We built a propensity to buy model that identified and prioritised who in Neptune’s customer base was most likely to make a new purchase.


Neptune, a premium home retailer, wanted to increase the conversion rate of their site visitors by understanding the root causes that motivate a customer’s next purchase. They wanted to improve their overall customer experience and understand the optimal customer journey.


  • Advised clients as to which customer touchpoints are most effective for onsite conversion
  • We developed a bespoke algorithm to harmonise and dedupe the dataset
  • Merged customer and transactional data to determine the correlation between different products purchased
  • Designed a random forest model to identify which key customers characteristics correlate to repeat purchase(s)


Spotted and cleaned over 50,000 duplicates in the customer base (35% of the total)

Advised client as to which points and patterns of contact are most effective

Built a product correlation matrix to display in a visual way how well different products sell together

Identified several variables most predictive of ‘second purchase’ customer behaviours

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