Marketers are often at the mercy of platform-reported metrics, which tend to overstate outcomes due to their reliance on last-touch or view-through attribution. This is where advanced attribution multipliers come into play. These correction factors allow businesses to adjust platform-reported conversions or revenue figures based on a better estimate of incrementality—i.e., what portion of those results were truly driven by media, rather than what would have happened anyway. BUT - the value of multipliers extends beyond just a number—they become powerful only when used with the right context: their source, time of validity, and interpretation. “For example, if Facebook reports 10,000 conversions in a month and the multiplier is 60%, that means an estimated 6,000 of those were truly incremental.”   What Are Advanced Attribution Multipliers? An attribution multiplier is a ratio applied to platform-reported conversions (or revenue) to estimate the incremental outcome: Incremental Conversions = Reported Conversions × Multiplier Note: The equation above is most commonly used for orders and revenue, but the same logic applies to other KPIs depending on the business model. Many advanced attribution frameworks also provide multipliers for upper- and mid-funnel outcomes such as add-to-cart events, new user signups, app installs, or lead submissions. In each case, the multiplier adjusts platform-reported or base attributed numbers to reflect the share of those actions that are truly driven by media, versus those that would have occurred organically.   These multipliers are derived from various methodologies: Geo match market testing Marketing Mix Modeling (MMM) Industry Benchmarks Each source varies in reliability and application. But across the board, multipliers help marketers better understand which dollars are driving growth versus which are merely catching conversions already in motion. “In the data-driven world of performance marketing, understanding the true impact of media is critical.”   Not All Multipliers Are Equal: A Hierarchy of Trust 1. Geo-Tested Multipliers – Gold StandardGeo-experiments, where similar markets are split into test and control regions, offer the highest confidence in determining causality. They allow for controlled A/B testing in the real world and produce multipliers grounded in actual lift.If you have a geo-tested multiplier for a channel in a given timeframe, it should be your default—it’s the cleanest read on incrementality. 2. MMM-Derived Multipliers – Model-Based, Directionally StrongMarketing Mix Modeling uses historical data and regression techniques to estimate the contribution of each marketing input. While MMM cannot isolate causality like geo tests, it captures macro trends, lag effects, and diminishing returns, providing a comprehensive view across time and channels. MMM multipliers are calculated by comparing modeled conversions to platform-reported ones over the same time window. They're especially useful when geo tests are unavailable or infeasible. 3. Industry Benchmarks – Useful, but GeneralizedWhen bespoke modeling isn’t available, industry benchmarks provide a fallback. These are usually published by attribution vendors or consultancies based on aggregate data across brands and verticals. While they offer direction, they are not tailored to your audience, creative, or media mix—so should be treated as temporary stand-ins. Understanding Multiplier Values: What High and Low Mean Not every multiplier carries the same implication. Here’s how to interpret different multiplier ranges: Multiplier Interpretation Implication > 90% High incrementality Nearly all reported conversions were driven by media. Strong evidence of true demand generation. 50% - 80% Moderate incrementality Some conversions would have happened anyway. Marketing is still effective, but with mixed influence < 40% Low incrementality Most conversions were not driven by media. Channel may be harvesting intent (e.g., retargeting, brand search). High multipliers typically show up in top-of-funnel prospecting efforts, new product launches, or under-penetrated markets. Low multipliers are more common in retargeting or brand-heavy tactics, where users are already far along their purchase journey.Low multiplier ≠ bad channel. It may still serve a strategic, protective function—even if not driving net-new demand.   The Time-Bound Nature of Multipliers One of the most overlooked aspects of multipliers is that they are not static truths. A multiplier calculated in March may not hold in July due to changing: Seasonality Creative performance Competitive landscape Budget levels Consumer sentiment Over time, the confidence in a multiplier drops. What was once a solid read becomes stale if not revalidated.That’s why advanced attribution systems should treat every multiplier as a time-stamped data point, not a universal constant.   What Happens During Peak Seasons? A common question marketers ask is: Should we expect higher or lower incrementality during major shopping events like Black Friday or Memorial day? Surprisingly, the answer is often lower incrementality, even though spend is higher.   Why? Natural demand is elevated — consumers are going to buy anyway, even without ads.Attribution platforms still claim credit, often inflating the perceived impact.Marginal lift per dollar drops — meaning marketing looks productive, but the true incremental effect shrinks.   In such periods, you might find that: A channel’s reported ROAS goes upBut the incremental ROAS, when adjusted by the multiplier, remains flat or even dropsTherefore, smart teams bake seasonality into their MMMs or run holiday-specific geo tests to capture this nuance. Using off-season multipliers during peak sales periods will lead to over-attribution and misinformed investment decisions.     Putting It All Together: A Framework for Applying Multipliers To operationalize attribution multipliers effectively: Rank by source: Use geo test multipliers > MMM > industry standards. Tag with metadata: Include time period, confidence level, and source. Segment by tactic: Separate multipliers for prospecting vs retargeting, branded vs non-branded search, etc. Refresh frequently: Aim for monthly or quarterly recalibration. Use triangulation: Blend methods where needed and flag where multipliers are stale or missing.   Final Thoughts Attribution multipliers are not just mathematical tools—they are decision-enablers. When applied thoughtfully, they shift the focus from vanity metrics to business outcomes. But this power comes with responsibility: to treat multipliers as contextual, time-bound, and source-sensitive.In an increasingly privacy-restricted and signal-fragmented world, marketers must lean on disciplined measurement practices. Using advanced attribution multipliers correctly is one of the clearest paths toward media investment that’s not just data-driven—but evidence-backed.  

