Attribution is the thorn in every marketer’s side. Leadership wants to see proof of ROI, meanwhile, you’re fighting the two-headed monster of cookie restrictions and tracking regulations. Because if a user opts out of tracking on your app and rejects optional cookies, what are you left with?  

With the rise of AI and machine learning (ML), there’s hope for slaying the attribution dragon once and for all. Marketers may finally be able to accurately match conversions to the marketing channels that are driving them.

So, what makes ML in marketing attribution so powerful? And how will AI and ML shape the future of attribution for modern marketers? Read on to find out.

What is marketing attribution?

Marketing attribution identifies which marketing channels, campaigns, touchpoints, and user actions are driving the most value for a business. 

Attribution helps marketers understand the impact of their efforts. It can answer questions like “What’s working and what’s not?” and “Which marketing channels are driving the most ROI?” Learning which parts of your marketing are most effective lets you optimize resources and build repeatable frameworks for success. 

How ML fuels better marketing attribution 

Marketing attribution isn’t always straightforward, given the complexity and speed of today’s customer journeys. For example, the path from first touch to final conversion may involve multiple devices, various platforms, and breaks between visits. 

These cross-channel and cross-device journeys make it tough to attain a complete view of the customer journey. And for lack of a better system, credit often goes to the last touchpoint before purchase. 

The loss of third-party cookies (scheduled for deprecation in 2024) will make the process even more difficult, not to mention potentially inaccurate. But machine learning is emerging as a new solution. 

Machine learning techniques play a crucial role in attribution modeling, especially in a cookieless world. The algorithms can analyze huge amounts of zero- and first-party data and then learn from them independently, without being explicitly programmed to. 

Then, the ML algorithms identify patterns and use statistical modeling to assign credit to each touchpoint. And since the algorithms also adapt and learn from data, the attribution model will improve over time. 

For example, Scuba’s ML-powered decision intelligence platform ingests and processes over 100 terabytes of data per week. By sorting through billions of events and micro-events in seconds, the algorithm can spot behaviors that typically lead to a conversion, such as logging in five times per week or utilizing an advanced feature. 

With ML marketing attribution, suddenly brands have the 360° view and the real-time insights they need to connect the dots of the customer journey. 

Benefits of using ML in marketing attribution

Here’s how using ML in marketing attribution can benefit your business, even without third-party cookies. 

1. More accurate analysis

Because they analyze so much, so fast, and with such accuracy, ML algorithms can spot patterns and trends that humans can’t. And when paired with behavioral analysis data that helps weave the customer journey together, you get more accurate analysis and better decision-making.

2. Better customer journey mapping 

By fusing customer data from various sources and channels, ML-driven attribution models help marketers build richer user personas. And by processing billions of micro-events and behaviors in real time, you can identify the most significant touchpoints in the customer journey. 

Finally, the entire process is automated. So, anytime you check-in, you’re getting the most up-to-date information.

3. Adaptability 

When you have in-the-moment feedback on your marketing performance, you can adapt quickly to changing circumstances. The system is based on continuous learning, which contributes to a culture of ongoing improvement and iteration. 

4. Future-proofing your marketing strategies

AI and machine learning are here, ready or not. And businesses that know how to use it will be ahead of those that don’t. ML-driven marketing attribution helps you stay on top (and ahead) of trends and user behavior, make data-driven decisions, and adapt your marketing strategies for the future. 

5. Optimized marketing efforts

ML in marketing attribution lets teams use data visualizations and custom KPI dashboards to quickly see the impact of their efforts. For example, which marketing channels generate the highest ROI, which campaigns drive the most conversions, and which tactics work best within a channel. 

Once you know all that, you can optimize your content creation efforts accordingly, like a waterfall of marketing efficiency.

6. Enhance budget allocation 

Knowing the impact of your marketing efforts makes budget allocation much easier and more effective. When you can focus only on the channels and tactics that deliver the best results, you reduce waste and pump up ROI.

The future of marketing attribution with AI and ML 

The days of using predetermined formulas or rules to assign conversions are numbered (not that we’ll miss them). The global marketing attribution software market is projected to expand 13.5% annually over the next several years, with AI and ML technologies fueling that growth. 

As more businesses struggle with growing privacy constraints and dwindling third-party data, ML-based attribution models are stepping in to fill the gap. As more precise analysis leads to better results, more and more brands will adopt ML-based attribution models. 

If your company is exploring how you can incorporate AI and ML into business workflows, you’re not alone. Research found 25% of U.S. companies are using AI and 43% are exploring its potential applications. Another statistic says 77% of businesses have already adopted AI or have an adoption plan.

It’s safe to say, the future of ML in marketing attribution looks promising. We at Scuba are excited to see what the future brings, and what new problems will be solved next with technologies and use cases.  

Want to learn about successful audience measurement with machine learning? Download the guide now: The Future of Cross-Media Measurement in a Privacy-First Economy.