APPROACH

The analytics experts at Tredence proposed to solve this problem in a two-step process.

  • We identified factors that drive Online Grocery pickup orders
  • Used the drivers to select the potential stores for the launch of Online Grocery

Mixed effects modelling technique was implemented to identify key performance drivers. Various factors around store attributes, market characteristics, customer properties, pre and post purchase experience were considered. These drivers were then used as input in to the Generalized Linear Model (GLM) technique to identify potential stores

Machine Learning algorithms were used to overcome the model instability caused due to lower data points.

The algorithms were tested and validated recursively on random samples created from the small pool of data. The final algorithm was used to score the potential stores in different markets. Sensitivity analysis was performed to account for individual factors.

KEY BENEFITS

  • The solution provided recommendations on product bundling and upselling, leading to greater engagement and purchase from the e-retail
  • It allowed quick and accurate responses to complaints

RESULTS

The client was able to:

  • Achieve an incremental revenue of $60MM by launching Online Grocery pickup service in predicted potential stores
  • Identify drivers affecting online grocery pickup orders such as market share, online grocery awareness, etc.
  • Improve accuracy over multiple iterations using various Machine Learning techniques