What are the methodologies to collect KPI data by a field force at retail execution? This is the question which each CPG company should decide for their retail execution processes.Many companies are already doing it for their products available at online shopping platforms. But it is easy, they just stream the platform data to their reporting infrastructure and create any report based on their desired KPIs because the data iS already in digital form. But for the retail execution throughout the physical outlets, the first issue is to transform the analog physical world into the digital data. There are ways of doing it and the companies shall choose one out of 4 different methodologies for their Retail Execution. And the main differences are:1) Manual collection: Field force simply writes down the shelf metrics on a page and later at the office they are typed by operators into a database (or excel).
Swiftness: KPI results are available hours later. Impossible to take instant actions during the visit according to the real time KPI results
Accuracy: Open to human mistakes in 2 layers both at writing and typing stages, average accuracy is less than 60%
Cost: Consumes an enormous time of field force and the operators, the cost is the highest of all.
2) Manual collection using a sales force application: Field force simply fills the required fields or chooses from the multiple choices for the specific outlet at the application screen.Swiftness: KPI results are available instantly. Possible to take instant actions during the visit according to the real time KPI results.Accuracy: Open to human mistakes, average accuracy is around 70%Cost: Consumes a big time of field force with an average of 1 hour for each visit.3) Automatic collection with "Image Recognition-IR" solution supported by operators in the shadows: Field force simply takes photos of the physical shelves but the IR engine is not strong enough to reach to the desired accuracy, so the provider uses operators in shadows to increase the accuracy to 95% by correcting each photo manually.Swiftness: KPI results are available hours later because of the hidden operators of the provider. Impossible to take instant actions during the visit according to the real time KPI results, should revisit the same outlet in the future again to take an action.Accuracy: 95%Cost: Consumes a big time of provider’s operators, thus the IR solution will be expensive for all traffic values.4) Fully automatic data collection with "Image Recognition-IR" solution: Field force simply takes photos of the physical shelves. as the IR engine is fully AI based. the accuracy successfully reaches 95% without any operators in the shadows.Swiftness: KPI results are available instantly. Possible to take instant actions during thevisit according to the real time KPI results.Accuracy: minimum 95% at the beginning. Increases with the time, as the engine gets more experience by unsupervised machine learning during the usage.Cost: Consumes negligible time of both the field force and the IR provider. Provides huge labor cost savings for both sides thus also the IR solution will be inexpensive, with a decreasing rate according to traffic values.Ailet had chosen the 4th methodology as core business model and for the last 6 years implemented many successful projects together with the world’s leading CPG companies.