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How AI is transforming POS data and retail

How AI is transforming POS data and retail

July 08, 2019

How AI is transforming POS data and retail
In an effort to combat online competitors, retain customers and increase sales, many retailers have adopted new technologies and business models focused on improving the customer experience through meaningful, high-quality engagements. These engagements can come in many forms, from clienteling (where the merchant delivers white-glove treatment by knowing and leveraging customers’ past behaviors and preferences) to in-store coupons. In fact, a Salesforce study revealed that 51% of consumers expect that companies will anticipate their needs and make relevant suggestions. Leveraging data is the key to providing such experiences, but unfortunately this is where many retailers stumble.

Retailers have always had access to data, but collecting it and making sense of it hasn’t always been easy. Today, AI-like features are being baked into a variety of solutions not only to collect data, but then make it actionable. Here are just a few examples.
  • Retailers and grocery stores with their own apps can collect data on customers’ past purchases and then deliver targeted promotions based on shopping history and preferences. Warning: getting too specific with promotions can come across as creepy.
  • Apps and loyalty programs can gather lots of useful data by allowing customers to opt-in to use in-store Bluetooth beacons. For example, merchants can determine the most effective product placement by tracking how customers navigate the store, where they dwell, and for how long. The merchant can also use this information to show value to the brands being displayed, or create a potential revenue stream.
  • In-aisle digital signage can display ads based on shared purchase histories/preferences of opted-in customers nearby.
  • Even a simple loyalty program that doesn’t use beacons or apps can provide value. Retailers can identify common traits of the most valuable shoppers and adjust staffing to ensure those customers have a positive shopping experience. For example, if the targeted customers frequently shop on Tuesday evenings, the merchant might consider having an extra employee on hand to ensure lines are kept short.
  • Guest Wi-Fi solutions can show retailers what customers are searching for when on the guest network. This data can be useful in determining what products a merchant should be selling but currently isn’t. The data can also help a merchant identify its biggest competitors.
  • Part of a positive shopping experience is feeling safe when paying with a credit card. AI is being used by banks and retailers to curb fraud. Aside from traditional payment security like EMV, AI systems can identify unusual buying behaviors such as excessively high tickets, purchases during odd hours or purchases in locations distant from the shopper’s home area.
  • Shopping data is a critical piece of an omnichannel strategy. Whether a shopper is buying online or in-store, collecting data on purchases, abandoned cart items, and browsing history can be used to create targeted and engaging marketing campaigns.
The above examples aren’t science fiction; they’re being implemented today and all signs point to both merchants and their customers expecting such experiences going forward.
Are you taking advantage of this trend? To help your merchants harness the power of AI and data, contact Matthew Hetherington, technical account manager, business operations and transformation, at Ingram Micro.