Share-of-visit data: A real door opener for QSR success

| by Drew Breunig
Share-of-visit data: A real door opener for QSR success

For brick-and-mortar retailers operating in today’s market climate, having an accurate gauge of audience visitation is crucial. Currently, 93 percent of sales still happen in-store and even a 1 percent shift in visitation share can have multi-million dollar consequences. Innovative brands have turned to location data to understand these shifts.
Share-of-Visit (SOV) is a marketing term that refers to the amount of total visitation a business location captures in comparison to the rest of the marketplace. While understanding the raw count of visitation to locations is an important indicator, today’s brands are finding much more value in viewing SOV from different perspectives and using its subsequent insights to uncover opportunities and fuel a host of decisions. 

In essence, you can think of share-of-visit analysis as an indicator of overall brand health, an enabler of insights about buyers, and a key layer on which decisions about business and marketing can be made. But, before we dive into real share-of-visit analysis, it's essential to understand the building blocks behind it. 

By looking at patterns of visitation — sourced from anonymized location data — the stores that audience frequent become obvious, as do the times they visit and even what other points of interest they visit. When coupled with other data sources, like TV viewership, auto ownership or demographic data, a much more refined view of what makes up a specific consumer audience emerges. 

For example, audiences that are morning commuters, frequent visitors to clothing stores and likely Hispanic can be grouped into a young Hispanic millennial audience. Then, then brands can measure SOV for these types of audiences to understand how customer engagement ebbs and flows in response to marketing. 

The result is an investigative snapshot of foot traffic across a segment, which helps assess overall traffic, store efficiency and market health. In fact, this type of analysis can also help brands prioritize competitors, market to market.

A 'real world' SOV analysis 

What follows is a glimpse of how this works when you look at share-of-visit to see how it – when viewed from different angles -- can change the way brands can explore customers and put insights into action. In this case, let’s look at the quick service restaurant (QSR) vertical. 

A look at some of the top QSRs, by daily national traffic tells a fairly intuitive story with peaks and valley for visitation and the Subway chain at the top of the heap. That makes sense since Subway has many more stores than others on the list. But if you look at share of visit (the percentage of the total QSR diners visiting these QSRs), it becomes clear that these brands have pretty much locked in their market share for the quarter:

























Since these charts reflect the number of stores a brand has in a market, Subway is still the leader since they have the greatest number of locations. But when that factor is removed from the equation, a completely different picture comes into focus:


























Through upper tier analysis, it becomes clear why McDonald's is so successful. This analysis shows that although McDonald's has fewer stores than Subway, those stores attract many more visitors per location.   

A closer look at Chick-fil-A also reveals some otherwise hidden insight since the trend line for visitation indicates the chain experienced a 3 percent gain last November, followed by a 15 percent gain in December. This is a relatively massive shift in audience, particularly when you consider that Chick-fil-A locations do not populate the landscape as heavily as Subway or McDonald's. 

These graphs show a breakdown of the brands' performance by DMA: 


























The bars show the number of visits in 2016 Q4 that Chick-fil-A (in the left axis) achieved in a particular market in Q4 2016. Atlanta emerges as their top market, followed by Dallas and Houston, with the dots corresponding to the number of visits per store in the right axis. 

Insights revealed through this breakdown of data indicate interesting information like the fact that Charlotte is the chain's No. 1 market for store efficiency. Likewise, a glance at the red dot on the graph, indicating the QSR norm in that marketplace, gauges how Chick-fil-A performs compared to market averages. 

The takeaway

Even though Chick-fil-A has a smaller footprint, these types of analyses make it clear that the brand is over-performing by a significant margin, especially in North Carolina. In fact, these graphs and their data show that the sole market with less than above-average performance for the brand is Philadelphia. That realization means it's probably a good idea for the brand to look at the data from cities like Philadelphia, Atlanta and major cities in Texas to learn how those QSR customer demographics differ.

But just the graphs we've tracked here continue to yield valuable insights since they also show how the chain performs to the  in-market average for the entire QSR category. In those last two images, you can see that the dotted line – indicating national efficiency as measure by visits per store – shows Orlando and Tampa are over-performing for Chick Fil A, while other areas like  Houston, Texas, Louisiana and Washington are merely in line,  indicating they might be prime territory for chain expansion. 

Of course, this has been just a small snapshot of how brands can use share of visit to better understand and attract audiences. Likewise, these types insights are versatile and can be applied to a wide range of decisions and areas of operation like media strategy, competitive intelligence, retail site optimization, and cross-channel campaign. 

Topics: Business Strategy and Profitability

Drew Breunig
Drew Breunig leads business application efforts at PlaceIQ, mapping client needs and goals to PlaceIQ's audience, data, and analytics products. Drew aims to make advertising and customer intelligence more effective, insightful, and manageable through the use of local intelligence. wwwView Drew Breunig's profile on LinkedIn

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