Leveraging data to make strategic decisions for growth opportunities: segmentation + store revenue model design


Marionnaud France



Marionnaud France had been losing marketing share as footfall into the store declined and the basket size per customers shrunk.  However,  it had been collecting Loyalty card data from 3.5million active customers for a period of time but had not been leveraging its full potential. 

With the help of a data agency, 9 distinct customer segments were uncovered.  They have different product needs, purchase patterns and basket size.  I came onboard to leverage these insights to develop a growth opportunity for the business.



Working with the data from the agency and the Marionnaud Finance and Retail Teams,  I was able to analyse and triangulate the customer data, store data and their sales performance to segment the 560 stores into 5 distinct store clusters. 


I facilitated a growth strategy session with the senior executive team to present the analysis and together, the team came up with a list of Growth Programmes, the relevant project champions, and set two company-wide KPIs: Store Traffic and Average Transactional Value per customer. 


Then I set up a series of workshops with the Project Champions with their team to deep-dive into each of the store clusters, then identify and map-out the growth activities and the experiment roadmap to drive the two KPIs.

The following Growth Programmes came out of the workshops:


  • The store segmentation programme 

  • Product range review process to augment the store segmentation programme

  • Focus on the skincare category to grow the category and high-value clients

  • New skincare product development programme

  • Loss Making Store programme



The retail team piloted a store in Provence based on the Store Segmentation programme with a revised product range to attract more of its local customers and ad hoc footfall.  This resulted in a 10% increase in traffic to stores and a 5% increase in average transactional value in the pilot store.  


The programme was later replicated across the AS Watson retail brands. 



Data analysis, prioritisation framework, customer value proposition and experience mapping, growth experiments design