curated data

Retail Recovery Will Require a Revolution in Curated Data

As retailers seek to jump-start buying behaviors and clear out inventory that’s been languishing in warehouses for months, they must turn their attention to curated data sets that can deliver the differentiated audiences they need.

As retailers seek to jump-start buying behaviors and clear out inventory that’s been languishing in warehouses for months, they must turn their attention to curated data sets that can deliver the differentiated audiences they need. These audiences will become all the more important as retailers recognize that, even as stores reopen their doors, shopping behaviors aren’t going to return to the previous “normal” any time soon. With that in mind, let’s look at an improved approach to data that can help retailers significantly expand their customer bases in a challenging economic climate.

Liddy Manson

The retail industry has ridden its share of economic waves over time, but no sudden downturn has unleashed quite the level of uncertainty — and urgent need for adaptation — as the current COVID-19 pandemic. The ongoing crisis has laid bare the vulnerabilities of many brands’ current data strategies and highlighted the need for a significant pivot to guide recovery efforts.

New Challenges Require New Solutions

The retail industry is in the midst of a crisis that no one imagined possible at the start of the year. Following the initial wave of business lockdowns during the pandemic and subsequent reopening efforts, retailers are faced with not only a need to re-engage consumers in this “new reality,” but also to contend with a serious inventory backlog. In seasonal industries such as apparel, the challenge is formidable.

Many retailers have struggled to get a handle on their inventories in a way that enables them to deliver on new expectations for curbside pickup. Product returns represent an even more significant challenge. Ultimately, the “new normal” we’re entering is going to make the concept of one-to-one inventory planning — i.e., the ability to deliver what customers need, where they need it, on the first try — is going to become all the more vital.

As with every significant economic upset, this crisis exposes opportunities to improve on previous approaches and refine for much-needed efficiencies going forward. Just as the 2008 recession gave way to new programmatic buying methodologies for retailers, this downturn will necessarily elevate more sophisticated approaches to buying and using audience data for the sake of retaining, acquiring and serving retail customers. These approaches aren’t new, by any means. However, given the boom times many brands had been enjoying pre-pandemic, they’ve been dramatically underemployed in retail in recent years, while other industries have been using them to great effect.

One of the main challenges facing the retail industry right now — and the one that an enhanced approach can correct — is that brands have been purchasing and marketing to the same undifferentiated sets of audience data. That means that Brand A and Brand B — not to mention Brands C, D, E, F and others — have been targeting the same individuals with the same broad-stroke approaches. The same group of consumers, each with personal tastes and preferences, as well as finite budgets, have been repeatedly hammered with messaging representing a wide array of categorically similar offers, with little regard for their personal customer journeys or inclinations.

We can do better. By seeking out alternative data sets — differentiated audiences profiled according to personal tastes and buying preferences — retailers can dramatically reduce waste within their media spends and increase the odds of connecting with the valuable new prospects likely to see worth in their particular offerings in a given moment.

A More Personalized Future for Retail

The tactics retailers used in the past must evolve to accommodate the challenges of the future. In that regard, the brands that adjust quickly to the new normal will be the ones that emerge strongest on the other side of the pandemic. Fortunately, there are multiple examples within other industries of precisely the type of data-driven, taste-based audience connections likely to resonate with retail customers going forward.

Look no further than entertainment brands Spotify and Netflix to see the power of properly applied taste profiles. Both services differentiated themselves early in the ultra-competitive, previously commoditized music and entertainment spaces, and they did so by understanding the types of music and movies that resonated with people on an individual level. In short, they gave people exactly what they wanted by leveraging unique data, and that’s precisely the approach that can help retail brands set themselves apart in the post-COVID landscape.

In recent years, too many retailers have been shouting at the same group of people at the same decibel level. It’s time for a more refined approach, one in which retailers put curated audience data to work for their brands. In doing so, they’ll recognize not only reduced waste and find greater efficiencies, but they’ll also establish more authentic connections with the prospects most likely to become loyal customers over the long haul.

Liddy Manson is COO at retail data company PreciseTarget. PreciseTarget is a data science company helping retailers, brands, and wholesalers acquire, engage, or reactivate customers.

This article originally appeared in TotalRetail. Photo by H.F.E & CO on Unsplash.

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