Closing the Loop: Proximity and Attribution
For marketers looking for insights into customer behavior, being able to reconcile their efforts with results is a key aspect of their jobs. For instance, how an ad on the subway directly impacts sales can be an elusive goal. Or whether or not an online campaign generates foot traffic to the physical stores. In the past, this has been a challenge that technology has met with measurable success in the online space, but hasn’t been easily quantifiable once the consumer has left the house and ventured into the real world. If data could be gathered throughout the whole sales process, from the home to store, whether online or off-line, the loop might be considered closed, and organizations might better gauge their campaigns and, more importantly from the customer’s point of view, shoppers might benefit from timely and personalized real-world communication.
The Customer Journey
Let’s look at an example. Alex, a driver of a particular demographic, is sitting at his computer at home on Long Island, comparison shopping for a new fuel-efficient car. He clicks on an ad, and ultimately, if all goes well, makes a purchase. But how he gets from the initial awareness of the product to the final decision of actually making the purchase exposes something: the customer’s journey is becoming increasingly complex. What in the world happens in this invisible chasm between the moment a consumer first lay eyes on a product, and the moment they reach for the wallet? As of now, we can tease some behavioral information out of the existing data. We can see where they were, perhaps, when they clicked on the ad, and we can reasonably determine where they were when they made the purchase. But certainly there’s more to the story. Isn’t there something else we could learn in that time between the two?
The Crucial Opt-in
One key component conspicuously absent from the process has been whether or not the customer ever visited the store. Of course, once the purchase has been made it’s pretty easy to determine that the customer was, indeed, in the store and did, in fact, make the purchase. We have the click, we have the card swipe. But what about all the information between the online shopping experience at home and the final visit to the store? And what about the behavioral clues that might be captured once they arrive at the store? And how can the consumer be helped if they made it to the store, but didn’t find the reason to actually purchase the product?
Studies have shown that customers are willing to engage in a quid pro quo exchange when they judge the trade-off as favorable. Is the value they’re receiving commensurate with the amount of privacy they’re giving up?
Which brings us to an important point. Perhaps the biggest challenge in gathering user data is getting the consumer to agree to information that appears, at a glance, to be private. Many companies abuse this practice and hide privacy terms in agate-sized print within EULAs no reasonable person could be expected to read. The fact of the mater is, we give up our rights to privacy every day. And likely for little to nothing in return. Where we go and what we do when we go there is not necessarily something we wish to divulge to just anybody. But without theses crucial breadcrumbs, it can be difficult for marketers to determine where the sale might have fallen apart, or where the messaging and sales support broke down. But for the customer to provide that data, several things have to happen, not least of which is the customer opting-in on the mobile device. And while that’s no easy task, studies have shown that we are willing to engage in a quid pro quo exchange when we judge the trade-off as favorable, giving up data if we deem the value we’re receiving is commensurate with the amount of privacy we’re giving up. Sure, I’ll give you my whereabouts for a 25% discount, for example, but I may not for a paltry 10%.
Proximity Provides the Missing Piece
The information we can gather from the mobile application at these touchpoints (and between them) is compelling. As the user moves through the complex sales cycle, from online browsing in the virtual world to in-store browsing in the physical world, we can start to see patterns and ultimately establish attribution. Some of the more compelling information we might discover using proximity are:
- A Consumer’s Physical Route
- Time Spent at a Location (Within a Geofence)
- Time Spent within Location Segments (Near Beacons)
- Consumer-initiated Interactions at a Location (NFC Tap or QR Scan)
By incorporating these technologies into the scenario we now have access to data we didn’t have before. Using a proximity platform allows campaigns to communicate with potential customers in physical spaces and do it at the precise moment the message makes sense. As these proximity systems are installed and mature, rules-based predictive models come in to play. At that point, campaigns can almost anticipate behavior and deliver a surprising results for consumers even before they think to ask.
So back to our friend Alex. He’s still shopping online, and he’s whittled down his short list to the Leaf, the Prius, and a Tesla with a salvage title, missing doors and two wheels. He’s downloaded the Auto Buyer’s mobile app and ventures out into the world of new and used car sales, scratching his chin and kicking tires. He visits 7 dealerships in all but eventually decides, after much soul-searching, to wait to make the purchase. He’s sensible. A $25k to $40k purchase is not an impulsive point of purchase sale for most, and for Alex, a lot more thought goes into his decision before he commits. But what happens to the data? How does Alex get help along the journey if it’s unknown what he’s up to? And when he finally does take the plunge, what will that sales cycle ultimately look like? With proximity touchpoints such as beacons, NFC tags and QR codes, all located on-site at the car dealer, and with preconfigured geofences around each dealership, we can safely say where he shopped and perhaps even determine which cars he looked at. We can even determine how long he may have looked at them, and which of the dealers he visited first and which he visited last.
In the end, we’ll conclude in our example that Alex bought the car. Sure, it took several months, but he did it. Perhaps one day he stopped into a dealership after work. He stood over the car he’d been researching, and as he hovered for a few minutes he suddenly received an attractive offer on his phone via push notification, and this notification appeared at the appropriate moment, in the relevant spot on the showroom floor where he stood. Soon thereafter the sale was completed. Business intelligence assisted in this transaction, but not without the help of critical data gathered by proximity technologies. Alex opted-in because the value was there. The mobile app provided helpful information allowing him to comparison shop, locate the dealerships, and contact sales personnel when he had questions. And in the end, the application provided Alex with an offer that sealed the deal. It arrived in the palm of his hand, at exactly the right place, and at the right moment.
If you’re interested in learning how proximity data can integrate with your existing business intelligence systems, click below to see how Smartwhere can help you better communicate with your customers.
Smartwhere is a powerful proximity platform that allows you to quickly and easily integrate proximity solutions into your existing environment. Whether it’s mobile retail, interactive marketing, or location-based advertising, Smartwhere is an all-in-one cloud-based platform that delivers and manages relevant content to consumers and other end users when and where they need it.