The “Check Engine” Light is On: Your Dashboard is Trying to Tell You Something

By David Lobaugh, President, August Partners, Inc. | January 2021

I recently replaced my car battery. When I turned the ignition key, the “Check Engine” light went on. So I did what any automotive savant would do under the circumstances. I stared at my dashboard, made a mental note, and then ignored it.

Wrong Move, Goober.

About $1K later, I found out that my car has an electronic throttle. Who knew? It turned out to be a costly lesson. I should have hooked-up my dashboard to a “diagnostic test and management device” – AKA a scanner – which would have read the codes, crunched the data deep in my car’s onboard memory chips, and analyzed what its problem was.

Just glad I’m not a broker. If I were, I’d be driving a Porsche, and this would have been a $2K lesson.

Dashboards: Little-Data Tip-Offs that We Need Big-Data Solutions

Over the past several years, we have seen the rise of location data availability in the retail real estate world. There are several purveyors of this data, who provide ongoing means of tracking shopper visits, seasonal trending, mapping options and other assorted datasets.

There’s no question that you should subscribe to one of those services if you’re not already doing so. However, you should realize that those very cool footfall graphs will take you just so far.

You will know where you are, but you won’t know how you got there. You will know the score, but you won’t know how to improve it. Topline tracking metrics are a must-have, but no one ever got rich just by perusing the Wall Street Journal’s daily ticker.

The “Check Engine” light is merely an indicator of what’s happening under the hood. You can see the effect, but not the cause.

Only when we lift the bonnet, plumb the underlying data, and analyze the findings can we successfully begin to reposition, remerchandise, re-lease and re-market our retail real estate assets.

Case Study: Second Location/Relocation F&B Deal

Let’s look at the case of two centers – Open Air Specialty Center (OASC) and Competing Mall (CM), located in the sunny Southeast. The former is a 500K sq. ft. center with a mix of apparel, food & beverage, and services. The latter is an 800K sq. ft. enclosed mall with a slightly more upscale blend of apparel, food, etc.

CM’s online leasing backgrounder boasts of its location in higher income zips, its regional draw, and the usual palaver about being in one of the state’s fastest-growing markets, etc., etc. You’ve heard this story before: “The market is good, so our center is too.” Long on puffery, short on data.

OASC – using location data study findings – discovered the true story: “Our center draws more shopper visits than CM. Our shoppers are more affluent than CM’s shoppers. And what’s more, we siphon-off half of their shoppers every year.”

OASC’s leasing team had been eyeing a couple of food & beverage prospects located at CM…

The Visit Share Win

Competing Mall is more than 1.5X larger than OASC.

Conventional wisdom said that CM was out-drawing OASC. As Goober’s cousin Gomer would say, “Surprise, surprise!” The data proved that OASC had the edge.

When we tallied the shopper visit sample counts, we found that OASC had a 120 basis-point advantage versus CM (left). Tracking-back the data to shopper points-of-origin, the numbers also showed that OASC substantially outdrew CM in a number of key tourism feeder markets across the U.S.

OASC’s food & beverage sales were robust, and management had been thinking along the lines of bolstering GLA allocation in that category. The leasing team had put out some feelers to a couple of key Competing Mall F&B tenants but had not made any headway.

The shopper visit share findings were a good starting point, but 1) not sufficient for the center’s own intel needs – i.e. to think through the merchandising strategies for the property, and 2) not necessarily sufficient in making a case to the F&B prospects the leasing team wanted to approach.

The team needed a deeper dive and further analysis.

The Weekpart/Daypart Drill-Down – A Merchandising Strategy Fundamental

One of the great features of location data is that each shopper visit data record is date- and time-stamped.

Using hundreds of geofenced property datasets, we have analyzed day-by-day and hour-by-hour shopper visit patterns. Predictably, the Thursday-Saturday weekpart is vitally important: those days generate nearly half of retail center shopper visits, across all types of centers, with minor variations among grocery-anchored, mall, open-air, outlet, and other formats.

