Foot traffic attribution, which ties ads to consumer actions, is a challenge marketers have long struggled to solve. As we progress further into the digital age, more companies are advertising through online and offline channels, making it harder to establish which ad campaigns are working and which may be a waste of resources and ad spend.
Tying ads to online conversions, purchases, and sales is simple and straightforward. Most ad platforms display these metrics on their dashboard home pages. They show you your ad spend per channel, click-through rate, and conversion value. Here’s what that looks like in Google Ads.
What most ad platforms cannot tell you is how your ads drove foot traffic to stores and other physical locations you care about. If driving foot traffic to retail locations is your job, Google Ads and other digital ad dashboards can’t help you. When in-store foot traffic attribution is crucial, how do you solve for it?
Keep reading to understand the different kinds of attribution and three ways location data can improve your foot traffic attribution measurements.
- Foot Traffic Attribution: The Basics
- 5 Ways to Solve for Foot Traffic Attribution
Foot Traffic Attribution: The Basics
What Is Foot Traffic Attribution?
Marketers who work with retailers or brands that have multiple brick-and-mortar locations (auto dealers, amusement parks, event venues, etc.) can use foot traffic attribution. This is a more straightforward approach because you only need two types of data that can be easily tied together.
The ingredients for foot traffic attribution are ad data and visit data. Ad data relates to media and the different channels through which marketers serve it. Location data relates to people who were exposed to the media and the locations a marketer is trying to get people to visit.
How Foot Traffic Attribution Works
For foot traffic attribution, a marketer starts with an audience created from location data. For easy math, let’s say the audience size is 100,000 people. The marketer would then push this audience — made up of mobile ad IDs — into a demand-side platform, a social media channel or any combination thereof. Ad platforms do not always match the list of mobile ad IDs at 100%, so let’s say there was a 75% match rate, meaning 75,000 people can now be targeted. Lastly, using location data, the marketer can see the impact the campaign has on in-store traffic by measuring the number of devices that came back during the campaign period from the original audience of 75,000.
Three Questions To Ask About Foot Traffic Attribution
Let’s bring this all together and set out some steps you can take to get started on foot traffic attribution, no matter how simple or sophisticated your current tech stack may be.
How do we define “attribution?”
First, define what attribution means to your company or client. Does it mean getting people to visit a certain location? More specifically, does it mean getting them there on a specific date or at a specific time? Does it mean having people buy something while they’re there? It could even mean getting an audience to buy a single SKU or spend more than they usually do.
What is an acceptable ROI?
Next, determine an acceptable ROI. Are you measuring traffic — how many people showed up at a certain location? Or, getting far more granular, are you measuring sales per square foot? Your ROI goal may be best made in collaboration with other teams — product, sales and finance, to name a few.
Which location data product is best for our needs?
Finally, get familiar with the location data products available on the market. They are not all created equal. There are variations in data quality, software ease of use, customer service, privacy compliance, scalability, POI accuracy and more. Your foot traffic attribution efforts depend on these criteria. This location data primer for marketers is a good place to start.
The Foot Traffic Attribution Continuum
There are different ways marketers use to measure attribution. These methods can be ranked in terms of difficulty. This list starts at easy and progresses through hard.
- Coupon codes
- Location data
- Pixel tracking
- Point-of-sale integrations
Easy Foot Traffic Attribution Methods
Marketers have long tried to solve for foot traffic attribution by asking, “How did you hear about us?” While this is straightforward and allows marketers to assess different channels, both online and offline, the data is not reliable. Most consumers don’t complete surveys like this, or they may misremember. As a result, the data tends to be unreliable. Let’s break this down further.
First, you can only ask customers who gave you their information or are making a purchase in-store. This dramatically reduces your data set. Stores will not capture casual shoppers who pop in or who are never entered into the POS system. FocusVision, a consumer survey company, reports that response rates may be as low as 5%.
