Crossing the analytical divide: 20% growth of average cheque and start of loyalty program
A project was undertaken in one of the SPAR European divisions that operates 41 retail stores with a total sales area of approximately 40,000 m2. SPAR is one of the largest franchised retail chains in the world. It includes over 13,000 stores in 48 countries. More than 370,000 employees serve 14 million customers daily.
Project background
Data Process Problems:
  • 1
    Category managers had no information from the tills (from cheques). To analyze and plan the assortment, they uploaded aggregated sales data from the accounting system.
  • 2
    Category managers had no information from the tills (from cheques). To analyze and plan the assortment, they uploaded aggregated sales data from the accounting system.
  • 3
    Pricing did not take into account storage and logistics costs. To take these costs into account, it was necessary to start analyzing sales together with the state of the warehouse.
  • 4
    There was no infrastructure to collect and improve data quality. The company was denied the opportunity to develop an analytical culture, because employees did not really have access to data that made sense to analyze.
Goals and objectives of the project
  • The key goal of the project was to create an infrastructure for the development of the company's analytical function. This infrastructure had to include a system for combining data from different sources and tools for category managers and analysts.

    It was decided to launch a project to download sales data from the accounting system based on Qlik Sense. As a result of the project, the functionality of the analytical system was extended, which laid the foundation for a full-fledged BI platform to solve the company's analytical tasks.
Selected Solution
  • The project consisted of several parallel steps. The first direction was the development and adaptation of the end applications for assortment analysis. The second was to create service applications to combine data from different sources, such as the condition of warehouses in stores and distribution centers.
    The final analytical system consists of 8 analytical applications and approximately 50 auxiliary applications that provide layer-by-layer data transformation.

    The key solution of the project was the analytical application "Products", which contains information on sales, turnover and balances for several years. The application allows to carry out a plan-factual, categorical and cross-functional analysis of key indicators for comparable sale points in the context of like-for-like periods.
As part of the project, an application adapted for mobile devices was created, as well as a data verification system that enabled the automatic loading of information that was changed in the data source, into analytical applications.
The Results
  • Today, around 40 specialists in the division use analytical applications: product category managers, analysts and managers at various levels.

    Business result:
  • Since the start of the project, RevPAM (revenue per square meter) has grown, and the average cheque has increased by 20%
  • The logistics function has been significantly improved by allowing transport and storage costs be taken into account when planning the assortment
  • Customer loyalty program launched and, as a result, a number of loyal customers 6.3% increase
  • A culture of data management has been implemented: the company has established an analytical department; applications are used by more than 40 employees
Sources and tech stack
  • The accounting system, POS management solution, Loyalty program management solution
Functional areas
  • Sales, warehouse