Retail marketing teams are given access to a great deal of data about their customers. This particularly applies to retailers who offer loyalty programs: in addition to the personal contact information, these programs allow marketers to track a wealth of data points, such as frequency of store visits, brand preferences, and amount spent per product category. All of this information is augmented by rich third-party data, which marketers use to create nuanced profiles of their shoppers for targeting.
Yet despite this plethora of data, retailers have a significant blind spot in their shoppers’ behavior, which costs them 6% of their revenue each year. Specifically, retailers often lose sight of their customers’ offline behaviors once they exit their doors. This is important, because if they maintained that line of sight, they’d be in a position to understand who they are truly competing against real competitors are, and why they lose their loyal shoppers to those businesses.
Just as I was getting home from work the other day, I remembered my husband and I needed milk for our morning coffee. I stopped at the convenience store next door to my apartment instead of the regular grocery store down the street to pick some up. As you’d imagine, that convenience comes at a cost but higher prices are easily overlooked when the store is so close to home. As a marketer though, this raised some questions. How many consumers behave like me–valuing convenience over price? If the grocery store understood how I behaved outside of its walls, what changes would it be willing to make to entice me away from the convenience store, and toward them?
The more I thought about it, the more I realized retailers were missing critical opportunities to pinpoint exactly where, when and why they were losing their shoppers, and what they could do to stem those losses.
That brings up a difficult challenge: How can retail marketing teams go about shining a light on their blind spots?
The answer may lie in smartphones.
Consumers interact with their phones on a continuous basis, leaving clues both to their likes and dislikes, and patterns of behavior. If harnessed, that data can tell retailer marketing teams when, where and how they’re failing shoppers. More than that, they can use this insight to inform product lines, identify optimum new store locations, and uncover previously unknown competitors and, importantly, how their target audience engages with them.
Let me give you another example. My cousin and her friends like to meet at Bloomingdales but they always come back with H&M bags. The rationale being that while they love looking at everything Bloomingdales has to offer, the clothes are out of their financial reach. They turn to nearby H&M to look for similar styles and the fast fashion neighbor benefits from the foot traffic its high-end competitors generate. Are my cousin and her friends unique? Or are they part of a much larger trend? If they’re the latter, consider how strategic that insight is to retailers. Data like this can be used to better target customers with more relevant messages, or to offer guidance about new store locations.
To obtain a deep understanding of consumer behavior, these brand marketers must have access to mobile data — looking at lat/long, demographic and app usage at scale as consumers go about their daily lives This isn’t an easy feat. Although mobile data is readily available, it’s highly susceptible to fraud. Once the fraud is removed, the data sets are a fraction of the size, and needs to be scaled up again in order to identify meaningful and actionable trends. As an industry, when we surmount the hurdle of fraudulent data and thoroughly understanding consumer thinking and behavior, the potential benefits to both retailers and consumers are endless. Consider that retailers are projected to spend over 15 billion in digital marketing, but how much can they track how their targeted users behave outside of their silos? Not only will this data shed light on consumer behavior, it will empower marketers to target consumers at the times and places they’re most likely to respond.
Consumers will benefit far beyond the usual industry claim of “getting better, more targeted ads.” They’ll benefit from the relevant incentives retailers will dream up to secure their business. Going back to my grocery store example, imagine if its marketing team, realizing how much revenue it loses to convenience stores, created a shopping list app that allowed me to add items by simply sending a text. And imagine if the store let me place an order online, and had it waiting for me at the retail outlet of my choice, the way Amazon shoppers can pick up orders from lockers at Whole Foods locations.
The future of mobile data is is win-win for retailers as they’re capturing lost revenue and gaining a thorough understanding of their total behavior. Even better, consumers will benefit from a renaissance in innovation designed to better meet their needs.
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