“Just because the impact of brand marketing isn’t immediate doesn’t mean it’s immeasurable.”   Start Here: Marketing Needs an Accounting Mindset Why modern marketing mix models are brand marketers’ secret weapon. Before diving into modeling, brand teams need a structured way to understand how marketing contributes to business outcomes. That’s where a marketing accounting mindset comes in—reframing media investments as assets, not just expenses. Rather than splitting marketing into “brand” vs. “performance,” this framework encourages teams to: Align spend with funnel stages (awareness, consideration, conversion) Track upper-funnel KPIs like: Engaged sessions Branded SEO growth Organic search traffic View-through conversions Structure budgets based on investment horizon—short-term activation vs. long-term equity building   This accounting logic sets up advanced modeling to do what it does best: quantify lagged, layered impact across channels and time. “The most effective brand marketing isn’t loud—it’s layered, long-term, and lagging.”   Why Traditional Attribution Fails Brand Marketers Upper-funnel tactics like TV, podcast, influencer, or sponsorships often don’t convert immediately. This creates tension when leadership expects every dollar to show direct ROI. Conventional tools like last-click attribution or basic lift tests fall short. They ignore: Time lags in customer behavior Synergy between upper-funnel and bottom-funnel tactics Multi-region or multi-product effects   That’s where advanced modeling—especially Marketing Mix Modeling (MMM)—comes in.   A Modern Modeling Stack for Brand Leaders Auto-Regressive ModelingAccounts for lagged effects from brand media—TV, influencer, etc.—so ROI can be captured beyond the first week. Hierarchical Modeling Disentangles effects by channel, region, or KPI. This is critical when modeling multiple upper-funnel metrics like Brand SEO, Engaged Sessions, or Top-of-Funnel Lead Quality alongside downstream conversions. Bayesian Priors Incorporates business rules and media logic (e.g., “TV has a delayed effect”) to ground the model in reality and avoid noise. Scenario TestingAllows brand teams to simulate what-if planning: What if we increase podcast spend by 20%? What happens if TV overlaps with retail? HVA (High-Value Action) Modeling Used to track upstream actions—newsletter signups, product views, social saves—that are correlated with downstream sales. These are critical for modern MMM.   “We didn’t just justify the sponsorship—we optimized the timing and messaging for even better lift.” Mary Maijer CMO, PowerHeal   Case Study: PowerHeal’s Bold Sponsorship Play When wellness brand PowerHeal evaluated a major sponsorship opportunity, they needed proof that the investment would pay off. Using advanced MMM techniques: HVAs like branded search and site engagement were tracked as early signals Auto-regressive lags revealed ROI peaking in Weeks 3–5 post-exposure Hierarchical models captured variation by region and media type   The result? Confidence to move forward—and the campaign exceeded ROI targets within one quarter. Chart 1 – Marketing Impact Curve Line chart showing lagging impact of TV vs. immediate spike from performance media (search), with cumulative lift over 6–8 weeks.   Chart 2 – Multi-Channel Impact Stagger   Your Brand Modeling Playbook You don’t need to build a model in-house—but you do need to frame the right questions. 1. Map Your MediaAudit all brand spend: linear/CTV, podcast, influencer, sponsorship, content. Include owned media and reach metrics. 2. Define Smart KPIsGo beyond sales: Brand search volume SEO visibility Engaged site sessions HVAs by media channel   3. Structure Your DatasetInputs: Weekly spend Media delivery (GRPs, impressions) Promotional activity External variables (seasonality, competitors) 4. Model Lag IntentionallyUse auto-regressive logic to quantify long-term brand lift—not just week-1 conversions. 5. Simulate StrategyTest how layering channels (e.g. TV + influencer) improves ROI vs. siloed spend. 6. Get Executive Buy-InModel outputs should be translated into visuals and business cases, not just statistical reports.   “Brand vs. performance is a false choice. Great marketers model both—and unify them.”   Final Word: Brand Building, Modeled Right Modern marketing is more measurable than ever. With a financial framework to define success and advanced tools to model long-term value, brand marketers can operate with clarity, confidence, and control. When you model brand right, you don’t just measure marketing—you multiply its impact. Ready to prove brand ROI with confidence? Let’s build your model. → Contact MSquared

