How data-led insights defeat competitors and fight climate change

Our partnership began in late 2018 with a goal to help the team sharpen its performance with data. The work and projects we focus on were driven by the team's objective: it's all about lap time.

We also helped the team address a new challenge in season five. Formula E regulations shifted to a timed race of 45 minutes plus one lap. The key to success lay in a blend of data, AI, racing expertise, and engineering knowledge.

To manage the speed and energy consumption of its battery-powered cars, Envision Racing's engineers had to accurately predict the number of laps in a race.

This is where we stepped in. We built an AI-based scenario engine, the Lap Estimate Optimizer (LEO), to inject greater precision into the team's lap-count intelligence and guide the team's racing strategies that season. LEO's algorithms assessed thousands of potential scenarios that could affect a race, such as overtakes, accidents, or sudden rain or hail, to understand how many laps there would be in a race.

With more precise predictions – especially when there were changing track conditions as there were in Paris when the drivers tackled rain, sunshine, and hail all in one race – engineers and drivers gained the insight they needed to balance car speed and energy consumption for superior performance.

LEO helped extract competitive advantage from data.

We then brought our analytics skills to the team's simulator, where drivers spend countless hours practicing for each race to perfect their lap times.

"The simulator plays a big role for me," says driver Robin Frijns. "It's all about preparing for the race weekend and getting the best out of the car's powertrain. The simulator has all the information on corner speeds and more so we can recreate the car, track, and conditions very close to reality."

The simulator hones the driver's performance and generates essential engineering and race-preparation data. But manually extracting useful insights from the enormous volumes of data was time-consuming and inefficient.

Cracking the data challenge

We built the Augmented Race Intelligence solution to quickly generate customized reports and data visualization for engineers and drivers so they can act on the insight based on their racing knowledge. The team could compare and build on each driver's performance to improve racing strategies and speed, perfect braking points, and secure an edge over the competition.

"With Genpact's platform, it's easy for us to see where we lose time and where we can gain time so we can adapt our lines and driving style to the track," says Frijns.

Augmented Race Intelligence was tailor-made for the time-pressured racetrack environment, especially as schedules had to change due to COVID-19 restrictions, often forcing multiple races into a single weekend. Speed of analysis meant that the team could apply the lessons from one race to the next race in less than 24 hours.

This analysis used to take days. We helped the team access new insight within hours of each race.

As our relationship with the team evolved and its faith in our capabilities grew, our work ventured beyond the racetrack.

Experience-led businesses – including motorsport teams – create a competitive advantage by looking at their organizations from the outside in through the lens of the customer or fan. For Envision Racing, attracting a growing base of loyal fans around the world was just as important as winning races. And – once again – data-led decision-making was at its core.

By helping develop a deeper understanding of its current and potential fans, the team could serve them better, nurture affinity, enhance the fan experience, and identify untapped opportunities.

Through Rightpoint, a Genpact company and leader in experience, we rolled out an experience-led research approach with Envision Racing's marketing team. By analyzing a large global dataset, we created detailed personas that revealed fans' media-consumption habits, social-media behavior, and brand preferences. This insight validated some of the team's hypotheses about its audiences and spotted ways to reach new people – for example, in Asia Pacific.

We distilled all of our findings into quick-reference tools the team uses to guide marketing decisions. "The insights we uncovered make it much easier for us to develop more personalized experiences for a variety of fans," says Daniel Matson, head of marketing at Envision Racing. “And, as every decision we make is now backed by data-driven insights, we can act with confidence."

While podium places elevate Envision Racing up the Formula E rankings, the team also battled to raise awareness of the most important race on Earth: the Race Against Climate Change. In 2020, Envision Racing demonstrated how it lives its purpose by becoming the first certified carbon-neutral team on the grid. But reporting on its emissions data took time and was error-prone until. We introduced automation to help the team collate and analyze data faster to maintain its coveted status.

We then created a carbon calculator so each team member could make emissions-conscious travel decisions throughout the year.

The success of our partnership relied on robust foundations.

Clear governance provided transparency into Envision Racing's many projects, requests, and updates. We used development platform GitHub to collaborate. And we ran daily meetings, testing, and open communications across time zones.

Our work didn't end when we finished a solution. Technology was the easy part. It was the transformation that was hard. We used our knowledge of organizational change and human behavior to embed new technologies and approaches into the team's daily work for the long term.

For example, previously, the drivers would rely more heavily on gut feelings when racing. But by responding to both the team's and individuals' goals, we could weave the dashboards and insights from Augmented Race Intelligence into existing strategy discussions between drivers and engineers ahead of each race. And as trust in the solution's findings grew, the team no longer felt the need to test and validate the findings. Engineers and drivers could rely more heavily on the combination of their intuition with our data-led insights when making race-day decisions.