Thriving Through the Retail Boomerang With Mobile Data

graphic depicting mobile data

Over the last two years, U.S. retail sales have been volatile. Economic uncertainty, inflation, looming COVID lockdowns, and supply chain delays have resulted in a string of months ending with both high and low retail sales. 

In January of this year, the U.S. Commerce Department reported a nearly 4% increase in retail sales, giving retailers hope that 2022 may be the year where shoppers go back to the brick-and-mortar.

With hopes high, retailers are looking closely at how to capture consumers with personalized and relevant marketing to drive them into their stores and capitalize on wallet share – and they’re all turning to their data to provide the level of insight they need to secure their footing. 

While data has long been at the core of strategic planning, so too have the mounting challenges of business analytics that lie with the data itself. In fact, a recent Data Integrity Trends report by Corinium Intelligence found that data and analytics professionals spend an average of 40% of their time on data cleansing, integration, and preparation. At some companies, that number reaches as high as 80%.

[Read more: 32nd Annual Retail Technology Study]

The volume and variety of available information is staggering, and retailers must first organize and align disparate datasets from internal sources just to glean initial insight into the data. Throw in the simultaneous challenge of ensuring the data maintains its quality and integrity throughout the process, and now you have a data challenge so large that even some of the most sophisticated internal systems aren’t equipped to handle it. 

Where retailers often miss the mark is in identifying the data that matters most – because the truth is, not all data delivers equal value. While a holistic view of data is important, it’s really the context of that data that leads to understanding consumer behaviors. 

Since the boom of smartphones and mobile devices, human movement data has single-handedly become one of the greatest indicators of consumer behavior. This movement data goes beyond transactions and provides insight into behaviors and patterns that transactional data simply cannot.

Let me give an example – say you’re a small business owner with a storefront in a local shopping center. You specialize in men’s clothing with a mix of business and athleisure wear. Typically, you see the most foot traffic over the weekend, but recently you’ve noticed the parking lot at the shopping center filling up every lunch hour, Monday through Friday. Despite business picking up at the shopping center, you’re not seeing your own sales increase. 

In this example, mobility data could play a crucial role in determining why foot traffic isn’t increasing. 

With access to mobility data, you could understand how much traffic is coming to the shopping center, and where those visitors are coming from. How far on average do they travel to get to you? Do they come from work or home? Digging deeper, you could analyze where else visitors to your specific store shop: are they more likely to visit luxury or discount stores; are they hitting the gym often? 

Backed by these insights, you’re empowered to create an efficient campaign targeting mobile devices within 8 miles to attract your lunch crowd, delivering messaging about your new bespoke line to luxury shoppers and offering loyalty rewards and sales to discount shoppers. Campaign measurement enabled with mobile IDs will even tell you if your ad was not just seen – but actually drove traffic to the store. 

This is just one example of how mobility data is changing the way retailers do business. But there are dozens of use cases in which retailers are taking advantage of mobility data to gain insights into demographic, income, and lifestyle demographics to more accurately market products and services; or refine site selection for future brick-and-mortars; or to tailor digital display advertising for specific populations at any given time.

The retail boomerang is here, and retailers must look to new ways of gaining insights and context as part of their data integrity plan.  The data is available and those that choose to dig in and search for new information will be best equipped to thrive in and adapt to economic volatility.

 —Dan Adams, SVP of Data and Operations at Precisely

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