In a world where marketing investment is scrutinized more than ever, CMOs are under constant pressure to justify spend, prove effectiveness, and drive both brand equity and revenue. Yet the industry’s dominant measurement tools have long struggled to capture the true value of upper and mid-funnel activity. Brand trackers are often lagging or disconnected from performance metrics. Traditional Marketing Mix Models (MMMs), while powerful, have focused almost exclusively on sales as the primary output. This is beginning to change. A new wave of Marketing Mix Models centered around High Value Actions (HVAs) is emerging as a more actionable, nuanced way to measure marketing’s real impact across the entire funnel. For CMOs seeking to drive profitable growth while building enduring brands, this shift offers a meaningful step forward.   The Evolution of Marketing Mix Models Traditional Marketing Mix Models were developed in a very different era of media and data. Designed primarily for large CPG brands in the 1980s through early 2000s, these models were built to evaluate the impact of a limited set of marketing inputs (e.g. TV, radio, print, and in-store promotions) on top-line sales. The goal was straightforward: determine which levers were driving revenue, using whatever aggregate data was available at the time.   Watch the unfolding of the history of MMM below! Catch this quick, engaging breakdown of how Marketing Mix Modeling has evolved over the decades:     But today’s marketing landscape looks nothing like it did then. We now operate in a highly fragmented, digital-first environment. Consumers engage with brands across dozens of touchpoints from connected TV and TikTok to influencer content, programmatic display, streaming audio, and more. At the same time, marketers have access to an unprecedented volume of behavioral data, enabling real-time insights into how people discover, evaluate, and engage with brands. The legacy MMM framework was not built for this level of complexity. It often fails to capture the impact of mid-funnel activity and brand-building tactics that influence consumer behavior over time. As a result, CMOs are left with a distorted view of what’s working, especially when brand investments don’t translate to immediate sales. To keep pace with today’s consumer journey and media environment, we need a more flexible, behaviorally informed measurement approach. That’s where High Value Actions come in.   Introducing High Value Actions High Value Actions are meaningful digital behaviors that signal consumer interest, intent, or future purchase likelihood. They are not soft vanity metrics, but rather quantifiable moments that serve as leading indicators of revenue.   Examples include: A prospective buyer using a store locator after seeing a digital video A consumer completing a product quiz or subscribing to a newsletter A user saving a product to a wishlist or joining a waitlist A shopper returning to a site and viewing multiple product pages A CPG consumer downloading a recipe or coupon after viewing an ad   What makes HVAs so powerful is their ability to connect brand marketing with observable, measurable behavior. Unlike traditional brand KPIs, HVAs occur in real time and are tied directly to engagement. Unlike sales, they happen earlier in the customer journey and are often more responsive to upper- and mid-funnel activity. But not all HVAs are created equal. Before incorporating these behaviors into your measurement framework, it’s critical to take a strategic approach to identifying which actions truly matter. The first step is to create a comprehensive list of potential HVAs relevant to your customer journey. This should include behaviors across your website, app, CRM, and other owned platforms, as well as external signals such as organic search queries, social media engagement, and referral traffic. From there, brands can take one of two approaches to identify the most meaningful High Value Actions. If a brand tracker is in place, run a regression analysis to estimate the strength of each HVA in explaining brand equity as measured by key metrics such as awareness, consideration, or preference. The resulting model can then be used to create an HVA Index, defined as a weighted composite of behaviors that serves as a proxy for brand strength over time. For brands without a brand tracker, correlation analysis can help identify which actions are most strongly associated with key business outcomes such as qualified leads, conversions, or sales. In either case, the selected HVAs or the resulting HVA Index can be integrated into a traditional MMM as an input, allowing marketers to quantify the incremental role of brand-driven behaviors in driving performance.   How HVAs Are Changing MMM Once you’ve identified your strongest HVAs whether through regression analysis tied to a brand tracker or correlation to sales or leads, these behavioral signals can be integrated directly into your Marketing Mix Model. Regardless of the approach, HVAs can drive incremental value in two key ways: 1. As output variables: Use HVAs as the dependent variable in a model to understand how media and marketing activity builds brand value over time. 2. As input variables: Include HVAs or an HVA Index as an additional input in a traditional sales-oriented MMM to quantify the contribution of brand-driven behaviors to revenue. This dual modeling approach allows you to quantify not only the direct impact of paid media on sales, but also its role in building brand equity through HVAs. By assigning a monetary value to your HVAs either individually or through an HVA Index, you can estimate the incremental contribution each marketing channel makes to brand strength. This additional value can then be layered into your overall channel performance analysis. The result: a more holistic view of effectiveness, and often, significantly different ROAS estimates once brand-building impact is factored in. Ultimately, this creates a more complete and dynamic view of marketing effectiveness. Intermediate behaviors become measurable ROI signals. Feedback loops are shortened, allowing for more agile optimizations. And upper- and mid-funnel investments can be evaluated with greater precision. For brands operating in long-consideration or omnichannel environments, this approach is especially valuable. It bridges the gap between brand-building and performance, giving CMOs a more actionable and defensible framework for planning, measuring, and justifying their marketing investments.   