What would you do if you knew where your customers come from, where they go after they leave your store, or when your best customers tend to visit your location? You might say your loyalty program already tells you some of the information, but remember that program is only effective if your customers make a purchase.
It is one thing to know what people are buying, it’s another to know what types of customers visit your store and leave empty handed or, worse, beeline for a competitor. While loyalty programs can’t answer these questions, mobility data can.
Whether you already have extensive shopper data or limited information on who shops with you, mobility data can help quickly and cost-effectively address your key business challenges. Today, mobile enabled devices such as smartphones and car navigation systems are ubiquitous. More importantly, the penetration of mobile devices is consistent across age and income ranges, with the exception of the very young or very elderly, which creates a very representative sample.
Mobility data analytics leverages this diverse dataset to provide consumer insights that would otherwise be cost prohibitive, if not impossible. Consider the following scenario. Let’s say that you are interested in how you compare to a competitor of yours down the street. Their parking lot always looks fairly busy, but then so does yours. Your sales have been steady over the last six months, but you’re curious about whether their recent product launch has been stealing visitors from you, or if they are attracting new customers altogether.
Without mobility data, how do you collect this information? On-site visitor intercept surveys, license plate surveys and online panels are all options, but they’re time consuming, costly and reflect shopper behaviour at a single point in time. If the samples were large enough, you could attempt to create trade areas for both your own location and your competitor using those postal codes you collected to see if you are drawing from the same areas. But more often than not, the sample sizes from such surveys are not large enough to make any definitive conclusions, let alone differentiate visits by time or day. And depending on the location of your competitor, some of these approaches may not even be possible.
While competitive shopper tracking is one of the most obvious uses for mobility data, it has several other public and private sector applications. By linking mobility data back to small area datasets such as Environics Analytics’ own PRIZM segments, you can develop meaningful profiles of loyal versus competitive shoppers. With these enhanced insights, organizations can more effectively target their marketing initiatives, reaching out to prospects who resemble their best customers.
This data will also enable you to identify the types of consumers who are walking or driving past your digital billboards at different times of day to inform scheduling, placement and messaging.
Mobility data can be instrumental in site selection as well, by identifying areas of higher potential value based on visitor patterns.
While traditional survey methods are cumbersome and inconclusive, it’s relatively easy to get more accurate and useful answers with mobility data. The first step would be to decide on what areas you want to study. It can be as easy as drawing a polygon on a map that outlines the boundaries of your location and your competitor of interest. You can choose to focus on your physical store or include the adjacent parking lot. The geographic term for these outlines are geofences, because essentially you are drawing a fence around the study area.
Next, you need to specify the period of interest. Are you looking at the most recent month, quarter or year of visitor activity? Given the speed mobile datasets are growing, the more recent the study period, the more comprehensive the coverage will likely be.
Once you have established the above parameters of your study, the mobile devices that were observed within these geofences during the established window of time can be identified. At this point, there are multiple vendors in the market that offer mobile visitor data. They vary by where they source their data, how it is captured, how it is processed and how it can be queried. To ensure compliance with consumer privacy regulations, personal identifiers such as device IDs are masked or randomized at the source so that all of the data is completely anonymous.
After you have gathered the data, you can quickly and easily query it to determine how many unique visitors you and your competitors saw over a given time period. Because you can look at this data over a larger time frame, you can filter by dates to determine trends and compare whether your competition is gaining market share or vice versa. You can also compare where your respective visitors are coming from and identify overlaps.
Mobility data will change the way you see visitors. With this data you can see if you are doing better in the mornings and late at night, while your competitor might excel with afternoon shoppers. You can also discover which of your customers are much more likely to visit your store exclusively relative to your competitors.
Going back to that earlier question regarding your competitor’s recent product launch, you can easily look at visitor behaviour before and after to understand the relative impact. Perhaps your shoppers visited the competitor to try the new product shortly after the launch, but did not return after. Or maybe they tried the new product and continued to shift visits to the competition. Without this mobile data, you would have limited (or no) insights into the impact of the new product on your shoppers and couldn’t make an informed decision about how aggressively you needed to act.
Mobility data quickly and easily allows you to generate insights about your customers that would simply not be possible without it. Whether you want to understand the loyalty continuum of your shoppers, measure who is seeing your out-of-home advertising or differentiating travelers who are stopping and enjoying your tourist destination versus just passing through, mobile data can provide new and unique business insights to make better data-driven decisions.
Paul Tyndall is Vice President, Strategic Projects at Environics Analytics