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Recommendations for returning customers

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New target customer segment identified

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>20% increase in ROI

Client

Ted Baker

Purpose

The marketing team at Ted Baker knew that there were insights to be gained from their online sales data, which they could use in their marketing campaigns. They were interested in the crossover between different marketing channels during a customer’s journey to making a purchase, the profitability of each channel and how these insights can be used to increase their customer engagement/return on marketing investment. In addition to these insights, the company wanted to trialwhat role machine learning could play in their business.

Approach

A team of four data scientists looked for trends and correlations between “first touch” and “last touch” marketing channels (Paid Search, Social Media, Email etc.), identified the most profitable channeland provided insight into which channel could be used more efficiently in the company’s cross-channel marketing strategy.

Both our S2DS teams have showed us that data science is integral part of our business in current times. Their work will make valuable impact and help our marketing efforts going forward. It was a pleasure working with Pivigo and the S2DS team.

Claire Holden, Global CRM Manager, Ted Baker
The Outcome

The team found that customers who made three or more purchases make up to five times more visits to the website on average before buying, compared with new clients. However, the existing clients spend up to 33 per cent more once they do return. Ted Baker can target these customers using an improved recommendation system to reduce their browsing time and increase the frequency of sales. The investigation also showed several channels currently don’t appear to be integrated into Ted Bakers’ cross-channel marketing campaign, so there is room for improvement. Furthermore, the team identified a segment of new customers where there is potential for increased revenue through targeted investment in the affiliates channel.

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