Watch the PowerHeal case study unfold below! See this approach in action as Katie Reed unpacks how PowerHeal leveraged HVAs and modeling to evaluate a major sponsorship opportunity:     The Strategic Advantages for CMOs Adopting an HVA-centric approach to MMM offers several strategic benefits: 1. It bridges brand and performance. By focusing on measurable behaviors that sit between awareness and purchase, HVAs dissolve the artificial divide between brand building and sales activation. This enables smarter investment decisions and clearer cross-functional alignment. 2. It improves media efficiency. Channels that once seemed hard to justify through sales data alone can now be evaluated through their impact on HVAs. CMOs gain a clearer understanding of which top- and mid-funnel tactics are truly moving the needle. 3. It accelerates decision-making. Faster signals mean faster learning. Marketing teams can iterate more quickly and avoid costly overinvestment in underperforming tactics. 4. It sharpens storytelling with the C-suite. HVAs offer a tangible way to show how brand marketing drives real business outcomes. They help translate brand investment into a language CFOs and CEOs understand.   What It Takes to Succeed Implementing HVA-based MMM is not plug-and-play. It requires collaboration between analytics, marketing, and product teams to identify the right actions, ensure accurate tracking, and integrate those signals into the model. It also demands discipline in prioritizing HVAs that truly reflect business value. Not every click or engagement qualifies. The most effective HVAs are those that correlate strongly with downstream revenue and can be measured consistently across time and tactics. Finally, CMOs must foster a culture that values learning, experimentation, and full-funnel thinking. HVAs are not the end goal: they are the bridge between marketing activity and meaningful business results.   The Future of Brand Measurement As marketing grows more complex and fragmented, CMOs need tools that provide both clarity and nuance. High Value Action-based MMM is one of the most promising innovations in this space. It allows brands to connect the dots between awareness and purchase, emotion and action, storytelling and performance. The brands that lead in this next era will not be the ones who simply spend more. They will be the ones who know what to measure and who are willing to evolve how they measure it.

In today’s dynamic marketplace, brands face increasing pressure to understand their customers with both speed and depth. Traditional research cycles are often long, expensive, and disconnected from immediate action. They cannot keep pace with the rapid changes in consumer behavior or the growing demand for agility in an intensely competitive world. The era of agile customer insights has arrived. The brands that succeed will be the ones that blend advanced measurement systems with AI-powered qualitative research to create a complete picture of customer behavior and confidently drive growth. This shift is not only operational but also strategic. Advanced analytics provide clarity on what happened, while consumer insights reveal why it happened. Marketing leaders must now embrace the powerful combination of both to remain competitive.   Why Combining Advanced Measurement and Consumer Insights Is Critical Sophisticated marketers rely on tools such as Marketing Mix Modeling (MMM) and incrementality testing to optimize performance across paid, owned, and earned media. These solutions offer clarity on where to invest, which channels drive the highest return, and how campaigns contribute to business outcomes. However, data alone cannot provide the full context. While quantitative outputs answer what happened, they do not explain the underlying reasons behind customer behavior. This is where qualitative research has traditionally played an important role by uncovering motivations, perceptions, and emotions through interviews, focus groups, and surveys. Too often, these disciplines have operated independently, leading to fragmented strategies and missed opportunities. Forward-thinking brands are now integrating these data streams to unlock faster, richer, and more actionable insights. This combination allows marketing leaders to move beyond reporting results and toward confidently making decisions that drive growth.   Advances in AI-Powered Qualitative Research One of the most significant recent advancements in customer insights has been the emergence of AI-powered qualitative research. Traditional qualitative studies were often expensive, time-consuming, and difficult to scale, which limited their use to small sample sizes and delayed decision-making. AI has fundamentally transformed this process. Whether the goal is to gather feedback on a new concept with stimuli, assess brand sentiment, evaluate website experience, or answer targeted business questions, the feedback loop is now significantly faster and more efficient. Today’s AI-driven platforms can process large volumes of unstructured data, including open-ended survey responses and dynamic questions, in near real time. This capability allows brands to understand not only what customers are doing but also how they feel about their experiences. AI tools can now convert previously overwhelming amounts of qualitative data into structured insights that are ready to inform business decisions.   