The basic takeaway: If you optimize for Thu-Sat, you’ve optimized for the whole week.

The table at right consolidates pre-Covid normative levels for the Thu-Sat Lunch (Noon-2PM) and Dinner (5PM-8PM) periods, as well as for the overall Thu-Sat shopper visit percentages.

Note: the lunch and dinner period visit percentages above do not necessarily mean that all those shoppers in the centers are dining during those hours, but it does reflect how each centers’ respective F&B lineups influence the overall visit percentages throughout the key periods. A rising frequency tide lifts all boats.

The Thu-Sat Weekpart/Daypart norms put the findings in perspective for Open Air Specialty Center versus Competing Mall:

OASC, CM and the AP norms for the overall Thu-Sat visit-draw metric are essentially the same (in the 48% range)
The Dinner period metrics for the two centers are both above the AP norms, and comparable to each other
The Lunch period percentages are favorable to OASC vs. CM, but it appears that there is room for either expanded F&B square footage, format/price-point adjustments – or both – when we comp OASC’s 11.5% to the AP norm of 13% (We have seen the Thu-Sat lunch percentage go as high as 16%-19% in well-tenanted centers).

The OASC leasing team now has a quantitative insight into an important aspect of its merchandising strategy. It also has more data to use in its future discussions with potential F&B tenants.

Shopper Quality & Cross-Shopping – Going for the Steal

From a merchandising perspective, OASC appears to have an opportunity to expand its F&B footprint, capitalizing on its existing lunch hour pattern strengths, with an added upside of increasing shopper visit frequencies.

How does the leasing team make its case to prospects? Do they approach an existing CM tenant for a relocation “steal,” or a second location? Are they looking for new entrants to the market or nearby candidates searching for expansion opportunities? In any event, how can the team marshal the data to make a deal? The visit share finding is a good starting point, but there’s more.

We Have a More Affluent Shopper Base…

As seen, OASC wins the visit share battle, 56% to 44%. Quantity is a good start, but what about quality?

OASC also wins the shopper affluence matchup, with a 12.6% edge in average HHI vs. Competing Mall’s shopper base (b in the table below), and a 1.5% advantage in dining-out spend per HH (c). Note that both centers substantially out-draw the ambient MSA HHI level of $77,500.

There’s another important set of stats in the table: The Shopping Frequency Index (a) – essentially an indicator of shopper loyalty. The data show that Competing Mall is more effective at generating repeat visits from its shopper base, most likely because of their high GLA allocation of F&B, a key category for driving shopping frequency. If OASC can make a couple of key dining deals, they could potentially boost their SFI score.

…And We’re Already Drawing Half of Your Customers.

The OASC leasing team is well-armed, but there’s one more thing: the data deep-dive shows that there’s a high level of cross-shopping between the two centers, with a clear-cut advantage going to Open Air Specialty Center.

Half of Competing Mall’s shopper base – measured over a 12-month pre-Covid, normative period – also shop at Open Air Specialty Center. So the message to a F&B tenant currently located at CM is this: OASC is an excellent second location or relocation option.

To paraphrase: “In the past year, two-thirds of our more affluent, higher-spend shoppers did not set foot in the mall where you are located, while in that same time period, half of your mall’s shoppers – your diners – visited OASC.”

It Takes 10 Million Lines of Data to Tell a 3-Point Story

The average geofence project generates more than 10 million lines of underlying location data. In this case, it boils down to being able to summarize the storyline in three points:

We have a higher shopper visit share
Our shoppers are more affluent/spend more than our competitor’s shoppers
We represent an untapped opportunity, and siphon-off half of our competitor’s shoppers, day-in, day-out

Next steps: putting it all together in presentation materials, in center fact sheets, in custom packages for targeted prospects. And in the leasing handouts you’ll be putting in your booth come RECon time… whenever that may be.

My “Check Engine” light is on again. Must be the headlight torque… or spark plug gears… or maybe the lug nut protostat.

I’ll keep an eye on it.