Calculating key performance indicators are a must in merchandising and product management, but getting source data for these calculations can be tricky.
First of all, analysts and managers at central offices rely on field reps to either count the number of products/faces and gather competitor numbers, or the reps perform calculations such as share of shelf by themselves. In both cases, the risk of human error is high and, over many stores, can seriously skew the final KPIs and negatively impact strategies.
New advances in artificial intelligence have given rise to much-needed image recognition technology for in store audit apps, which allows quantifiable data to be extracted from pictures. With image recognition technology for store audit apps, field reps only need to take one picture of the category shelf and KPIs will be automatically extracted, as well as compiled with data from other stores in the Cloud.
Managers and analysts at central offices can then generate reports and visualizations on the collected data, including out-of-stocks, share of shelf, product availability, competitor availability, and promotions, among others, and then filter stores by any criteria deemed useful for building their strategies.
VisitBasis, creator of the namesake in store audit apps, chosen by many market leaders in the CPG and product distribution industries, has developed its own image recognition software for retail: BrandML. Despite its advanced technology, BrandML is cost-effective and implementing it only takes a few weeks.
Interested in learning more about the BrandML image recognition technology for retail in store audit apps?
