“Clicks are for kids; store visits are for adults.”
So goes the mantra of location optimization guru David Shim, CEO of Placed. He noted that 92 percent of retail transactions take place offline, but businesses only optimize for about eight percent of those.
More consumers than ever are going from digital to physical. In other words, they start their solution search online but end up in a physical store for their purchase.
In many cases, they are going back and forth between the two, or even using digital to do research while they are in a physical store. But businesses are missing most of that activity.
If we’re only tracking online behavior, then we are likely often declaring campaigns failures that might actually be succeeding. Clicks do not necessarily equal store visits. Sometimes campaigns with the lowest CTR (click-through rate) prove to drive the highest number of store visits.
Location, in particular, is largely ignored. Only eight percent of mobile optimization effort is invested toward location. Shim says it should probably be more like 50 percent.
Location-based optimization (LBO) will eventually match digital in terms of optimization investment. The metrics end up being similar to digital.
It is more important to measure lift from online ads than conversion of total store visits. How much higher is the conversion rate for those who saw the digital ad (exposed) vs. those who didn’t (unexposed)?
In the example above, dividing the ad-exposed conversion rate by the unexposed conversion rate yields the “lift,” the percentage by which the digital ad increased in-store conversions. Then dividing the total number of store visits by the lift yields the “lift store visits” (the number of store visits affected by the digital ad). This is the key LBO metric.
But not all location data is equal. For example, location is more than proximity, which gives a lot of false positives. Did the person actually visit your store, or was she just wandering nearby? You need to think about where the data is coming from.
Another factor is the conversion time window. The time in which a first visit after ad exposure is likely to take place flatlines after a while, but how long a time that is depends on the vertical. Restaurants have a shorter conversion window than auto dealerships, for example.
To be effective with location-based digital ads, use geofencing, limiting the circumference from the store to a few miles. The closer to the store, the higher the conversion rate and the lower the cost per exposure. Past five miles, there is typically little to no lift in local store visits.
Optimize for features like ethnicity, carrier, states, time of day and week. Don’t just target around stores, but find other places where your customers are.
For example, JC Penney in New York found that people who lived near certain subway stops on lines that stopped near their stores were more likely to be customers, so they geofenced around those subway stops, too.
It can be worthwhile to target even by the hour.
After analyzing these data, Target found that next year they should target their ads earlier. Their customers were out early, but they were at Home Depot, and they were already searching for where they might want to go later in the day.
Wondering how you might collect store visit data that can be linked to people who saw your online ads? Shim’s company, Placed, gets its location data via several different apps that offer an incentive in exchange for people exposing their location information.
These apps are able to track those users’ web behavior, and then track their location afterwards. Data from that sample group are then extrapolated to the larger customer population.
Check out the full set of slides for Shim’s presentation:
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