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Field force teams often perform multiple store activities every visit, which results in a substantial amount of time spent on gathering high volumes of data. CPG solutions like process automation tools enable the sales rep to be more organized, consistent, and effective in planning field activities. However, data collection processes remain a highly time-consuming task. Delivering the proper, and valuable information from a store to headquarters very often engages a field person for most of their time spent in the store. Much of this time can be saved using image recognition technology to improve retail execution.

To adapt to the modern shoppers’ challenges, logistics and store performance, Consumer Packaged Goods (CPGs) companies try to Know More to execute better, which means driving decisions at their headquarters. This leads to the pressure to optimize in-store activities and deliver actionable insights to drive sales.

Is it the sales rep who should spend his/her valuable in-store time to collect and provide data? Should companies transition their field team into market research units?

Modern technology, such as Machine Learning and Neural Networks (NN) supporting Image Recognition Technology might be the answer and help.

From StayinFront’s experience, we see, still, the most common method of collecting field data is recording the store performance manually in the SFA software. The scope is more or less extensive, but the time allocated to collect data constitutes a substantial part of the in-store visit.

Furthermore, our internal research proved that the average accuracy of the manual shelf audit remains within the range of 45-65%. The aforementioned range was established for Must Stock Lists (MSL) audits from different markets: LATAM, EU, and Asia Pacific.

How can StayinFront Digital improve the quality and completeness of in-store data?

The picture can substitute thousands of words – knowing that we decided to incorporate modern science and technology to deliver a wide range of information from a single set of images, thanks to NN and Machine Learning technology. Pictures of the shelves captured with a mobile device, no matter how long the product category is, are stitched to present the whole category. The combined full shelf view is analyzed, and individual SKUs are recognized and populated automatically to deliver custom-crafted reports. This way, StayinFront Digital can provide actionable data not only to the sales teams in the field, but also to several departments seated in the CPG’s headquarter office.

By limiting the in-store tasks to picture taking activities, CPGs can obtain more consistent, reliable data with the average accuracy audit results reaching up to 96-97%. Dedicated reporting panels may reprocess the raw data to deliver actionable insights to those who probably have never visited the store. Image Recognition by StayinFront reveals the accuracy gap related to the store performance reporting and supports CPG in Knowing More about their retail performance.

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