Ted Baker is one of the UK’s leading designer lifestyle brand. One of the ways that retail brands can increase customer engagement is through targeted advertising. However, to do this you first need to identify who your customers are and their spending habits in a process known as customer segmentation.
Problems to Solve
To conduct their investigation, a team of four data scientists were given access to sales and customer data to explore, spanning from 2007 to early 2017. Using machine learning algorithms, the team revealed five key segments in the market: low spenders, infrequent high spenders, frequent high spenders, discount hunters, and unsatisfied customers (characterised by the fact this group only returned items).
Following an analysis of purchase history, the team were able to provide recommendations to increase sales – including a list of customers who are most likely to buy a particular item given their purchase history.
Further insights provided by the team, showed that by combining the sales data they used with web analytics data (for example how many times a customer visited the website before purchasing), revenues could be further boosted. They were also able to utilise association rules mined from customer purchase histories to generate marketing leads and a recommendation engine for products. Finally, the team identified that the value contributed by the frequent high spender group is not growing over time, unlike the other groups. This allows the marketing team to decrease their investment in targeted campaigns towards this group.
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"This is our first investment in data science within marketing and has helped us understand where to get the biggest return on investment from every £1 spent. It is clear that Pivigo has demonstrated the value of data by optimising our search ad spend to convert more new customers."