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Grocers: It’s Time to Disrupt Your ‘Traditional’ Predictive Analytics Approaches | Progressive Grocer

The AI approach is based on processing all the available data and evaluating the connections and relationships between every single product, which sheds light on the various “ripple” effects – such as cross-category cannibalization/affinity, promotional and price elasticities, and forward-buying – that occur across the entire retailer’s product assortment anytime a decision is made.

Now apply this same holistic measurement and simulation to pricing decisions. AI can play out an astronomical number of trade-offs and scenarios of promotional and regular pricing to deliver results: decisions are made – not on a margin per SKU basis, but on an organizational blended margin rate to deliver expected long-term profits.

“Traditional business processes like SKU-store rollups have traditionally not been calculated regularly due to legacy hardware constraints,” Thorsen states. “Now retailers with large assortments and many stores can accurately perform these calculations to understand product demand, promotion plans and replenishment schedules.”

For the high SKU counts and high transaction frequencies in the grocery business, AI is a significantly more powerful approach than traditional predictive analytics.

The insights that AI provides support significant topline sales growth without increased promotional cost. In the high-volume, low-margin supermarket industry, AI is a technology that will prove to be as impactful as the internet – while those grocers that succumb to the “sunk cost” fallacy and continue to prioritize investing in legacy predictive analytics tools over AI face nothing short of existential risk.

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Grocers: It’s Time to Disrupt Your ‘Traditional’ Predictive Analytics Approaches | Progressive Grocer.

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