Peak

Eurocell, the UK’s leading manufacturer, distributor, and recycler of UPVC window, door, conservatory, and roofline systems, faced a challenge in harnessing their vast amounts of data to provide customers with an unrivaled customer experience. They aimed to fully personalize every single customer touchpoint, improving their understanding of their customers and how they communicate with them through their digital channels and branch teams. This need intensified during the COVID-19 pandemic as their e-commerce sales rapidly increased. They recognized that artificial intelligence (AI) could empower them to deliver on their strategic goal, but needed help distinguishing between their diverse customer-base in their SAP data, and understanding how their data could be used to create AI models.

Speedy Hire plc, a FTSE-listed equipment rental and support services provider, faced a significant challenge in managing its complex network of 3,500 products across 200 depots in the UK. The company's commitment to excellent customer service meant always having the right products available whenever and wherever customers needed them. This was a complex task due to the variety of products, from simple cut-off saws to lighting towers, and the need to forecast not just one-off sales but also the likely duration of hire and expected product condition on return. Furthermore, Speedy had committed itself to a four-hour delivery and collection promise, adding another layer of complexity to its operations.

Regit, formerly known as Motoring.co.uk, is the UK’s leading online service for drivers. The company faced a significant challenge in predicting which of their 2.5 million users were likely to change their vehicle and when. This information was crucial for the company to serve their customers in a more personalized and targeted way. The company operates by generating leads for companies in the automotive industry by providing a platform for people to book test drives, buy and sell cars, and request brochures, among other services. However, they were unable to predict which of their users were likely to change their vehicle, or even know who had changed their vehicle until after it had happened. This lack of predictive capability was a significant barrier to increasing their call center revenues.