The Advantages of AI-Driven Qualitative Studies AI-powered qualitative research provides distinct strategic advantages. Speed: Brands can now gather actionable customer feedback in hours or days, not weeks or months. Cost-efficiency: AI removes the manual labor associated with traditional research, lowering costs and allowing brands to conduct more frequent studies. Scale: Thousands of consumer voices can be analyzed simultaneously, creating insights with statistical weight that were previously impossible. Enhanced decision-making: When combined with structured measurement data, these insights help marketing leaders understand not only what happened but why it happened. This leads to smarter decisions across campaigns, products, and customer experience strategies.   Case Study: How PowerHeal Leveraged Integrated Insights PowerHeal, an innovative brand in the advanced wound care category, needed to drive growth and build trust in a highly competitive space. The company was approached by a major extreme sports brand with a sponsorship opportunity that would provide access to millions in a new target market. To evaluate this opportunity, PowerHeal leveraged an AI-powered qualitative research platform to extract rapid, actionable insights from its target audience. By conducting conversational surveys with consumers profiled to match the sponsorship demographic, the brand gathered immediate feedback on the perceived relevance and fit of the partnership. The AI-driven approach delivered rich qualitative data at speed and scale, providing PowerHeal with direct consumer sentiment that would have traditionally required far more time and resources. The insights from the AI-powered study, combined with advanced marketing mix modeling and High Value Action frameworks, played a pivotal role in the decision-making process. Consumers expressed strong enthusiasm for the sponsorship, describing it as a natural alignment with the PowerHeal brand that enhanced their interest and trust. This validation, along with supporting quantitative benchmarks, gave PowerHeal the confidence to proceed, viewing the sponsorship as a high-value growth lever capable of driving both brand equity and measurable business outcomes. To explore the details and results of our collaboration with PowerHeal,  read the complete PowerHeal case study here.   What’s Next: The Future of Agile Insights The combination of advanced measurement and AI-powered qualitative research is becoming increasingly essential. The next evolution will include continuous, always-on customer feedback systems powered by AI. These solutions will enable brands to detect changes in sentiment and behavior before they impact business performance. While AI will take on an increasingly central role, human judgment and strategic expertise will continue to be essential. The most successful organizations will create integrated systems where marketers and AI work in tandem to generate timely, actionable insights that drive better business decisions. For C-suite marketing leaders, the takeaway is clear. Brands that integrate data and customer insights effectively will be better positioned to adapt to shifting markets and evolving customer expectations. The question is no longer whether to combine advanced measurement and AI-driven qualitative research, but how quickly this integration can be achieved.

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.  

If marketing is the art of storytelling, then measurement is the science that makes sure the story has a happy ending. For too long, marketers have been trapped between messy data, conflicting metrics, and finance teams asking, “So... what did we actually get for that $5 million?” Enter the Marketing Accounting Framework - a structured, scalable way to reconcile attribution chaos and align your media decisions with actual business impact. Whether you’re running a scrappy DTC brand or steering a well-oiled enterprise marketing machine, having a marketing accounting framework is the modern CMO’s secret weapon. Here's why.     From Gut Feel to Growth Engine: Why Marketing Needs Accounting Principles Traditional marketing reporting often leaves leadership flying blind. ROAS looks great in-platform, but finance isn’t convinced. UTMs show one thing, incrementality testing another. So which numbers do you trust? The marketing accounting framework brings discipline to this chaos by aligning measurement with decision-making. It helps marketers triangulate data from three critical layers: Base Attribution – Think UTM-based, last-click data. Platform Attribution – What Meta, Google, Roku, etc., report. Advanced Attribution – Methods like Marketing Mix Modeling (MMM) or incrementality testing. Each offers different visibility into performance. But when structured under a unified framework, these layers allow you to make better, more confident investment decisions.     