Future of Marketing: Goodbye Vanity, Hello Clarity in Marketing Attribution

The customer journey gets smarter as AI revamps marketing metrics.

This is the second in a series of articles dedicated to AI’s impacts on the future marketing organization. The first article examined AI’s potential impacts on agencies.

Many sales and marketing executives complain about the lack of clarity about which marketing actions drive customer interest and conversion. Most attribution modeling uses first or last-touch methods for lead conversion, while individual tactics often use clicks, engagement rates, or worse, engagement to measure effectiveness. A sea-change caused by the move from third-party cookie data to first-party data will force more complete customer journey analysis and attribution modeling to evaluate entire marketing programs better. 

While generative AI dominates headlines, combining machine learning and predictive analytics may win the show for marketing. Forced by the move toward first-party data, customer journeys, and personalization, expect a strong movement toward better analytics. As part of the larger model, Generative AI can help marketers query complex analytics engines and derive insights on demand.

Precision empowers better optimization of marketing budgets and drives more efficient marketing programs with higher yields. For example, better customer data highlights the power of customer reviews on a review aggregator. This secondary content site factors later in the customer journey’s decision to buy or a particular industry site’s editorial coverage.

In its 2023 CMO study, Duke University noted the impact AI has on some content but says there is room to grow on analytics to inform decisions. “Marketers can increase the use of AI to improve marketing ROI by optimizing the content and timing of digital marketing, for programmatic advertising and media buying, for predictive analytics for customer insights, and for targeting decisions,” Duke University Fuqua School of Business Marketing Professor Christine Moorman noted.

The Need for Clarity

It is incredible how many data-centric CMOs and marketers lack clarity about which of their programs work. Pressed for answers, they find their data is disorganized, and the tools used to analyze it, from CRM platforms to analytics dashboards, are antiquated. So they continue because relying on vanity metrics and vague indicators and an overreliance on first or last-touch attribution. Really, there is little choice based on the vast majority of existing tools.

Based on the cookie data era of digital marketing, these models assign too much weight to first or last touch. All other actions, from sales-fueled empty content to sexy “me, too” influencer marketing campaigns, go through waves of funding and retraction, subject to skepticism.  

Is the skepticism merited? All marketing actions deserve to be analyzed for effectiveness. Based on cookie-based attribution alone, the variability and resulting de-prioritization is logical but inaccurate. As noted, most marketers don’t know with certainty what compels their customers to start their journey, trust the brand, or act. As such, first and last-touch attribution models are antiquated.

External change is forcing the matter. Data protection laws like General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) have forced most browsers to shed third-party cookies, forcing digital marketers into a new territory. With third-party cookie data becoming much less relevant in the immediate future, marketers must focus on unifying their first-party data to build better, more effective personalized customer journeys and experiences.

Unification includes first-party data, procured second-party data, and vast troves of unstructured content and feedback from and about customers. Given the amount of unstructured data and AI and its potential to better analyze vast swaths of customer actions, attribution modeling needs to evolve. Building a more complete customer journey analytics profile now takes strategic urgency.

With a complete customer journey analytics perspective,  “role player” marketing tactics can receive better attribution for helping to convince customers to work with a brand. AI-fueled clarity will fuel change in a way only large amounts of data and robust analysis can provide. Sunlight reveals all.  Actions that may be over-credited for their impact will be revealed for their lack of impact.

For some marketers, more accountabilities will lead to better decision-making and more robust program executions. For others, however, it will become painful and lead to dramatic actions in the worst cases.

Vanity metrics like page rank, impressions, and empty click-throughs are likely to become devalued in the new AI environment. If they retain value, they do so in a unified customer journey, not as disaggregated analytics that provides glimpses into the funnel but fail to illustrate full impact.

The Full Customer Journey

ML and generative AI models built to identify, unify, predict, and analyze the entire customer journey are increasing the value of marketing metrics. They provide holistic views of customer data, allowing brands to understand better what triggers movement to transactions and encourage additional purchasing.

Some brands have already implemented full customer journey analytics, usually more prominent brands that can afford large custom data science implementations. For example, Nike used AI to analyze customer data gathered from their branded app usage patterns, behavior on social media platforms, and previous purchase history.

These unified customer journeys are the exception to the rule. The smaller the company, the more challenging unifying data has proven to be. Integration amongst analytics providers and sources is the most significant barrier to advanced marketing metrics. Most companies implement Google Analytics, create a few dashboards in their CRM, and pray they can make sense of it all.

This challenge often finds a root cause in data hygiene, which is usually an afterthought for marketing organizations. This haphazard approach to data won't fly once brands attempt to build advanced AI analytics applications. Piecing the data puzzle together becomes difficult when data is unstructured, misplaced, and even corrupted by rushed marketing actions.

The faster data sources like customer data, Google and social network analytics, CRMs, etc., are integrated into a predictive AI model, the better. The goal is to provide as much of the whole data source picture as possible.

Even with clean, comprehensive performance data, marketers can find it challenging to establish and act on a unified view of the entire customer journey. However, recent AI advances and a surge in marketing AI startups are promising for marketing organizations. Now, it is a question of correctly finding and integrating the right tool.

This trend should start building traction slowly, but the market will turn as more companies experience success using AI to analyze their customer journey. Expect the AI customer journey to pick up steam quickly and become a priority for marketing organizations in 2025.

Conclusion: AI-Driven Customer Journey Attribution Benefits

One expected impact of an AI customer journey analysis will be a movement toward data-driven attribution modeling. The unified customer journey allows AI algorithms to analyze all customer touchpoints and assign credit to the actions that cause movement toward transactions.

Machine learning will drive better attribution by processing incredible amounts of unstructured and, as the year progresses, uncookied data. Pattern recognition, statistical modeling, and improved performance through supervised learning will fuel more robust attribution based on each touchpoint's ability to generate transaction momentum.

Brands will be better able to see their entire journey and the impact of each action. This is where marketing gets exciting for forward thinkers. Cutting actions with little impact will yield a budget to double down on successful actions or introduce new marketing elements to the customer journey.

Another massive benefit of utilizing a full attribution model is understanding customer behavior change much earlier in the process. Further, knowing where change is happening in the journey allows marketing organizations to adapt without unnecessarily disrupting programs that are functioning well.

Brands concerned about the move to first-party data should focus on data governance, cleaning, and unification to better understand their customer journey. Gathering intelligence about vendors and platforms who can help extrapolate the data is useful. In many cases, existing CRMs offer some form of customer analytics, however incomplete.

Several platforms, such as G2, track leading software providers in the customer journey space. Brands may need to hire consultants and service providers to help them clean, integrate, and unify their data and marketing analytics. CognitivePath does not endorse any of these platforms or the solutions and service providers they recommend. To learn more about CognitivePath’s advisory services, visit our website.