Marketing measurement needs different standards for stable and uncertain work
Marketing teams often apply precise, repeatable metrics to activities whose outcomes are neither stable nor easy to predict. That mismatch can produce misleading conclusions, unrealistic expectations and poor decisions, argues marketing strategist Kathleen Schaub. Her central distinction is between orderly processes, where inputs and outputs are consistent, and work shaped by changing conditions and multiple independent actors.
The distinction matters for marketers deciding what to measure and how much confidence to place in the results. A process may be complicated yet still predictable enough for tightly defined performance indicators. Campaigns, customer journeys and forecasts, by contrast, can be affected by customers, competitors, partners, influencers, governments and other forces that a marketing team does not control.
Where precise measurement fits
Stable work tends to have low variety, high certainty and known sources of variation. Its rules are clear, and the information needed to assess it is relatively easy to obtain and interpret. Constraints such as regulations, contracts and brand guidelines can help make processes more consistent.
Examples include checking brand assets against usage rules, managing email bounces and unsubscribes, following creative approval checklists, and sending event registration confirmations. Social posting schedules, statistical thresholds for A/B tests and routine budget rollups can also be assessed against established procedures.
For this kind of work, exact metrics can be useful. Teams can identify whether a required step was completed, compare performance against a consistent standard and improve efficiency. Schaub likens these processes to factory work: when the operating conditions are sufficiently controlled, managers can develop repeatable practices and forecast outcomes with greater confidence.
That logic can also support improvements to stable processes, including the use of new practices that incorporate AI. The point is not that precision is inappropriate in marketing, but that it is most defensible when the work itself is sufficiently consistent.
Why campaigns and journeys are different
Other marketing activities take place in conditions that are volatile, uncertain, complex and ambiguous—the VUCA framework associated in the article with the U.S. Army War College’s preparation for unpredictable battles. In business, these conditions can emerge when external circumstances keep changing, situations are novel, objectives compete, or independent actors affect one another.
Campaigns, customer journey management, B2B deal management, revenue forecasting, new product development and strategic planning are among the examples Schaub identifies as VUCA work. Marketing and related functions such as sales and customer support operate at the company’s boundary with the outside world, where customer behavior and other influences can shift.
In that setting, a customer journey may not follow a neat funnel, and a simple relationship between a marketing input and a business outcome may be difficult to establish. Customer behavior can vary substantially, while the people and organizations influencing it do not act as parts of a controlled process. Applying the same precision expected of a routine checklist or monthly report can therefore overstate what the measurements show.
This has direct implications for cross-channel attribution. Attribution attempts to make sense of interactions across a customer journey, but the journey may be irregular and affected by forces outside the measurement system. The source does not prescribe a particular attribution model; its broader warning is that teams should not mistake more detailed data for proof that an uncertain process has become fully controllable or predictable.
More data does not guarantee certainty
Marketing automation can help create data that makes customer journeys easier to examine. But data collection does not eliminate the uncertainty inherent in those journeys. Schaub cautions that marketers became overly optimistic about automation making marketing predictable; in her framing, additional data can instead provide further evidence of the complexity involved.
For marketers and executives, the practical challenge is to match measurement standards to the work being evaluated. Stable operational tasks can support precise, deterministic indicators. For campaigns, journeys and forecasts, measurement still has a role, but conclusions should reflect the limits imposed by changing conditions and interacting actors.
Recognizing that difference can help teams avoid treating every variance as a process failure or every observed association as a dependable forecast. It also encourages leaders to ask whether a metric describes a controllable routine or an outcome emerging from a less predictable system. The answer should shape how results are interpreted and what decisions they are used to support.
