To Know Your Data is to Know Your Customer

people-escalators_730x280.jpgWell known retail editorial Retail Dive published an article the other day exploring the idea of better understanding customers through data. That is, being able to understand what customers shopping at stores prefer. They referred to this as “hyper-localization”, which can easily be misconstrued as *producing* goods locally, apparently. Instead it actually refers to retailers modifying the in-store assortments based on what customers in those areas truly want.

The concept of “hyper-localization” is further defined in the article as:

“[Hyper-localization] is from the idea that increasingly consumers now are searching with intent. They’re using their mobile devices to search for businesses, for services, for products at the point of inflection, while they’re on the go and while they’re in the marketplace, so there’s a lot of intent in their searches.”

This is according to retail expert Doug Stephens.

He goes on to say, "Consumers are starting to revolt against the whole chain store mentality. But the concept of hyper-localization is really a backlash against mass marketing, where we’re hit with the same marketing funnel through broadcast media and mass media."

He hit the nail on the head, and we at Celect are glad to see this level of acknowledgment. 

Use the Data for Better Assortments

The whole point of analytics in retail is to better understand the needs (and preferences) of your shoppers. In the case of predictive analytics for retail, it’s to accurately anticipate their needs in the form of an optimized product assortment. Part of this is looking at the stockpile of data that most retailers are sitting on. Doing this completely changes things like clustering for example. Traditionally, clustering served as a means for retailers group similar stores together based on various identifiers such as demographics, climates, or incomes. This was an attempt to standardize the product assortment in stores based on factors that actually have little impact on individual preference.

The article also states a subtle but important point.

"Analytics is key because, done right, it provides a large national retailer with information that becomes meaningful in new ways. It's not just about stocking more locally-produced goods, but more locally desired goods.”

It’s not about what’s produced locally, but what is DESIRED or PREFERRED locally. Think of it as the opposite of the traditional clustering models based on demographics, income, weather, etc., which completely ignore preference.

With this data, from both in-store and online sources, retailers are able to outperform their competition, which is getting more difficult every year through traditional efforts. Predictive analytics is helping reduce extreme markdowns, boost inventory turns, lower inventory spend, and enhance overall customer experience.

predictive analytics whitepaper  

Topics: assortment optimization, product assortment, localization, preferences

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