Second, when customers do answer how they heard about your store, you have to rely on their memory. They may have heard a radio ad, seen a billboard and heard from a friend, but they may have forgotten some of these channels, or the survey may ask the shopper to select only one answer. Answers, when accurate, may be highly variable, making it difficult to discern patterns.
Another simple way to measure the impact of a campaign is through coupon codes. These are best used for campaign or promotional attribution and less useful for understanding which channel led to a conversion or sale.
Use a keyword in your CTA on your digital ad or send a flyer to the zipcodes you hope your customers live in and tell them to bring in the coupon to receive 20% off their purchase. Codes are typically campaign-specific and can easily tie ads to in-store traffic. This option has its weak points. Customers may forget the keyword, leave their flyer at home, or come in when the coupon is expired.
Standard Foot Traffic Attribution Methods
The power of location data drives the most reliable solution to foot traffic attribution, tying digital ad spend to in-store visits. With location data, advertisers create custom audiences of visitors to locations they care about and serve ads to those people.
These audiences can be made up of current customers, your competitor’s customers, and visitors to locations advertisers otherwise care about. In your location-based marketing software where you created or accessed the audience, you can see what portion of your target audiences showed up in the locations you are advertising and calculate attribution.
Depending on the tools you’re using, location-based marketing software can also show you more detailed reports, such as foot traffic by day and comparisons to your competitors’ in-store traffic.
Advertisers can take foot traffic attribution powered by location data a step farther by using pixels within their ad serving platform. With the addition of a pixel, marketers can see who was actually served the ad and tie that cohort of viewers to in-store visits. Combining location data and pixel tracking makes foot traffic attribution even more granular and powerful.
Hard Foot Traffic Attribution Method
The final and most difficult option for tying ad spend to in-store activity is what happens at checkout — what the customer actually buys and how much they spend. This provides the most detailed analysis of ROI on digital campaigns; however, visit, point-of-sale, and transaction data intersect with several layers of the tech stack, and the data can be siloed and difficult to integrate. Brands and retailers run multiple systems, and these depend upon complex, tight integrations. This takes engineering time and dollars.
Solving for foot traffic attribution is not hard when you take the right approach. To determine which is best for you, you must define your goals and decide how much you want to invest in attributing digital ads to in-store visits or transactions. The middle path is an ideal option for the many brands and retailers and the agencies that work for them.
Reveal Mobile Has Democratized Foot Traffic Attribution
Attribution is hard. And expensive. To add insult to injury, sometimes it’s wrong. Advertisers have struggled to accurately measure campaign performance for as long as they’ve been running campaigns.
Attribution is so infamously difficult that everybody knows the adage: Half the money we spend on advertising is wasted. We just don’t know which half.
Why is attribution so hard, expensive and unreliable? Two reasons: Marketing is complex. Consumers are unpredictable.
When you’re running campaigns to generate in-store foot traffic and drive retail sales — this is where attribution has truly fallen short. It’s either been too expensive or too difficult to make sense of.
Reveal Mobile’s results-driven geofencing marketing tool, VISIT Local, has democratized attribution for location-based campaigns once and for all. It’s built right into the product, and it’s included with every campaign at no extra cost.
VISIT Local has made foot traffic attribution for marketing campaigns easy, inexpensive and accurate. Best of all, you don’t have to change where you’re running your campaigns to take advantage of VISIT Local’s clear, actionable reports. They’re all in one centralized place.
VISIT Local provides marketers with the ability to track the customer path from view to visit for four advertising channels: web, mobile app, streaming audio, and CTV/OTT.
While campaign attribution used to be accessible only by the largest organizations, now advertisers big and small can run ads in a wide range of high-growth channels and know precisely how their ads performed.
VISIT Local has leveled the playing field for marketers everywhere. We give media buyers at NRF’s top 100 retailers and digital leaders at 10-person agencies alike the same power to know and understand the value of their paid media investments.
To learn more about foot traffic attribution, download the whitepaper “Foot Traffic Attribution and the Digital Marketer.”