The Need

  • Retail client wanted to understand the make up of their customer base – the different needs, preferences, shopping & promotional behaviours that existed and how these affected value, cost and risk. A small number of top level groups were needed to allow a coherent marketing strategy to be developed, but SKU level insight was also crucial.

The Solution

  • Item-level sales data was extracted, aggregated to customer level and analysed to produce ‘factors’ relating to groups of products bought together or as substitutes for each other
  • Groups of customers with similar shopping habits, including purchase profiles across these factors, were then identified to form cluster segments
  • Similar sements were then combined to reveal the natural hierarchy in the customer base, leading to identification of a small number of key customer types, in terms of what they bought, how & when, including promotional activity

The Result

  • The nature, size, preferences, location & value of each segment were used to drive marketing and promotional initiatives
  • This resulted in a 5% increase in sales and a 10% uplift in marketing ROI
  • Stores were analysed in terms of the mix of customer segments they served, driving local stocking and marketing initatives
  • Segment dynamics were also used to forecast how the base was changing, emerging trends, LTVs and to identify churn and acquisition hotpots

Other Success Stories

  • A Storage Firm redesigned their business model to act on the insight gained from a customer usage segmentation, leading to sustained growth from better serving their core clients needs
  • A Travel Firm used the insight gained from a segmentation of holiday purchase behaviour to refocus their business strategy to track changing market requirements
  • A DIY chain identified loss making activities from a detailed segmentation of promotion, purchase and delivery behaviours.

For more information read our blog page “Effective Strategy from Data-Driven Business Insight” or contact us.