Once upon a time, bricks-and-mortar was the only game in town. Pre-digital marketers all shared a common goal – converting people to visit and buy from a real-world store was the very essence of advertising. Ecommerce has changed all that. As retail action has followed attention online, the dominant conversion quest now exists primarily in the world of clicks and linkbacks. This difference is attractive to advertisers thank to its inherent reliance on data – digital actions are supremely trackable, from the first click of an ad all the way through to an ensuing purchase.
This kind of insight makes driving people to stores seem quaint. The lack of data bridging digital ad exposure with offline visitation is crippling. No wonder ecommerce is booming. When you can’t prove the offline return of your digital spending, why bother? More than 50% of consumers’ pre-purchase research takes place on mobile, but the overwhelming proportion of US sales still occur offline. The dilemma for marketers is, can this divide be bridged?
Some have tried to crack the problem. For instance, Nielsen now supplies marketers with anonymized credit card data that can prove a purchase. But that data only comes in about four weeks after a campaign. For the new generation of advertisers who operate at programmatic pace, that is a lightyear.
But things are changing. The new glue is mobile itself, because the device consumers carry in their pockets drops a trail of clues like the proverbial Hansel and Gretel. New ad data vendors like PlaceIQ, Cuebig and even Foursquare are using those clues to offer advertisers a glimpse of the holy grail. This new world lets buyers know, for example, that a mobile user spent 90 minutes at the cinema for which they saw an ad yesterday, or that she regularly drives past her local 7-Eleven on the way to work.
Like its digital forbears, this discipline will also evolve, but today most activations employ rudimentary measurement. Merely logging a store visit is an acceptable conversion goal as a proxy for a possible end purchase. But as attribution data gets plugged in, we will be able to follow the journey all the way from click to visit to buy.
Today there are already great improvements that can be made from optimizing programmatic campaigns with visit rate in mind. Take the example of a money transfer company – whose business is almost entirely offline – that measured foot traffic at 25 Midwest stores, and was able to drive more visits by dynamically varying its media mix according to local precipitation amounts, commuting habits and time of day.
Or the DIY chain we recently worked with that bought real-time ads inside apps to drive real-world visitations using a Cost Per Store Visit metric. That was a new currency to us, but one I am sure will gain more traction as this technology comes further into usage.
We are still in the early throes of an in-store conversion boom that will plug the marketing data divide and reboot brick and mortar commerce. But already I am getting asked if typical patterns are emerging in the ways that digital campaigns influence in-store behavior.
The answer is that it is too soon to tell. The beauty of programmatic and machine learning is that we don’t necessarily need to identify and act on patterns we see – we can let the computer do that for us.
For now it is clear that advertisers with physical stores, as well as those with brand products sold in them, may be the biggest beneficiaries. So, whether you are a quick-service food retailer or a detergent maker, the best advice is to dip your toe in the water. The key is to treat them just like any previous ad category, by setting benchmarks, just like with CTR. Measure early and monitor mid-flight to find out what the future of in-store has, well, in store.