P&L Frameworks Explained One of the biggest unlocks in this methodology is the P&L view of marketing. Rather than thinking of marketing as a singular cost center, businesses benefit from structuring marketing into multiple "mini" P&Ls based on customer lifecycle stages and/or other critical business aspects.. There are many ways to structure the P&L view, depending on the type of business ranging from retail to CPG to professional services. There are some important questions to ask that can help you determine the right structure: Are you making investment decisions by customer segment? Are you making media investment decisions by geography? Are you making media investment decisions by product or service type? Is some media meant to drive upper-funnel outcomes only? Are you driving conversions, revenue, or margin?   Some examples of different types of P&L’s range from customer segment to revenue and many outcomes in between. Below are some examples: Customer Segment: New, Existing, Churned Geography: Country Retail location Product types:  Hardware vs Subscription Main SKU vs Add-ons SKUs Product Categories Customer funnel:  Visits, AddToCart, Lead, Orders, Initial Sign-up, Monthly Subscription Sales channel  Metrics:  Conversions, Revenue, Margin, Profit, LTV Ultimately, you want to pick P&L’s that are meaningful to the business which marketing has the ability to influence.   Common Examples of One, Two and Three P&L Frameworks   One P&L Framework Best suited for companies focused almost exclusively on new customer acquisition (think wedding rings, insurance lead gen, or aesthetic services like LaserAway). The marketer's mission is clear: drive new leads, efficiently and at scale. Decisions are anchored around: Media-driven revenue share ROAS by UTM vs platform Adjusted ROAS via incrementality benchmarks High/low sensitivity decisions for budget optimization   Two P&L Framework More common in growing DTC brands, this adds retention marketing into the mix. Now you’re not just acquiring, but also nurturing existing customers. Your accounting view now includes: P&L for new acquisition P&L for retention programs Separate performance targets for each Smarter testing on how to scale profitably across both   Dental Chain Example: Four P&L Framework This advanced model specific to dental services: contemplates Scheduled and Completed appointments by new vs. existing patient types. This structure helps the business understand how marketing is contributing to “leads” - in this case scheduled appointments in addition to revenue generating completed appointments for a broader view of the patient funnel. Each of the four efforts—new patient booked, new patient completed,, existing patient booked and,  existing patient completed—gets its own attribution lens, ROAS targets, and risk-adjusted testing strategy. This model requires more sophisticated infrastructure but delivers more views of performance and different optimization opportunities..     Decision-Making Powered by Sensitivity and Scenario Planning The real power of the framework kicks in with sensitivity analysis. Every marketing decision—cut, hold, scale—is evaluated based on: How much agreement exists across attribution models The spend size and potential upside/downside The risk of over or under-allocating due to data blind spots For instance, if UTM, platform, and MMM all agree a channel underperforms, it's a low-sensitivity, high-confidence budget cut. But if UTM says "cut," platform says "scale," and MMM is neutral? That’s a high-sensitivity decision—flag it for testing. The framework enables "mock decisions" before dollars hit the market. You model out potential outcomes, identify where incrementality testing is most valuable, and prioritize tests that de-risk big swings in spend.   Translating Attribution Into Action With Multipliers The framework doesn't stop at diagnosis - it provides a prescription. Once you’ve aligned advanced attribution with base data, you generate a multiplier: That multiplier becomes your Rosetta Stone. It translates your ideal CPA or ROAS into tangible platform bids. Suddenly, you’re not just trusting a spreadsheet—you’re using causal measurement to drive in-platform optimizations. Example: Target CPA (based on tested incrementality) = $100 Platform underreports value (multiplier = 0.5) You can now confidently bid up to $50 in platform to hit your real goal This closes the gap between finance’s demand for provable impact and marketing’s need for tactical flexibility.   One Dashboard to Rule Them All Finally, the framework enables a unified reporting view—a channel dashboard that incorporates: Spend Base attribution (UTM, platform, or both) Advanced attribution (MMM, testing, etc.) Multiplier and adjusted CPA/ROAS You don’t have to pick one truth. You choose attribution anchors per channel based on where signal is strongest. UTM for Search? Great. Roku’s own attribution for CTV? Makes sense. You build the dashboard around truth, not convenience. This lets marketers and finance finally speak the same language. No more hand-waving. Just data, decisions, and dollars moving in harmony.   TL;DR – Why It Matters Structured thinking wins: Marketing is finally getting its GAAP moment. Lifecycle view unlocks efficiency: One, two, or three P&L structures provide clarity by growth stage. Smart testing reduces risk: Sensitivity analysis shows where to validate with MMM or experiments. Multipliers bridge gaps: Translate marketing truth into platform action. Finance alignment improves trust: One dashboard, two teams, shared goals. In a world where every dollar is scrutinized, having a marketing accounting framework isn’t a luxury - it’s a necessity. It turns chaos into clarity, guesswork into growth, and marketing into a truly measurable business engine. Time to ditch the “it feels right” era. Let’s market like CFOs approve.  

