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  • Models Launched 50% Faster at £8.1B Global Auto Distributor

Models Launched 50% Faster at £8.1B Global Auto Distributor


As it scales analytics to 200 business units, Inchcape relies on DataRobot for rapid experimentation and automation across the entire AI lifecycle—expediting value delivery and reducing time to market by more than half, according to the company.

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Inchcape’s newly formed global analytics group needed rapid experimentation to find the highest-value models and deploy them across 200 business units, plus a single platform to manage the entire AI lifecycle.
DataRobot delivers a full AI lifecycle solution enabling fast proofs of concept for experimentation and quick deployment as well as monitoring of models in production.
Inchcape reports that it creates, tests, and deploys a model 50% faster than before. The team says it identifies the best pricing across thousands of SKUs 75% faster than before, all using the Enterprise AI capability offered by DataRobot.
What we find really valuable with DataRobot is the time to value. We can test new ideas to quickly determine the value before we scale across markets. Now, DataRobot helps us deploy an end-to-end solution in a market in half the time we used to do it before and manage the entire AI journey.
Ram Thilak
Ram Thilak

Group Head, Data Science & AI, Inchcape

Data Science: A Profit Center

When you’re tasked with creating a global analytics capacity from scratch, where do you start? At Inchcape, a £8.1B auto distributor, it began with a plan.

Alex Capewell, Director, Global Data and Analytics, and his small team set out to build a centralized analytics group serving more than 40 countries across the globe. The leading independent, multi-brand automotive distributor operates on six continents with brand partners such as Toyota, Mercedes-Benz, and BMW. 

The goal was to zero in on high-value models that the team could scale to support business decisions globally. For that, the group needed to build the tech stack and team, and implement AI.

“We wanted to leverage technology to help us go faster and manage hundreds of models, without accelerating costs,” Capewell said.

Fortunately, the business believed in the potential of analytics to deliver real value.

From day one, we viewed data science not as a cost center but as a profit generator.
Ram Thilak
Ram Thilak

Group Head, Data Science & AI, Inchcape

Nearly 50% Faster from Experimentation to Production

For the core of its tech stack, Inchcape began with a global data warehouse using Microsoft Azure. The company chose Databricks for data preparation and BI calculations, and initially, used Python notebooks for analytics. The entire architecture is based on Lakehouse. For images in particular, they rely on Google Cloud Platform.

Inchcape deployed the DataRobot AI Platform as an essential piece of its scaling strategy. Critically, DataRobot connects via APIs with the company’s technology ecosystem and the legacy systems acquired through occasional acquisitions.

Toward Inchcape’s scalability goals, DataRobot brings automation to the entire AI lifecycle. On the front end, Inchcape notes it now runs proofs of concept in less than half the time, helping them arrive at a viable model or move on to another one faster. Expediting experimentation, the team at DataRobot partnered with Inchcape to create a single model that could be trained, scaled, and repeated for each business.  

“What we find really valuable with DataRobot is the time to value,” Thilak said. “We can test new ideas to quickly determine the value before we scale across markets. Now, DataRobot helps us deploy an end-to-end solution in a market within weeks, as well as manage the entire AI journey.”

Inchcape’s brand partners notice and appreciate that agility.

Where I think we’ve really set ourselves apart is the speed and the scale that we’ve managed to deploy across countries, geographies, OEM partners, and brands. When we share the scale that we’ve deployed with our manufacturers, they’re flabbergasted. Once we have the data, the speed to market of a new solution is something they’re always super impressed by.
Alex Capewell

Director, Global Data and Analytics, Inchcape

Visibility within DataRobot aids in model explainability for the business and partners. The interface easily shows the sensitivity of changes to parameters.

Optimizing Pricing in One-Quarter the Time

As Inchcape has enriched its team, it finds DataRobot empowers data scientists to assess value quickly, deploy models to production, and monitor them—in one place.

The speed to market with DataRobot is super fast and it scales our data science efforts. We can improve both the efficiency and effectiveness of our data scientists.
Alex Capewell

Director, Global Data and Analytics, Inchcape

The analytics team at Inchcape has now grown from five to 230 people globally. With more than 100 models in production, they enhance decisions across the business:

Optimizing pricing—DataRobot helps identify the best pricing across thousands of SKUs for vehicles and spare parts. Previously, Python coding for this took about 16 weeks. Now, it’s down to three to four weeks, the team reports.

“We can get more accurate pricing that helps improve margins,” Capewell said. “It’s probably been our biggest win as a business using DataRobot.”

Churn prediction—The company identifies customers at higher risk of churn and the reasons why, and proactively reaches out. That’s helped drive a double-digit uplift in customers attending out-of-sale workshops, according to Inchcape.

Lead scoring—Inchcape taps into all data about customers to score leads, anticipating the type of vehicle they might buy and their likelihood to buy. “Lead scoring was one of our first use cases,” Capewell said. 

Demand forecasting—Inchcape created a new model to anticipate demand for parts, topping one previously built by a consultant.

“We gave DataRobot to one of the new members on our team,” Capewell said. “Within two or three weeks, he came up with a model that was significantly more accurate. It’s a testament to the power of DataRobot to get models to market faster than ever, with fewer resources and less cost.”

A Partnership for Continued Value Delivery

Next, the team looks forward to taking their analytics practice further with DataRobot’s custom inference metrics. By adding their own KPIs to the metrics provided by DataRobot, they can fully track model performance. When they identify a more accurate challenger model, they can replace it – ensuring they continuously improve model quality.

And they expect generative AI to further improve efficiency and business outcomes, from coding to explainability to visualization. DataRobot uniquely combines predictive and generative AI so that companies can make predictive data more easily consumable, via chat interfaces for example. 

They expect generative AI to further accelerate and extend their efforts. New use cases are expected to drive even greater efficiency and business outcomes, from coding to explainability to visualization. And, since DataRobot’s AI Platform uniquely combines predictive and generative AI, customers can make information from their predictive models more easily consumable to more people with easy chat interfaces. 

Inchcape sees its partnership with DataRobot as integral to its continued value delivery.

“DataRobot’s partnership has been first class and really helps us accelerate,” Capewell said. “We have very ambitious deployment plans and there’s no way we could have gotten where we have without DataRobot on our side.”

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