Understanding the New Horizons in Ad Attribution Tracking: From Advertisement to Store Foot Traffic

By Mike Rowan

In the dynamic world of digital marketing, attribution has always been a key concern for businesses. However, stores that rely on digital advertising to drive revenue through in-store purchases have often had to rely on obscure metrics — or even an imprecise “gut feel.”

As anyone in business knows, understanding exactly what led a customer to buy, or influenced a purchase decision, is vital for planning and optimizing advertising strategies. Recent advancements in technology have taken attribution to the next level, allowing brick & mortar retailers to access much more precise tracking from online advertisements to in-store foot traffic. 

Let’s explore the current landscape of ad attribution tracking, focusing on the latest methodologies and tools that are reshaping the way that many stores with a physical presence can now rely on firm data as opposed to making educated guesses.

Traditional Attribution Model

 

Traditionally, ad attribution has primarily been focused on online behavior, utilizing models like Last Click, Linear, or Time Decay to trace the digital path a user took before making a purchase or conversion online. While informative, these models couldn’t connect online advertisements to physical in-store visits or purchases.

This gets even more complex as you consider how many brands rely on television, radio, print and even outdoor ads to reach their target audience and drive foot traffic and revenue into their locations. With the full digitization of TV  and radio, combined with the advancements in mobile and location tracking, this attribution loop is finally closing at a rapid rate. 

Integrating Online and Offline Behavior

 

Tracking the impact of display ads on in-store foot traffic and purchases is a complex task, as it requires connecting online interactions with offline behaviors. However, it is possible by employing various strategies and technologies. Here’s how you might approach it:

• Use Unique Codes or Coupons in Your Ads:

Historically, this method has always proved to be quite popular. By providing unique discount codes or coupons in your display ads, and asking customers to show or use these codes in-store, brands have the direct ability to track redemptions and gauge the effectiveness of specific campaigns.

However, while many POS technologies can track these transactions, it can still be problematic as it doesn’t account for the large number of people who may make a purchase but leave that “valuable” coupon at home. Therefore, this method may offer a partial picture of the overall effectiveness of a campaign. 

• Loyalty Programs and Mobile Apps:

If you have a loyalty program or mobile app, you can track customer purchases and visits to the store. The app not only can help to offer incentives for customers to check-in or use the app in-store, it also can analyze the data to see if there’s an uptick in foot traffic or purchases following the display of specific ads.

While this method can work, it still presents challenges — namely, convincing customers to download the app/sign up for the program. As with redemption codes, it may provide valuable data but strike out in giving the brand a comprehensive conversion model as it likely doesn’t incorporate many of the initial steps that it took to sign up or download in the first place. Plus, many people will forgo signing up and just make the purchase, which may lead to an unquantifiable source of revenue that isn’t properly assigned to a particular channel. 

• Incorporate Beacon Technology:

Beacons, which are small, Bluetooth devices that can be placed around the physical location, can be very helping in closing the attribution loop.  If customers have your app or have Bluetooth on the beacons can communicate with the device, providing data on their in-store behavior. By leveraging this technology, you can cross reference the data to see if there’s a connection between previous ad impression and store visits or purchases. 

However, beacons rely on Bluetooth being enabled on the device and/or the app being in use in order to be effective. Like other methods, this can lead to an overall gap in the confidence of the end reporting.  

• Track Payments with Partnerships:

Tracking digital ad conversions with payment data involves using a combination of online tracking tools and payment processing data to determine how many conversions resulted from a particular digital advertising campaign. Here’s a step-by-step guide to help you track conversions with payment data:

• Create Unique Campaign Identifiers:

Assign unique identifiers to your digital ad campaigns, such as UTM parameters in URLs. These identifiers can include information about the source, medium, campaign name, and other relevant details.

• Implement Tracking Pixels:

Use tracking pixels (also called web beacons or conversion pixels) on your website or landing page. These small, transparent images are embedded in the webpage and trigger a server request when the page is loaded. This request can track events like page views, form submissions, or other interactions.

• Use Cookies and Browser Storage:

Use browser cookies or local storage to track visitors who have clicked on your digital ads. This allows you to follow their behavior on your website, see which pages they visit, and track if they complete a conversion action.

• Integrate Payment Processing Data:

Connect your payment processing system (e.g., Stripe, PayPal, Square) to your tracking software or analytics platform. This integration allows you to correlate payment transactions with the unique campaign identifiers, cookies, and tracking pixels you’ve set up.

• Analyze and Attribute Conversions:

Analyze the payment data and attribute conversions to specific ad campaigns based on the unique campaign identifiers. This step involves matching the payment transaction records with the tracking data to determine which campaigns led to successful conversions.

• Utilize Geo-Targeting and Geo-Fencing:

Using location data is one of the more cohesive ways of putting more of a true attribution model together, especially when it comes to advertising. 

When ads are served, you can pixel or cookie the individual devices and also identify additional household devices that may be contained on the same network or using the same IP address. By having a more comprehensive list of devices that were exposed to a particular message, there is a high probability that the buyer can be identified as they enter into a set location/geo-fence and attribute them back to the original or supporting source. 

To make this especially effective, you should also integrate the payment data associated with each purchase so that it can map the actual transaction back to the original source or the ads that ended up assisting in the conversion. (see above)

This isn’t particularly easy to set up, but it isn’t terribly difficult either, provided that the team is well versed in data management and makes sure that all the proper connections and 3^rd party partners are hooked up. 

Challenges and Ethical Considerations

 

While these technologies offer incredible insights, they also bring challenges and ethical considerations. Privacy concerns are paramount, and businesses must ensure that they are adhering to legal regulations and best practices in data collection and handling.

Make Sure You Protect User Privacy!

 

Ensure that you are following all applicable privacy regulations and best practices when collecting and using data. Obtain user consent when required, provide transparent privacy policies, and allow users to opt out of tracking when necessary.

Keep in mind that attribution can be complex due to factors like multi-device browsing, ad viewability, and cross-channel marketing efforts. It’s essential to adopt a comprehensive approach and consider various attribution models to get an accurate view of your digital ad conversions.

Case Studies: Success in Attribution

 

Several major retailers and brands have successfully implemented these technologies to fine-tune their advertising strategies.

Example 1: Retail Chain A

Retail Chain A used geolocation tracking to determine the impact of their online ads on in-store visits. They found that customers who interacted with online ads were 25% more likely to visit a physical store, allowing them to refine their marketing strategy.

Example 2: Brand B

Brand B employed beacon technology to understand in-store behavior and found that their in-store layout was causing confusion for customers. By making adjustments based on the data, they were able to increase in-store conversions significantly.

Future Prospects

 

The field of ad attribution is evolving rapidly, and the future promises even more refined and integrated tracking methods. From virtual reality to artificial intelligence, new technologies will continue to blur the line between the digital and physical consumer journey, providing marketers with unparalleled insights.

Conclusion

 

The latest advancements in ad attribution tracking represent a seismic shift in the marketing landscape. By bridging the gap between online advertising and in-store foot traffic, marketers are now equipped to understand the complete consumer journey.

Businesses that embrace these new methods stand to gain a significant competitive advantage. However, the complexity and ethical considerations associated with these technologies mean that careful planning and execution are crucial.

In a world where every interaction can be traced, measured, and analyzed, the potential for understanding and serving customers has never been greater. The future of ad attribution is bright, and those who navigate it wisely will thrive in the evolving marketplace.