The Brand PowerHeal is a breakthrough brand in advanced wound care, offering the world’s only FDA-cleared bioelectric bandage clinically proven to accelerate healing by 2-3x while reducing infection risk and scarring. Unlike traditional bandages that simply cover a wound, PowerHeal activates the body’s natural healing process by generating microcurrents that mimic the skin’s own electrical signals. The end result: faster, cleaner, and more complete healing. Prior to April 2024, the product was available for decades under a different brand name and only by doctor’s prescription to treat medical-related wounds. After receiving FDA clearance for over-the-counter (OTC) use, the product was rebranded as PowerHeal, unlocking a major opportunity to reach new consumer segments including sports enthusiasts prone to injury.     The Opportunity Following OTC clearance, PowerHeal launched a go-to-market strategy including a DTC website, Amazon retail presence, and digital media investment across Meta, Google, and Amazon Ads building awareness and driving revenue. Then came an inflection point: a prominent extreme sports property approached PowerHeal with a seven-figure, multi-year national sponsorship proposal. The deal promised exposure to millions of highly relevant consumers at live events and through affiliated media. Before committing to this high-stakes investment, PowerHeal’s leadership team turned to independent measurement consultancy M-Squared with a critical question:     The Approach: A Triangulated Framework M-Squared designed a rigorous measurement plan blending advanced analytics with real-time consumer insights and benchmarking data. This three-pronged approach was designed to evaluate the sponsorship through multiple lenses: 1. High Value Action (HVA) & Marketing Mix Modeling (MMM) We began by segmenting PowerHeal’s revenue into two P&Ls based on purchase paths: Amazon (65% of revenue) and DTC (35%). Since both channels were contributing meaningfully to the business, we aimed to better understand the customer behavior behind each and whether audience motivations or engagement patterns differed.     Next, we collected and analyzed brand signal data, including organic search and social impressions, website traffic, Amazon star ratings, and more, to understand how these signals correlated with sales. Through correlation analyses across both P&Ls, we identified the brand variables most strongly associated with sales value – specifically, Google and Amazon organic search impressions – and used these to build High Value Action (HVA) models. These models quantified the impact of current marketing efforts on PowerHeal’s top brand drivers. We found that paid media accounted for 25% of brand impact, while the majority of contribution was falling into Base. These findings spurred new hypotheses about what was driving brand growth, pointing to areas for deeper investigation in future analyses. We then built multiple Marketing Mix Models (MMM) combining the top brand drivers, marketing channels (paid and non-paid), and Base variables to determine individual sales contribution for both Amazon and DTC. Paid media drove the highest contribution (61.3%), followed by organic search (28.9%). Notably, PR accounted for 6.1%, a remarkable return given there were only two activations. While those releases had high circulation, the results were a significant eye-opener.     PR, like sponsorships, builds brand equity, drives awareness, shapes perceptions, and influences consideration. Both deliver broad reach in environments where the message is more credible and emotionally resonant. If two PR activations could perform this well, a high-profile sports sponsorship likely holds significant upside. As a final step, we applied the respective MMM contribution percentages to overall sales and calculated two key metrics to compare value: 1) sales per thousand impressions and 2) sales per click. The results were striking: organic search and PR, both brand-building channels, delivered substantially greater value than paid media. The power of brand for PowerHeal became abundantly clear.   2. AI-Powered Qualitative Research While modeling helped dissect PowerHeal’s current business state from an analytics perspective, we also wanted to hear directly from consumers most likely to attend extreme sports events. To gather these insights, we initiated a qualitative survey. Our goals were to: Understand how this audience treats their own sports-related injuries Gauge how they would respond to PowerHeal sponsoring extreme sports events Explore their perceptions of the PowerHeal product, including purchase intent and whether the sponsorship would influence that decision   To capture these insights quickly and cost-effectively, M-Squared partnered with Voicepanel, an AI-powered qualitative research platform that conducts adaptive, conversational surveys. The platform’s dynamic questioning and voice-recorded responses delivered breadth and emotional depth, surfacing clear, actionable takeaways in record time. The findings were highly encouraging. Consumers saw the partnership as a natural brand fit, with many stating it would increase their trust in PowerHeal and positively influence their likelihood to purchase. Example survey insights:     3. Case Study Benchmarking Seeking additional external validation, we reviewed case studies from adjacent brands such as KT Tape and Abbott’s Libre Sense to understand how they leveraged sports sponsorships to drive brand lift and sales growth. We found strong evidence of significant revenue increases following KT Tape’s Olympic sponsorship and Libre Sense’s partnership with IRONMAN and elite marathoner Eliud Kipchoge. In addition, we analyzed data from leading research sources including Nielsen and WARC, which consistently showed meaningful upside for brands investing in sports sponsorships. Notably, this channel ranks among the most trusted in advertising, with reported lifts in purchase intent often exceeding 40%.   A Clear Case for Sponsorship M-Squared’s triangulated analysis told a highly consistent story, pointing to a strong likelihood of success. As a final step, we evaluated the sponsorship proposal through multiple lenses, crediting only what we believed to be high-impact impressions. Using PR as a performance proxy, given its strong similarities to brand sponsorships, we applied estimated funnel metrics to calculate ROAS and CPM. The results were compelling: Estimated ROAS: 3.3x Estimated CPM: $6.72 These metrics were not only cost-efficient, they exceeded benchmarks across PowerHeal’s current portfolio and projected strong short and long-term returns.   Conclusion: Confidence Through Convergence By blending advanced modeling, consumer sentiment, and real-world benchmarks, M-Squared delivered a holistic, data-backed recommendation. Each input reinforced the others, painting a cohesive picture of opportunity. The evidence pointed to this sponsorship as a strategic growth lever: a chance to elevate PowerHeal’s profile, accelerate adoption, and solidify the brand in the minds of a high-fit, high-need audience. Today, PowerHeal moves forward with confidence, validated in their original instinct. All signs indicate that this sports sponsorship has the potential to be a transformative moment for the brand.  

