For Carrefour, we developed both a mobile application and a Drupal website for the back end part, allowing them to create a multitude of surveys destined to staff members and customers and to conduct an in-depth analysis of the results.
A staff-oriented application
Made for the use of Carrefour staff, the mobile application was distributed privately via a QR code and all the users we pre-registered by the administrators. As the different regions and stores were not likely to have the same needs in terms of surveys, we enabled the administrator to create pools of users. Each pool having access to different surveys.
After login in the app, users are able to choose between the different surveys available for their pools, download a survey and then start managing the questionnaire. Once the sufficient number of answers were collected, the users were then able to send the results to the back office to analyze them.
The survey generator
The Drupal back office website we developed include a survey generator allowing to create surveys with various types of questions: multiple choice questions; single questions; photos; grids; matrices, weighted questions; pictures, and more. This comforted our client that the tool could be use on the long-term.
A powerful analytic tool
Although we are proud of the application and the survey generator we developed, we consider the main strength of this platform to be the analytic tool we developed to gather and analyze the surveys' results.
The platform gathers information by conditional filters, engendering a first simple segmenting of results. The tool also avoids duplicated answers, thereby avoiding miscalculations and false data. The analytical power can be seen through the tool's extensive data analysis. The data analysis provides the opportunity to look closely at the gathered data through inner-correlated analysis. This is when the relationship between groups of data is picked apart and shown.
During the first trials, the tool was deployed in 72 Carrefour Stores in China, collecting more than 32k piece of data that were analyzed in the back office.
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