Even with high levels of inflation and a recession predicted to bear down on us in 2023, consumers are eyeing this holiday season with eagerness — they’re ready and willing to splurge on holiday spending this year, according to a recent McKinsey report.
Since this might be the last good time for retailers to cash in before the economy pulls back (and consumer spending with it), many began their holiday advertising earlier than ever before — even prior to Halloween in some cases. There’s something to be said for widening that window for the most profitable time of the year, but the “Christmas Creep” can also be grating on consumers who feel like they aren’t yet ready for the barrage of ads and holiday music in late October or November.
Some adjustments retailers are making to boost revenues this season aren’t as visible to a consumer's eye, however. Behind-the-scenes technology advances have helped many retailers keep money flowing in and have helped them adapt to the challenges of a holiday shopping season complicated by lingering labor market and pandemic effects.
Artificial intelligence (AI) has made a major impact in the retail space in recent years, and it can help improve the shopper experience without much discernable disruption. There are a number of reasons AI- and data-backed initiatives will be among the biggest factors in how successful this year’s holiday season will be for retailers that have enabled them.
During high-volume, high-profit windows like these, accuracy and speed on the ordering front are essential to maximize sales. This year’s increased holiday spending is surprising to many, so it might be too late for some retailers to adjust their supply chain accordingly. With AI plugged into ordering systems, however, demand beginning to pick up would be noticed early and orders adjusted automatically to account for the increase. Leaving potential sales on the table because supply can’t catch up to demand — even though it could have been ordered in time — is a gigantic missed opportunity.
Furthermore, camera systems that are plugged into inventory management systems and use AI can identify when an item is low on stock and needs to be brought out from the back. Giving floor workers and managers visibility at all times into what's flying off the shelves and needs a quick re-supply will help keep aisles stocked and customers happy. Similarly, if an item is low on the shelf but there's no more in the back inventory, AI can automate the ordering process to make sure putting in a needed order isn’t overlooked.
These ordering and stocking efficiencies can have a more significant impact than simply some saved time. With the labor market still extremely tight, help is hard to come by. AI’s automation features that let employees work smarter and get more done for the same amount of effort are extremely valuable. AI-powered systems linking inventory and shelf-level cameras will provide clear pictures of what needs to be restocked, when, and what needs to be reordered — without a physical survey of shelf space and backroom inventory. Reducing unnecessary or wasted trips when restocking — e.g., grabbing the wrong item or the wrong amount — makes employees more efficient and keeps productivity high even with less staff than was needed in prior years. This is crucial for growth-minded retailers that are still feeling the hiring crunch of recent years.
By improving the technology foundation that helps stores stay up to speed with the fast pace of holiday shoppers, retailers can take a bigger slice of the seasonal spending pie this year. As consumers excitedly splurge once more, no retailer needs to be left out in the cold.
Francois Chaubard is the CEO and founder of Focal Systems, a platform that helps retailers cut shelf scanning costs in half by switching from manual to automated shelf scans.
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Francois Chaubard is the CEO of Focal Systems. He was an EE masters then CS masters from Stanford University. He researched Deep Learning and Computer Vision under Fei Fei Li in the Computer Vision and Geometry Lab. He was a Deep Learning Researcher at Apple working on secret projects. Before that, he was a Missile Guidance Algorithm Engineer at Lockheed Martin working on Kalman filter / Information theory. He heads up Deep Learning Research, Sales, and Strategy.