Using customer data to bridge the gap between online and offline
Nike on Melrose
Retail giant Nike has launched a retail-of-the-future concept store in West Hollywood, that aims to lure the digital shopper with personalized services and experiences.
This store is meant to take everything we know about our customers digitally and, with the new NikePlus app, use that to elevate their shopping experience
– Cathy Sparks, VP & GM of Nike Direct Stores
To select the location of the store Nike mined their customer data to find the perfect customer in the crosshairs of loyal, stylish and vocal enough to tell them what they want. The result, the Nike Live Melrose concept store – serving Los Angeles NikePLus loyalists.
Customers access tailored lines
According to Nike, on top of providing a selection of nike.com best-sellers and essential running, training and sportswear product, Nike by Melrose will offer city-specific styles — all of which is determined by Nike digital commerce data – things like buying patterns, app usage and engagement – to serve local NikePlus members exactly what they want, when they want it.
In this video from the LA Times, Sean Madden, Senior Director of Product at Nike, describes the unique features of the store accessible only by app users.
Nike demonstrates how the overlap in online and offline experience can be cleverly activated to better service a high-value customer base – in this case, the Melrose ZIP code. By mining their customer data they were able to maximize loyalty with existing customers (increasing engagement by 48%), whilst capitalizing on finding new lookalike customers in the area.
Keeping the transaction activity online, via the app, provides a closed-loop with all in-store purchase data coming back to a single customer’s profile.
The true data rockstar element is how customer data is used to stock the store in real-time. For example, if yellow products become a trend on the app with that geographical customer segment, Nike will put more yellow products into the store. The ability to anticipate trends and forecast is genius. Now to see if this approach can be scaled!
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