As consumer journeys splinter across channels and devices, measurement must evolve from linear attribution to flexible, predictive models.
The buying funnel has appeared on at least one slide in every marketer’s go-to deck for decades. The story is always the same: awareness narrows to consideration, consideration narrows to conversion, and conversion leads neatly to loyalty. It’s tidy. It’s intuitive. But it’s increasingly inaccurate.
Even at its best, the funnel was an oversimplification, but now it has lost most of its punch. The explosion of media options and unlimited access to information has pushed shoppers far outside any linear path. They loop, pause, restart and splinter across devices, channels and moments of intent. Or they convert after a single exposure and disappear entirely. No single diagram can credibly represent them all.
In 2026, the most effective marketers will stop forcing consumer behavior into linear models and start building measurement systems that are non-linear, contextual and flexible. Or in other words: human.
Every purchase is shaped by a constellation of small choices. Desktop or mobile. App or web. Guest checkout or logged-in. Each decision subtly reshapes the path to purchase and the probability of conversion.
An existing customer doesn’t “enter” a buying journey the same way a first-time shopper does. A consumer in a mobile app behaves differently than one browsing on a desktop. Even the same person may follow entirely different paths depending on time of day, urgency, mood or outside influences that have nothing to do with advertising.
When marketers try to measure success through strictly linear attribution models, they optimize for the illusion of control rather than the reality of influence. That tension between what measurement promises and what behavior delivers is why so many teams feel uneasy about their data today.
Linear measurement breaks down because it assumes continuity that rarely exists. For years, the industry’s response was to double down on user-level tracking and try to reconstruct a complete journey from first exposure to conversion. Better IDs, longer persistence and more stitching across platforms were all meant to restore order to the funnel.
In some cases, that still works. Logged-in environments with strong first-party identity can reveal meaningful patterns in how customers move across channels and devices. But for many brands, a complete user-level view is either unrealistic or misleading at scale. Cookies expire. People switch devices. Privacy constraints limit what can be observed. Even when identity resolution is possible, it often captures only part of the story.
That reality is pushing measurement in a different direction. Instead of trying to follow individuals from start to finish, a more effective approach is to focus on which signals actually influence outcomes and how those signals perform across channels and environments. This is not a marginal adjustment in measurement strategy. It is a shift in how success is defined.
Non-linear measurement helps put user-level data into context. In logged-in environments, individual paths reveal how different experiences influence conversion likelihood, informing personalization, creative sequencing and on-site optimization. Those insights still matter.
Where user-level continuity breaks down however, aggregated methods take over. Tools that visualize nonlinear journeys, such as Adobe Customer Journey Analytics (CJA), showcase the full customer journey and allow marketers to explore it in deep granularity.
This is where marketing mix modeling (MMM) and incrementality testing have regained relevance as complements to modern analytics. These approaches focus on understanding how channels, messages, and environments influence outcomes at scale. Incrementality testing isolates causal impact, while marketing mix models place that impact in a broader, longitudinal context. Together, they help answer questions linear funnels cannot, including which channels truly drive incremental growth, how exposure intensity affects conversion probability, and where additional spend stops delivering meaningful returns.
In mature organizations, these methods reinforce one another. Incrementality results inform modeling assumptions, and models help determine where to test next. Measurement becomes iterative rather than deterministic, designed to learn and adapt rather than confirm a fixed path.
Non-linear measurement shifts the goal from reconstructing a perfect journey to predicting the next meaningful action. When marketers can anticipate what is most likely to happen next, measurement becomes an input for buying decisions in real time.
Those predictions can take many forms. They may estimate the likelihood that someone converts after a specific interaction, the probability of a repeat purchase, sensitivity to price or promotion, or responsiveness to a particular message in a particular environment. Each signal offers a different view into intent, and together they help marketers decide where to invest, when to engage, and how to tailor messaging across channels.
This approach fundamentally changes how media is planned and bought. Instead of allocating spend based on assumed funnel stages, marketers can adjust investment dynamically based on predicted outcomes.
These signals surface through patterns observed across many paths, which is why non-linear measurement depends on flexible data foundations and the ability to analyze variation. Cloud-based environments like BigQuery and Vertex AI in Google Cloud make this possible by allowing teams to store raw and modeled data side by side, test assumptions, and iterate as behavior evolves.
Custom modeling approaches, including solutions like Meridian, extend that flexibility by adapting measurement to the realities of each business rather than forcing teams into a fixed framework. The unifying factor is not the toolset itself, but the mindset behind it. Effective non-linear measurement accepts uncertainty, designs for it, and uses it to guide smarter media decisions over time.
Advertising has always tried to anticipate human behavior. It borrows from psychology, behavioral economics, and data science to influence decisions at scale. And it works — just not in straight lines.
That’s why flexibility matters. The most resilient measurement strategies don’t lock marketers into a single model, funnel, or attribution framework. They allow teams to combine user-level insight where it exists with aggregated understanding where it doesn’t. They adapt as channels evolve and behaviors shift.
This is the philosophy behind how Adswerve approaches measurement. Rather than forcing clients into one methodology, Adswerve helps organizations build architectures that support multiple lenses: user-level analytics, incrementality testing, and marketing mix modeling working together. The goal isn’t theoretical purity. It’s decision-making confidence.
Marketers who cling to funnels will continue to argue about attribution while consumer behavior moves on without them. Those who invest in flexible, predictive measurement will spend less time reconciling dashboards and more time acting on insight.
The future of measurement isn’t about finding the one true path to purchase. It’s about understanding how countless paths influence outcomes — and building systems agile enough to learn from all of them.
Ready to uplevel your measurement strategies? Connect with Adswerve’s team of measurement experts.