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Store-Level Insights

For leading FMCG & Retail companies, the ‘Retailer Store-Level Insights’ module of Impaqtr gives a comprehensible and flexible insight into your retailer promotions.

The ‘Retailer Store-Level Insights’ module of Impaqtr gives a comprehensible and flexible insight in the store sales of retailers. Stores are the place where products and shoppers come together. Therefore, it is important to understand what happens in these stores on topics like assortment and field execution. Analyzing retailer store data help to set-up clear objectives for field teams and helps them to prepare their store visits.


 Objectives

  • Understand the differences in performance between stores of a retailer

  • Analyze SKU distribution and follow-up on assortment

  • Optimize field objectives and targets

  • Allow for an effective communication between store managers and sales representatives


Specifications

  • The ‘Retailer Store-Level Insights’ module contains a powerful mathematical algorithm to calculate relevant KPI’s visualized in dashboards and tables.
  • Custom product segmentation can be implemented, so that it is aligned with your other business reports.
  • Store sell-out data are automatically matched with store mappings attributing stores for example to sales representatives, regional clusters, and strategy objectives.
  • All insights can be consulted on a tablet, so that sales representatives can get insights during the field visits
  • The ‘Retailer Store-Level Insights’ module can be accessed through the ‘Aurora’ Platform. Product and Store mappings can be maintained by the customer through our shared One-Drive
     

Insights by Visualisation

  • Classic performance overviews on brands and SKU’s

  • Compare multiple stores on performance.

  • Summarizing tables, …

 

Data Requirements

  • At the sales level weekly store selling-out volumes and prices of your own brands and SKU’s.

  • Store characteristics to incorporate business logic

 

Possible Extensions

  • Link with field information from CRM software

  • Introduce statistical models to understand correlations between parameters

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