.

FPP unifies data from multiple sources into a centralised data lake on AWS, giving F1 a more comprehensive view of its fans. “Our previous solution took 24–48 hours to stitch data together,” says Raul Alexis, commercial partnerships at F1. “So the data was often outdated before we could even think about segmenting and bringing it to life through an activation channel.”
 
From this data foundation, F1 manages its customer journey in Salesforce Data Cloud, a cloud-based digital marketing solution powered by artificial intelligence (AI). “Having our data in Salesforce Data Cloud means that audiences are predefined, and we get the richness of the data all in one place,” says Alexis. “Then, we can use Salesforce Marketing Cloud to personalise that data on a one-to-one basis at scale. That helps us meet our fans on their terms in NRT, keeping their attention.”
 
To unlock the huge potential of AI and machine learning (ML), F1 is building its ML operations using Amazon SageMaker—a fully managed service for building, training, and deploying ML models for virtually any use case. F1 uses the service for advanced modeling, and bringing ML models into production.
 
With the Bring Your Own Model capability of Salesforce Einstein Studio, F1 connects its models to the Salesforce Data Cloud and operationalises them. Using AWS and Salesforce Data Cloud, F1 can personalise the fan experience across every touch point, drive efficiencies across the organisation, and optimise decision-making. “We can unlock personalised communications with anything from an AI-written newsletter with dynamic content that is based on specific customer segmentation to email subject lines that are dynamically created according to customer preferences,” says Adam Forrest, senior vice president of global brand and sports partnerships at Salesforce.
 
On AWS, a customer can access Salesforce data securely with a zero-copy approach. Moreover, Amazon SageMaker Data Wrangler, which reduces the time it takes to aggregate and prepare tabular and image data for ML from weeks to minutes, can connect to Salesforce Data Cloud. Thus, data analysts and scientists can access Salesforce data in NRT, prepare features for ML models, and scale processing with Amazon SageMaker jobs and pipelines. Amazon SageMaker also offers project templates for Salesforce, providing an automated way to deploy endpoints for traditional and large language models and expose them as APIs. This compatibility between AWS and Salesforce can help F1 connect data across properties in NRT; deepen its understanding of fans; create more accurate segmentation, activation, and measurement; and unlock new possibilities for F1 to surprise and delight its global fan base.