A deep dive into online-to-in-store attribution
Columnist Michael Della Penna walks you through the various methodologies used to understand the impact of impressions on in-store visits and how they affect campaigns.
Connecting advertising to outcomes has increasingly become a key initiative for companies of all sizes. Over 50 percent of companies with 100+ employees and more than one digital channel are using multichannel attribution models, up from 22.9 percent in 2014, according to eMarketer. Keep in mind that this figure only includes multichannel attribution for digital initiatives.
Marketers are also turning to mobile and location data to help bridge the online and offline gap and to provide a fuller picture of attribution and a consumer’s behavior after exposure to an offline ad.
Technology that measures impression-to-store-visit data at scale has garnered more interest among marketers over the past year. This tech gives marketers the opportunity not only to understand uplift resulting from an ad exposure, but the customer journey, competitive behavior and more. (Full disclosure: My employer is a provider of an online-to-in-store attribution solution.)
How does it work? There are different approaches, with varying effects on campaigns.
First, it’s important to understand how location data is collected. There are four main ways: bid stream, panel, SDK and beacon-based.
Bid stream solutions take a user’s latitude and longitude off an ad he has opened. Panel providers gather a subset of users and project or model the data to the larger population. SDK (software development kit) providers gather location signals from GPS, WiFi and Bluetooth within an app. And beacon providers require users to have Bluetooth on and read the signal when in proximity to a beacon.
If you’re looking at location to determine attribution, both precision and accuracy are paramount to validate an actual visit. Precision means how exact the location is on the map and tied to a point of interest such as a retail store. It’s the difference between placing an individual at 55 Grand Street vs. somewhere on Grand Street or lower Manhattan. Precision is expressed using lat/long coordinates — the more decimal places you have, the more precise your location is.
Accuracy is how close the location provided is to the actual location. If a user is placed at 55 Grand Street, for instance, that is being really precise, but the accuracy may not be right because the user might actually be at 125 Houston Street — a few blocks away.