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A Look at Hyper-personalization: Part 2


Editor's Note: This is the second of a two-part series on hyper-personalization in the retail food industry.

As I wrote in my previous column, we looked at the personalization standard Kroger has set for the grocery industry over the past decade. At the end of the column, we introduced the “segment-of-one,” which leads us to hyper-personalization.

Most retailers with loyalty programs capture shopper-identified transaction data at the household level. The enrolling shopper is given several cards or key tags with the same identifier and uses a phone number at checkout that is linked to the household. However, the use of smartphones and mobile devices today dictates that marketing must move to the individual shopper level, necessitating systems that are able to track individual shopper purchases and aggregate purchases from multiple household members.

Solution providers often calculate category affinity scores, brand loyalty scores and discount propensity scores using a household’s total and category level aggregated purchase data. For example, a discount propensity score is created for the Smith household using the total value of discounts received as a percentage of total purchases. That score is then indexed against scores of all other households, enabling the marketer to place the Smith household in the "discount driven" or "brand loyal" segment.

Contrast this with hyper-personalization. Each product purchased by each individual shopper in each shopping trip is used to calculate a brand loyalty score and discount propensity score. This requires considerable processing power and represents an immense amount of data that must be stored at the brand, product and category level but provides an exponential increase in shopper intelligence as each shopper is viewed as a true segment-of-one.

In addition, the frequency of purchase for each product by each individual shopper is also tracked and used to understand the most effective timing of promotions to the shopper. By gaining access to the shopper’s digital shopping list created through the retailer’s website or mobile app, hyper-personalization is able to incorporate the shopper’s intent.

Hyper-location Meets Hyper-personalization

If ever there were a match made in heaven, it is the union of hyper-location and hyper-personalization. There are a growing number of technologies — beacons are currently the most prevalent — that support in-store marketing triggered by real-time shopper location. But contextual relevancy is not enough.

Conveying an Oreo offer to shoppers near the cookie category may be relevant to some shoppers, but not all. If a particular shopper has never purchased Oreos or has a gluten allergy, the Oreo offer is, at best, irrelevant or, at worst, seen as spam.

Used properly, location data can further enrich hyper-personalization, providing the marketer with knowledge of the usual timing of shopping trips, typical path through the store, and more. This information can influence the communications sent to the shopper while in-store. Location technologies inform the marketer about the individual shopper which, when combined with trip type, can inform which promotions are presented in-store: promotions meaningful to the specific individual and/or household.

Technology-fueled innovation is transforming retail marketing. Big data, combined with machine learning and artificial intelligence, is creating a new model of hyper-personalization and is enabling marketers to work at a true “segment-of-one” level for the first time. Hyper-personalization promises dramatic improvements in the effectiveness of strategic promotion targeting over yesterday’s shopper segment-based approach.

Not only is hyper-personalization more effective, it is being delivered as a cloud-based capability, making it available to even smaller retailers because of its cost efficiency. Just as the Kroger-Dunnhumby partnership transformed retail marketing, the consultancy-based approach is itself being disrupted by hyper-personalization delivered via the cloud.

This development is a prime example of the democratization of technology — advanced capabilities being made available to retailers of all sizes — and showcases the opportunity retailers have today to leapfrog their largest competitors who are saddled with outdated infrastructure and processes.

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