How AI and ML shut the attribution hole between advertising channels

As marketers devote more budget to digital channels, a surprising disconnect remains. Even though the majority of retail sales, for example, still take place in physical stores, most marketing efforts focus solely on tracking online metrics.

The challenge? Traditional attribution models fail to connect digital spend to real-world outcomes. To close this gap, marketers must embrace AI and machine learning to gain a complete picture of how their campaigns are driving both clicks and in-store purchases, enabling a deeper understanding of the true return on investment.

The CMO Allocation Dilemma

If you’re a marketing manager, chances are you’re focused on delivering results and spending most of your marketing budget on digital channels. But with 80% of retail sales in the United States taking place in physical stores and almost 80% of marketing budgets spent on digital channels, something is not adding up.

At first, this number surprised me, considering how much time people spend online. But from a consumer perspective, it makes sense. I don’t like buying shoes without trying them on. I also once walked into a store for one thing and came out with $200 worth of skin products.

The reality is that we spend money on screens while our customers walk through the doors. But if we measure clicks but don’t know what happens in-store, how can we definitively prove the impact of marketing?

It’s tempting to think that digital is king when it comes to attribution, but focusing exclusively on the web is a mistake. Many attribution models still fail to connect digital efforts to real-world actions like foot traffic and in-store sales. If your metrics are limited to clicks and impressions, you’re not seeing the full picture and, worse, you’re misdirecting your budget based on incomplete information.

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

Which channels generate not only visits, but also 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 information – and this is where AI and ML come into play.

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Balancing digital spend and offline performance

Although most consumers are starting to shop online, many still prefer to shop in-store. Some like to compare stores, while others may want to check in-store availability.

All of this indicates that marketers need a better way to measure campaigns. Measuring online impressions without considering offline actions is like watching only the first half of a movie. The real challenge is closing this gap.

The consumer journey has become increasingly complex, with shoppers establishing multiple touchpoints with a retailer or brand through online shopping, in-store visits and social media. A global attribution pipeline is essential for connecting digital advertising to real-world results.

With so many factors at play, the methodology behind attribution must take into account foot traffic, sales data, and transactional data. Without it, marketers won’t have the holistic understanding needed to gain real-time insights into campaign performance, discover what’s working and what’s not, and make changes on the fly to ensure that Advertising investments are well spent.

In this crowded and competitive market, marketers must ensure they have a comprehensive understanding of the following:

The customer journey at every stage of the purchasing process, from first exposure to a brand to in-store visits. What is the final lever that pushes them to make a purchase?

This is 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 total impact of your digital spend on offline revenue.

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AI/ML: your provider should already be using it

As a marketer, you shouldn’t have to think about how AI and ML are integrated into your attribution tools. These technologies should already be working behind the scenes, analyzing large amounts of data to help you understand what’s driving revenue, not just clicks. If your current attribution provider isn’t already using AI to link 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 connect digital spend to real-world results, like foot traffic and in-store purchases? Are you using a consistent methodology to measure both visits and transactions? How can your platform optimize campaigns currently running, using real-time insights?

If your attribution partner doesn’t use ML, prepare for unnecessary expenses. Without AI/ML, attribution models risk failing 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 all touchpoints.

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The importance of real-time optimization in flight

Traditional attribution models often give us insights after the campaign is over. But by then, the budget is spent and any possibility of adjustment is long gone. AI and ML are game changers by providing real-time, in-flight optimization. You can now monitor the channels and tactics that attract visitors to your stores and adjust your budget accordingly.

For example, if an ad performs better than expected in driving traffic to your stores, you can quickly allocate more budget to that channel. You might also find out why a customer left your store without purchasing anything: perhaps the in-store experience was lacking or the campaign message didn’t encourage them to buy. It’s not just about improving ROI, it’s also about maximizing every marketing dollar by combining online engagement with real-world results.

In today’s complex marketing landscape, attribution tools must provide a complete view of your customers’ journey, from the moment they click on an ad to the moment they complete a purchase in-store. AI and ML offer the key to obtaining these insights, but your provider should already be doing the heavy lifting. If not, it’s time to ask the right questions and demand better solutions.

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