How AI and ML bridge the attribution disconnect across marketing channels

As marketers pour more budget into digital channels, a surprising disconnect remains. While the majority of retail sales, for example, still happen in physical stores, most marketing efforts focus solely on tracking online metrics.

The challenge? Traditional attribution models fail to connect digital spend with real-world outcomes. To bridge this gap, marketers must embrace AI and machine learning to gain a full picture of how their campaigns drive both clicks and in-store purchases, unlocking a deeper understanding of true ROI.

The CMO’s attribution dilemma

If you’re a CMO, chances are you’re laser-focused on delivering results and spending most of your marketing budget on digital channels. But with 80% of U.S. retail sales taking place in brick-and-mortar stores and nearly 80% of marketing budgets spent on digital channels, something doesn’t add up​. 

At first, this number surprised me, given how much time people spend online. But from a consumer’s perspective, it actually makes sense. I don’t like to buy shoes without trying them on. I’ve also been known to walk into a store for one thing and walk out with $200 of skin products. 

The reality is that we’re throwing money at screens while our customers are walking through doors. But if we measure clicks but ignore what happens in-store, how do we confidently prove marketing’s impact? 

It’s tempting to think digital is king when it comes to attribution, but focusing exclusively online is a mistake. Many attribution models are still painfully bad at linking digital efforts to real-world actions like foot traffic and in-store sales. If your metrics end at clicks and impressions, you’re missing the bigger picture — and, worse, you’re misdirecting budget based on incomplete insights. 

Marketers should demand better answers to age-old questions like:

• Which channels are driving not just visits, but purchases? 
• How do historical campaign trends inform future strategies? 
• How can we optimize in-flight, not after the fact?

What the industry needs is a way to accurately bridge the gap between digital marketing and physical retail performance. The answer lies in attribution tools that blend offline and online insights — and here’s where AI and ML come into play.

Dig deeper: Measuring the invisible: The truth about marketing attribution

Bridging digital spend and offline performance

Although most consumers start shopping online, many still prefer to make in-store purchases​. Some like to compare shops, while others may want to check in-store availability.

All of this points to marketers needing a better method of campaign measurement. Measuring online impressions without considering offline actions is like watching only a movie’s first half. The real challenge is closing this gap.

The consumer journey has become increasingly complex, with shoppers making multiple touchpoints with a retailer or brand through online shopping, in-store visits and social media. An all-encompassing attribution pipeline is essential in connecting digital advertising to real-world outcomes. 

With so many factors at play, the methodology behind attribution needs to account for foot traffic, sales data and transactional data. Without this, marketers will not have the holistic understanding needed to get real-time insights on how campaigns are performing, to find out what is and isn’t working and make changes on the fly to ensure ad dollars are well spent.

In this crowded and competitive market, marketers need to ensure they have a holistic understanding of: 

• The customer journey at every stage of the shopping process, from first exposure to a brand to store visits.
• What is the final lever driving them to make a purchase. 

That’s where AI and ML can help. By analyzing historical data and real-time signals, these technologies help predict which online interactions drive in-store visits and purchases. The result? A more complete view of the customer journey, where you can track the full impact of your digital spend on offline revenue.

Dig deeper: Multichannel attribution: Understanding the metrics behind successful campaigns

AI/ML: Your vendor should already be using it

As a marketer, you shouldn’t have to think about how AI and ML are baked into your attribution tools. These technologies should already be working behind the scenes, analyzing vast amounts of data to help you understand what’s driving revenue — not just clicks. If your current attribution vendor isn’t already using AI to tie online marketing to offline results, it’s time to ask some tough questions. Here are a few to get you started:

• How does your solution link digital spend to real-world outcomes, like foot traffic and in-store purchases?
• Do you use a consistent methodology to measure both visits and transactions?
• How can your platform optimize campaigns while they’re still running, using real-time insights?

If your attribution partner isn’t using ML, be prepared for wasted spend. Without AI/ML, attribution models may fail to account for the complex nature of customer journeys, leading to misattribution of marketing spend. This results in suboptimal budget allocation and missed opportunities to optimize marketing strategies across touchpoints.

Dig deeper: 3 ways to use predictive analytics to make better decisions

The importance of real-time, in-flight optimization

Traditional attribution models often give us insights after the campaign is over. But by then, the budget is spent and any opportunity to adjust is long gone. AI and ML change the game by offering real-time, in-flight optimization. You can now monitor which channels and tactics are driving people to your stores and adjust your budget accordingly.

For example, if an ad performs better than expected by driving foot traffic to your stores, you can quickly shift more budget to that channel. You might also learn why a customer left your store without buying anything — perhaps the in-store experience was lacking, or the campaign’s message didn’t encourage them to purchase. This isn’t just about improving ROI — it’s about maximizing every marketing dollar by blending online engagement with real-world results. 

In today’s complex marketing landscape, attribution tools must provide a full view of your customer’s journey — from the time they click an ad to the moment they make a purchase in-store. AI and ML offer the key to unlocking these insights, but your vendor should already be doing the heavy lifting. If they aren’t, it’s time to ask the right questions and demand better solutions.

Dig deeper: AI and machine learning in marketing analytics: A revenue-driven approach