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    Extending the profit potential beyond self-search

    Part 3 of a 3-part article, in collaboration with Zebra Technologies

    Retailers who have invested in the capabilities required to enable self-search such as lighting-based indoor positioning, digital maps, and product locations can leverage these capabilities for other purposes that will drive further growth and efficiencies.

    1. In-store order picking


    Tests performed by Signify with MediaMarktSaturn in Germany confirmed that in-store order-picking times can be reduced by 50% when pickers have access to store navigation. This drives significant cost savings for retailers who have chosen to fulfill their online orders from the local store network. It also supports the trend that retailers team up with food delivery companies like Gorrilas, Deliveroo and Just Eat Takeaway to offer 30-minute grocery delivery, picked in the local stores.


    2. Location-based marketing


    Indoor positioning systems deployed to support self-search can also be used by retailers to power location-based marketing. Retailers already offer shoppers in-app coupons, but by the time they pass the pasta section, how likely is it that they will remember the coupon for pasta sauce waiting for them in the app? Not very likely. But if instead they receive that promotion as they pass the pasta sauce aisle, would they be more likely to grab a jar? Probably!


    The combination with self-scanning makes location-based marketing even more powerful. Think about it: a shopper scans the pasta, and now the app sends them the coupon for the pasta sauce as they pass that aisle. A great opportunity to offer the retailer’s private-label product, or alternatively to monetize this high conversion opportunity with CPG brands.


    3. Shrinkage prevention


    Fear of increased shrinkage due to self-scanning errors or deliberate theft has kept retailers away from offering self-scanning in their stores. It doesn’t need to be that way, as fraud detection algorithms have become more advanced and can alert store associates to perform checks and rescans.


    Feeding these algorithms with location data will make them even more effective. Comparing location data captured during the shopping trip against scanned products can be used to identify anomalies that might indicate a higher chance of intended or unintended scanning mistakes. As an example, think of shoppers with long dwell times in high-risk areas with expensive items who have not checked out any of those items. Location data can also surface erratic shopping trips in which shoppers take illogical routes or scan products far from their shelf positions.


    4. Product location maintenance


    In the previous section of this report, we concluded that self-search requires that both shoppers and products are accurately located, and to do so retailers should keep their realograms and planograms in sync. Location data can come to the rescue here. If both self-search and self-scanning are deployed, retailers can check the location of product scans to highlight realogram misalignments. In this way, every product scan, whether performed by shoppers or store associates, contributes to the continuous and real-time maintenance of the planogram and realogram alignment.




    This is the third in a series of three articles on the profit potential of self-search in big box retail. For the full read, download our whitepaper, “The profit potential of self-search in big box retail.“


    Learn more about how Signify can help you to implement self-search in a scalable and cost-effective way.

    About the authors

    Gerben van der Lugt, Global Lead, Indoor Location Services, Signify
    Gerben van der Lugt is Global Lead, Indoor Location Services at Signify
    Mark Thomson, Director of Retail & Hospitality Strategy EMEA, Zebra Technologies
    Mark Thomson is Director of Retail & Hospitality Strategy EMEA at Zebra Technologies

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