Many brands struggle to prove ROI for their digital media spend. These 3 simple strategies can to tie real-world conversions to digital advertising.
Unlike TV and Radio advertising, digital media offers the possibility of tracking individual ad views to purchases. Many brands have made this leap for their online and in-app purchases, but a much smaller number have taken the next step and started tying digital media to real-world activity – or even realized that it’s possible!
That’s a huge missed opportunity – for modern advertisers being able to accurately link media to sales is an essential step in proving the value of campaigns. There are three main strategies that can be used to do this deterministically.
- Bring the event into the app
- Use a coupon or QR code to tie together the mobile action and the physical action
- Use a persistent anonymous user identifier, such as a loyalty card number, to track users across platforms.
There are also a number of probabilistic solutions available, but this post will focus on the deterministic methods that you can build yourself. Note that all of the ideas outlined here are based on solutions I have helped clients implement. These are not hypotheticals, they are battle tested and can work at scale for any brand that meets the prerequisites.
The first strategy, bringing the event into the app or website, is often the easiest. This is the classic “buy it in app/online and pick up in-store” model. Any product that can be purchased on a website or app and then picked up at a physical location can be tracked this way and I have seen this solution work for businesses ranging from fast food restaurants to big box stores. Because everything happens in-app it’s easy to use your attribution analytics tool (whoever you use) to tie the conversion back to the campaign of origin and calculate ROI.
There’s an added bonus to this solution in that you get the mobile conversion up front, and then still get the user to come into the physical store, which opens up the possibility of additional add-on conversions. Tracking the value of those add-on conversions can be challenging since they happen outside of the app, but this is where strategies two and three can come into play.
The second method requires a bit more technical infrastructure. In this scenario you show the user an ad for your store and once they click it, they are taken to a page on your mobile website or in your app with a coupon that they can save. Once in the physical store, they get the items and checkout as normal, scanning the coupon from their phone to redeem the discount or incentive. This let’s your internal CRM system know that the purchase was driven by a mobile or web engagement and send a server-to-server message back to the analytics tool you use for your website and/or app to record the conversion as an event – just as if it had happened in-app.
In an ideal deployment, the coupon includes a unique identifier so you can tie the in-store conversion to the unique coupon code, which gives you the specific user session. You can then use your attribution service of choice to tie that session back to the specific ad impression. Again, you end up with the data you need to calculate ROI for your creative and campaign, which means you have the info you need to iterate intelligently for future versions of the campaign.
The weakness of this strategy is that, because the conversion hasn’t already happened in-app, you’ll see a much higher drop-off rate between showing the coupon and in-store conversions. There will likely also be a significant percent of users that forget to use the coupon at checkout, making your drop-off rate appear even higher than it actually is. Because of this, many brands adopt a hybrid of options 1 and 2, driving as many people as possible to pre-purchase in-app and then providing a unique code which is scanned in-store to pick up the item or items. That way the actual in-store pickup can be tracked, along with any additional in-store purchases.
The third strategy requires the most up-front setup because you need to have an internally-generated persistent ID for each user already in place – a brand loyalty program is ideal for this. The win here is that it lets you tie the full range of logged-in web behavior and in-app behavior to the data you already have for the customer’s in-store data, providing an unrivaled picture of the customer’s buying habits. The key step here is getting the end user to enter their loyalty program id into your app at some point. Once that’s done you can make a 1 to 1 match between the user profile and the device or devices on which that user runs the app. Note that apps – because they use a persistent device identifier and the user can stay logged in across multiple sessions or even permanently – make this 1 to 1 linkage far easier than a website where you have to get the user to login each time.
This is the holy grail of smart advertising and means you can target media to customers based on their buying habits online and in-store. With this type of system in place, all the guesswork around putting the right ad for the right product in front of the right customer disappears. As the user responds to those ads and completes actions in-app or in your physical store, you can log them all against a single anonymous user identitfier in your CRM system. The challenge of course is getting everything into a single system. For the first two scenarios I’ve recommended pushing the data from your CRM into your analytics tool, but in this case I’m going to recommend moving in the other direction and pushing data from your mobile and web analytics tools into that CRM. That said, I’ve seen this work both ways.
For privacy advocates who think this sounds like the premise to a dystopian novel, keep in mind that at each step of this process the end user is actively consenting to be tracked and is receiving rewards for doing so. This profiling data should also only ever be tied to an anonymous id – and never to personally identifiable information (PII); both for ethical and legal reasons.
There’s also a challenge because the sheer scale of data involved in option 3 means that most companies have not made the technical investments required to make it work. That said, I have helped several large companies set systems like this up. Those companies are reaping the rewards with more targeted ad spends, higher conversion rates, and stronger ROI.
There are other approaches to solve this problem, of course, but most of them echo the general shape and structure of these three solutions.
How have you addressed this challenge?
Note: A different version of this article, edited to read like an interview, was published on the RadiumOne blog at https://radiumone.com/radiumone-insights-tie-digital-media-store-traffic/