Location-Based Marketers: Are You Missing a Key Ingredient to Your Location Data Strategy?

Location-Based Marketers: Are You Missing a Key Ingredient to Your Location Data Strategy?

by Danny Shayman

Location data intelligence has become one of the most important—if not the most important—data sets for location-based marketers. Understanding user patterns, based on where consumers go, how often they go there, and other signals are key to unlocking important customer trends. But even though location data is already a major intelligence upgrade versus how location-based businesses operated in the past, it’s generally missing a key ingredient that is foundational for marketers.

The bad news: Most of you are missing this enhancement.

The good news: This missing ingredient is generally a fraction of the cost of a location data yet it has the potential to dramatically increase the value of that investment.

With data, granularity matters

The granularity of a data set often determines the limitations of its uses. For example, what if your location data could only go as low as state-level? If you have 20 locations across a state, certainly consumer usage patterns will vary location-by-location. The locations near big cities will most likely have more foot traffic than ones in the rural areas and a location near a major airport will likely be visited by a higher percentage of travelers than most.

In terms of cadence, the data has to be at least down to the monthly level, if not deeper. Year-over-year trends are just not narrow enough. Or what if all of your competitor data was aggregated into a single bucket? You could see that you are losing market share to your rivals, but you wouldn’t know which ones to focus on. Remember, granular data can always be aggregated into higher-level views. City information can be rolled up to the county, state, & country level, months can be rolled up to quarters & years, and so on.

So, yes, with data sets, granularity matters and there’s a granularity issue with most of the location data available today that is holding back its potential.

Location by segment maximizes your location data investment

Many location data intelligence practices focus on location as the most granular level. That’s good, but not good enough. Audience segmentation is truly needed to slice up the data to make it more meaningful—and actionable. After all, just seeing traffic to a particular location doesn’t really tell the full story. Take a bar/restaurant example. It may have many meaningful types of customers such as Work Lunchers, Early Dinner Senior Citizens, After Work Apps & Drinkers, Girls Night Out, Couples Diners, Weekend Families, and so on.

Just seeing one big pipe of what’s going on at a location without it split into key customer groups can be very misleading.

Maybe a location seems to be maintaining average visitation, but under the hood, it could be severely losing one segment even while the other segments are growing just incrementally enough to mask that fact. If the regional marketing manager knew that one of his or her locations was losing the After Work crowd, they could react with drink specials, loyalty member drives from 5-6pm, and boost marketing to reach those people.

With customer segments as the most granular level of location data, marketers can derive meaningful insights, such as how far do certain segments drive to reach them. Which segments are reacting well to marketing messages? Which competitors are gaining an edge on your most important customers? These are the data points that today’s location-based marketers must know to have any chance in protecting and growing market share. Without proper segmentation, a location data investment just isn’t as valuable. It’s valuable, but it needs to be organized at the customer level in order to truly unlock the full power of how location-based data can give marketers the edge they need.

Today’s location-based marketing needs must be driven by AI

Of course, often the first thing marketers do with their location data is to try to build in segmentation so they can start solving for this challenge, but these methods can be slow, rooted in less-impactful indicators such as demographics, or worse, force legacy views of the customers rather than letting the data dictate the segments.

What does this mean—to let the data dictate the segments?

Even an expert human eye would have trouble analyzing today’s giant data sets, but machine learning is great at trend spotting. Finding patterns in large bits of information is what they are built for and they can do it without introducing any bias such as how the business has segmented its customer groups for decades. New innovations in artificial intelligence and machine learning enable marketers to find non-intuitive customer groups within location data that may have never surfaced via manual means. The main reason why AI-powered segmentation is so important is speed. Segmentation is not a “set it and forget it” discipline anymore. That’s just not good enough. The new needs of business intelligence means that it must be done much more often than an annual exercise.

This is even more telling right now during global pandemic. No historical data set could have predicted the way that these months have played out. Location-based marketers rely on location data to provide context to KPIs and there just no year-over-year or quarter-over-quarter data that is anywhere near valid right now.

  • Who are the new customer segments for your location-based business?
  • What’s the market share of your “buy-online-pickup-at-the-store” segment versus your biggest market rival?
  • How has the behavior of your key customer segments changed while your non-essential location has been closed?
  • Are you ready with your marketing strategy to act upon your location data when things begin to open up?

Even though the current health crisis and its impact on the economy is an extreme example of how external factors can change the makeup of your customers at any location-based business, customer behavior is always in flux and there are variables no analyst can connect the dots to at the speed and accuracy of computers.

Your location data + AI segmentation = a customer-centric foundation unique to your business

Of course, location-based marketers are using segmentation. But often it’s being done by various media companies or third-party audience segmentation companies that are trying to fit your location data into their way of doing things. Historically, marketing organizations have spent too much time building to their partners rather than forcing partners to build to them. Location-data segmentation needs to be a core, foundational analysis that centers the entire marketing organization around the customer. Then, those segments are pushed to your various media partners for myriad use cases.

Every partner should be evaluated for how well they are able to drive results based on your unique view of the world and your customer. The state of business is just too important right now to buy segmentation “off the rack”. When it comes to location data, the way you segment your customers will dictate how you go to market. You need a true reflection of what’s really happening at your location-based businesses—and the level of granularity that addresses what is most important to any business: its it’s customers.

AI Enabled Customer Segmentation Will Transform Marketing