More Data, Less Confidence: Marketing’s Measurement Problem

By Matt Spiegel, EVP, TruAudience Growth Strategy

One of the quiet ironies of modern marketing is that the more measurable it has become, the harder it has become for companies to agree on what performance actually means.

Most marketing organizations have a sharp view of how targeted audiences interact with a given channel, but struggle to connect those insights into a coherent view of demand generated, demand captured, and most importantly, incremental marketing value created.

For too many companies, measurement systems remain organized around media architecture rather than consumer behavior, with every team seeing only part of the truth.

The fix is not simply collecting more data or forcing agreement around “one source of truth.” It is also not enough, but it is a requirement to build the connective intelligence (i.e. dashboards) that lets marketing, finance, product, and commercial leaders see the same metrics. Measurement is about much more than “reporting”; done well, effective marketing measurement is an analytics, governance, strategy, and capital allocation endeavor.

The Data Confidence Gap

Marketers have never had greater access to data—or less confidence in what that data is saying.

More than half of marketers say their confidence in marketing measurement hasn’t improved over the past year; 14% say it has declined, and nearly one-third report that measurement uncertainty has already put 11% to 20% of marketing budgets at risk.

The problem is compounded by cross-channel measurement blind spots (data gaps caused by siloed and incomplete data sets). Seventy percent of marketing leaders say those blind spots prevent them from accurately proving full-funnel impact, while 69% say data blind spots in walled gardens limit their ability to evaluate marketing effectiveness.

Too often the perceived fix to measurement’s ails is to come up with a single metric of success. Like many simple things, this answer isn’t wrong altogether, but it’s insufficient.

Different marketing initiatives need different primary success criteria. Front line marketing teams need to be thinking differently about marketing effectiveness than the CMO and their C-suite peers.

What does work is a connective framework, with C-Suite alignment, that enables the entire organization to know what matters when, and to have that framework built on top of trusted data sources.

The Cost of Competing Truths

The consequences of the fragmented approach to measurement show up in the decisions organizations struggle to make.

Imagine asking the CFO and Chief Revenue Officer to report quarterly revenue, only to discover that each arrived with a different number. No CEO or board would accept competing versions of financial performance.

Marketing can be held to the same standard. The issue is not whether marketing creates value; it is whether the organization can explain that value with enough consistency to make confident investment decisions.

Industry research demonstrates that despite the potential, “consistency” remains a key challenge: 34% of marketers cite unreliable measurement as a major challenge, 33% point to conflicting data, 67% of marketing leaders reported having siloed or fragmented data systems, 42% cited incomplete or missing data, and 31% struggled with data latency or timeliness.

Most organizations want to achieve marketing measurement excellence. But these real and not glamorous data and technology issues stand in the way. The result, marketing credibility is questioned, and the consequence is less effective marketing and less growth.

The Foundation Beneath Every Metric

The foundation beneath every metric is not a dashboard. It is customer intelligence infrastructure: the ability to connect people, behavior, interactions, and outcomes.

This requirement is as true and differentiating for a marketing organization as it has ever been. AI enables lots of learning, but it isn’t a given that marketing organizations actually capture that learning.

The industry now has “walled gardens” and “black boxes” both on the buy and sell sides of the industry. These closed ecosystems offer “results” based on their definitions. That raises a strategic question for marketing organizations: are you building your own durable intelligence about customers, or renting performance from organizations that keep the learnings and whose incentives may not fully match yours?

Marketing greatness is not defined by who collects the most data, but by who can connect it into the clearest, most actionable understanding of the customer—and use that understanding to make better business decisions that scale in effectiveness over time.