Cracking the Upper Funnel Puzzle: How Brands Are Using Auto-Regressive Methods in MMM to Invest with Confidence

In today’s fragmented and privacy-conscious marketing landscape, the ability to measure the full impact of upper-funnel investments has become a defining competitive advantage. While performance marketing has historically enjoyed clear attribution pathways, brand-building activities have often suffered from murkier measurement and underinvestment. Today, best-in-class marketers are leveraging advancements in Marketing Mix Modeling (MMM), including the use of auto-regressive methods, to more accurately quantify upper-funnel contributions and make smarter, more confident investment decisions.

Best-in-Class Measurement Frameworks and the Evolving Role of MMM

At its core, MMM triangulates multiple data sources to estimate how different marketing and non-marketing factors drive sales and other key outcomes. While incrementality testing and experimentation provide valuable causal validation, MMM remains the gold standard for holistic, channel-agnostic measurement across both online and offline activities.

In an era of heightened privacy regulation and third-party cookie deprecation, traditional user-level attribution methods like Multi-Touch Attribution (MTA) have lost much of their utility. Without deterministic tracking, these models struggle to maintain accuracy and scalability. As a result, marketers are turning to methods that rely on aggregated data and statistical inference, with MMM leading the way in offering a durable and privacy-compliant solution.

However, traditional MMM approaches have their limitations, particularly when it comes to upper-funnel channels. Many models focus predominantly on shorter time-to-conversion windows, which inherently biases results toward lower-funnel activities like paid search or affiliate marketing. Without adjustments, MMM can systematically undervalue the true long-term impact of upper-funnel media.

The Challenge of Short-Term Windows: Understanding Theta

One of the key parameters within MMM is "theta," which represents the adstock decay rate or how long the effects of advertising persist after the initial exposure. In many models, theta values are short, reflecting the immediate sales uplift seen from performance-driven campaigns. Yet upper-funnel media, such as Connected TV, programmatic display, and traditional broadcast, operate on a much longer influence horizon.

By overly emphasizing short-term response, standard MMMs can fail to capture the sustained momentum generated by brand-building efforts. This not only leads to underinvestment in critical upper-funnel activities but can also create an unhealthy overreliance on lower-funnel channels that are easier to measure but harder to scale efficiently.

Capturing Momentum: The Role of Adstock and Auto-Regressive Variables

Savvy marketers understand that advertising’s influence is not a one-and-done event. Exposure builds over time, creating what is known as 'adstock', which refers to the accumulated awareness and brand equity that continues to influence purchase behavior well beyond the initial impression. Capturing this effect accurately is essential for valuing upper-funnel investments.

Auto-regressive methods provide a powerful solution. By incorporating lagged dependent variables into MMM models, brands can account for the cumulative impact of past advertising on current outcomes. In essence, they recognize that today’s sales are not only influenced by today's advertising but also by advertising exposures weeks or even months prior.

This methodology transforms MMM from a snapshot into a dynamic, momentum-based model, better reflecting the realities of brand-building and upper-funnel effectiveness.

From Theory to Practice: Attributing Auto-Regressive Effects Back to Media

Identifying that an auto-regressive effect exists is just the first step. The true value comes from attributing it back to specific media investments. To do this, brands combine insights about channel-specific theta values with the scale and spend levels of each channel.

For example, if Connected TV has a longer theta (slower decay) compared to paid search, and if CTV investment was significant during the relevant period, a disproportionate share of the auto-regressive uplift can be confidently assigned to upper-funnel channels. This more nuanced attribution enables marketers to accurately credit brand-building media for its contribution to long-term sales momentum.

Case Study

A recent Marketing Mix Model delivered by M-Squared for a leading aesthetics brand revealed that a significant share of sales influenced by upper-funnel channels occurred well beyond the standard 2–6 week conversion window. When the measurement window was extended to a full 15 weeks, the brand uncovered a 222% increase in attributed revenue from these upper-funnel channels. This insight materially changed their understanding of performance and ROAS, ultimately giving them the confidence to continue investing in brand-building media as a key driver of long-term growth and profitability.

Unlocking Greater Media Value and Strategic Shifts

The impact of this enhanced modeling cannot be overstated. Brands that incorporate auto-regressive techniques into their MMM frameworks often find that upper-funnel channels deliver far more value than previously recognized. Instead of appearing inefficient or unscalable, brand media emerges as a foundational driver of sustained growth.

This realization frequently leads to strategic budget reallocations with a greater willingness to invest in awareness-driving channels and a more balanced portfolio between short-term activation and long-term brand building.

Moreover, by understanding the true carryover effects of media, marketers can set more realistic expectations for campaign payback periods, optimize flighting strategies, and build more resilient marketing plans that aren't overly dependent on immediate returns.

Conclusion: Embracing a More Complete View of Marketing Effectiveness

As the marketing landscape becomes more fragmented and consumer journeys more complex, the ability to rigorously and precisely measure upper-funnel effectiveness is no longer optional. It is essential for long-term growth.

Auto-regressive methods within MMM offer a sophisticated yet accessible pathway to unlock this critical insight. By embracing these advanced techniques, brands can better capture the full value of their marketing efforts, invest with greater confidence, and build sustainable growth engines that outperform over the long term.

In a world where short-termism often dominates, the marketers who master the upper-funnel puzzle will be the ones who lead their brands to lasting success.