Winning executive trust in the move beyond marketing attribution

A tough conversation is unfolding between marketing leaders and the C-suite. CMOs are realizing that static, deterministic metrics like attribution create false narratives. Many fear CEOs will blame or shame them for revealing that new insights invalidate much of their earlier reporting.

The fear is real — but the bigger risk is staying trapped in bad metrics once you know they don’t work. It’s better to embrace marketing’s complexity and adopt measurement methods that reflect how customers actually buy.

I asked marketing experts who have guided companies through this transition to share their best strategies for earning executive buy-in. Here’s what they recommend.

Position the shift as a strategic evolution, not an admission of failure

Marketing measurement exists to bring data-driven clarity to what’s working and what isn’t. This insight enables smarter budget decisions and strengthens the customer journey to drive more revenue. Marketing automation made the first meaningful move toward this goal by bringing data into marketing decisions. It also offered a partial view of the steps customers take on their way to purchase.

But with attribution, companies pushed data too far. It tries to make data do more than it can and, in the process, spins false narratives. Customer journeys look less like a linear funnel and more like a child’s scribble.

Markets are complex systems, not predictable machines attribution tries to mold them into. As a result, businesses often make worse marketing decisions with attribution than without it — creating more pain than necessary.

Reasons to move beyond attribution include the following:

Attribution requires predefined rules (e.g., first touch, last touch, multitouch or time decay)

In the messy real world, it’s impossible to know for certain in advance what customers will do. Markets are semi-predictable in the way weather has trends and patterns, and marketers can use data to gain insight into these — but nothing is guaranteed in the way attribution implies. Interactions among billions of people and enterprises create feedback loops within market systems, introducing uncertainty into every situation.

Attribution favors what is measurable, not what’s important

Not every touchpoint can be tracked. The influence of brand, PR, word of mouth and loyalty is completely missing. Lower-funnel channels that drive clicks — such as email and paid search — receive more credit than upper-funnel engagement, even though growing evidence shows that early interactions are often more influential.

Attribution obscures marketing’s interdependencies

Marketing can’t be reduced to the sum of its parts. When you buy a car, how can you attribute portions of the price you paid to the billboard you saw last month, the recommendation from your brother-in-law two weeks ago, the TV ad last week, the Instagram ad the day before and the location of the dealership? 

Attribution hinders marketers from discovering the real contributors to revenue uplift. Simplistic conclusions drawn from complex situations lead to faulty decisions.

Dig deeper: It’s time to move on from multi-touch attribution

Document your full, customer-centric buying process 

To get the most accurate story, you’ll need to collect a broader range of data. The customer buying process is far more expansive than most companies track. Once executives see the bigger, more complex reality, they’re less likely to trust short-term, simplistic metrics like attribution.

When documenting, focus on three areas that are often overlooked.

Make sure the buying process is truly outside-in

Your sales funnel is not the customer journey. The funnel represents an internal process, and while measuring it can reveal where waste occurs, it doesn’t help you understand actual customer journeys or markets.

Extend the buying process as completely as you can

Dig into what’s hidden. A company’s sales process may overlap with only a third — or even less — of what customers actually think and do. The journey can span years and involve multiple buying cycles, double-digit buying teams and hundreds of interactions. Pay close attention to identifying moments of conversion.

Watch for gaps

Organizational silos cause even the smartest teams to miss customer journey steps that fall between functional boundaries. Journeys often stall when customers need content that isn’t captured in marketing metrics (because it doesn’t produce leads) or sales metrics (because it doesn’t close deals).

Consider this documentation — and the data collection behind it — a work in progress. Early versions may be incomplete and imprecise, but as your data improves and analytics mature, you’ll refine the picture until it becomes a useful GPS for a constantly changing, probabilistic market.

Dig deeper: How attribution masks what’s actually driving growth

Connect new marketing metrics and methods to the business 

Metrics help coordinate functions across the company, and evolving marketing metrics will inevitably affect other areas. Before finalizing your plan and presenting it to the CEO, consult the CFO and CRO about how current methods work for them.

Marketing measurement is moving toward analytical approaches used in probability-affected environments, such as economics. These methods can uncover trends and causes in messy real-world systems in ways attribution cannot. However, these insights differ from what companies are used to. You’ll need to discuss how these changes affect business decision-making and how new marketing data can support more accurate financial guidance.

Today, the leading method for advanced marketing analytics is marketing mix modeling (MMM), a form of multivariate regression analysis. MMM explores potential causal relationships among multiple factors simultaneously — a capability essential for marketing. 

Data specialists look for relationships between a single dependent metric (for ROI, this would be the financial “R” marker) and two or more independent variables (in marketing, a range of possible contributing tactics). Using this method, analysts identify the best fit that connects current outcomes with past interactions.

Causal inference is another advanced option. It goes beyond the associations and patterns that MMM can reveal, identifying more accurate and persistent relationships. The use of AI and related technologies is making these methods increasingly affordable and accessible.

Dig deeper: Why causal AI works when other forecasting models fail

Conduct a quiet proof of concept (POC) before rolling out new methods

Once the marketing team has a few months of experience with the new methods, they’ll be better prepared to answer the many questions the CEO and CFO will inevitably have. 

Consider running a quiet POC in parallel with existing methods for a period of time. Because buying cycles often include time lags between activities and the outcomes they influence — sometimes months or even years — building in several months of data will improve clarity.

Empathize and keep the conversation going 

As you develop new capabilities, continue the dialogue with the CEO and CFO. Understand what they’re trying to achieve and how your changes affect their goals. Don’t assume you know their perspectives — and don’t force alignment. The CEO will have the final say, so their support is essential to the journey.

Marketing metrics are best used as fuel for collaboration, not as performance scores. This shift takes time, and everyone will learn and adapt along the way. Most will also need occasional reminders of why the change matters. The old deterministic mindset runs deep — and it can be hard to let go.

Dig deeper: The smarter approach to marketing measurement

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