One of the leading health and personal care brands had planned a promotional campaign offering bonus packs at a promotional price in the face of some intense competition from its rival brand. Their customer (Team Leader for the brand at a retail chain) had worked with a major retailer to put up pallet displays with eye catching graphic skirtsacross all its stores nationwide. Their customer has often seen poor sales lift across 30-40% stores for such campaigns in the past. Snap2Insight was tasked to verify quality of execution nationwide and identify stores with incomplete execution or no execution. Leveraging their partnership with a leading crowdsourcing vendor, they quickly gathered photos of displays from every single store and using their patent pending Deep Learning technology, they analysed them to recognise SKUs stocked, present of graphic skirt, promotional price and type of display put up. Using execution scores they provided for each store covered under this promotional campaign, their customer worked with their retail coverage partner (merchandiser) to prioritise and fix all stores with low execution score and high potential for sales lift, thereby improving sales and achieving break over for over 90% of stores.

Internet

Snap2Insight

Praveen Gopalakrishnan

Renish Pynadath

Melwin Hansie Patrick

Gautam Malu

Benny Breazeale

Anoop Ramankandath

The Computer Vision Team at Snap2Insight leverages advanced deep learning technologies to analyze visual data collected from retail stores. By processing images of promotional displays, they assess execution quality, identifying gaps and discrepancies in SKU presence and promotional graphics. Their insights enable the business to target underperforming stores efficiently, driving improvements in promotional execution and ultimately enhancing sales performance for their clients.

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Computer Vision Team

About

Team members

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Computer Vision Team

About

Team members

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