Unlocking Profitable Growth: Turn Media Investment into a Revenue Powerhouse Since its launch in 2014, Parachute Home has redefined modern living with high-quality, responsibly made home essentials. From organic bedding to thoughtfully designed furniture, the brand has built a devoted following that values sustainability, craftsmanship, and comfort. As a digitally native brand, Parachute quickly gained traction through direct-to-consumer (DTC) excellence, and as demand grew, it successfully expanded into physical retail stores across the U.S. This omnichannel growth strategy positioned Parachute for long-term success, bringing the brand to more customers than ever before. With a solid foundation and a strong brand reputation, the next challenge was clear—how to optimize marketing investments for the next phase of scalable, profitable growth.   The Challenge: Unlocking the Next Growth Chapter   Parachute’s expansion into retail, alongside its strong digital presence, opened up new revenue opportunities. However, as the brand evolved, eCommerce sales began to soften, prompting a deeper look into growth strategy optimization. The first instinct? Cut costs, scale back media investment, and take a cautious approach. But before making sweeping changes, Parachute needed clarity on what was truly driving revenue. Was media investment as effective as it could be? How did paid media impact both eCommerce and retail performance? Were there opportunities to refine the media mix to drive greater efficiency and long-term growth? Additionally, expanding retail operations had increased overhead costs, making profitable growth a key focus. Rather than reducing investment reactively, the real opportunity was to optimize spend—maximizing impact while ensuring strategic, data-backed decision-making. To unlock these answers, Parachute partnered with M-Squared to develop an advanced measurement framework that would reveal the true impact of media investment across all channels.   Exploratory Data Analysis: Identifying the Key Growth Drivers Without a robust measurement system, it had been difficult to fully capture the role of media in driving both eCommerce and retail sales. Most existing analytics were digitally focused, meaning that the halo effect of paid media on retail was not well understood. Key Observations from the Data: Both eCommerce and retail sales followed similar seasonal peaks, driven by major promotional events like Memorial Day, Black Friday-Cyber Monday (BFCM), and Christmas. New and returning customers exhibited similar seasonal purchasing behaviors, indicating a potential synergy between acquisition efforts and retention impact. Media investment had been primarily focused on new customer acquisition, but its influence on returning customers was stronger than previously measured. By reframing the challenge as an opportunity, Parachute could move beyond basic media attribution and towards a holistic strategy that maximized total revenue impact.   The Solution: A Data-Backed Roadmap for Scalable Growth To accurately measure the true impact of media investment, Parachute and M-Squared developed a triangulated measurement approach combining Media Mix Modeling (MMM) and Flywheel Analysis. Media Mix Modeling (MMM) for Precision Attribution While Parachute had previously conducted incrementality tests to assess media performance, a fresh round of MMM analysis provided a more comprehensive and adaptable view of media effectiveness. The model assessed four key customer segments: New Customer – eCommerce Returning Customer – eCommerce New Customer – Retail Returning Customer – Retail By isolating each segment, MMM enabled a clearer understanding of how media impacted both eCommerce and in-store transactions.   M-Squared Flywheel Analysis: Optimizing for Profitability To complement MMM insights, M-Squared introduced Flywheel Analysis—a powerful tool that mapped out customer acquisition costs, break-even scenarios, and long-term value creation. This analysis allowed the team to: Determine the true cost of acquiring a new customer. Identify breakeven points based on Lifetime Value (LTV). Run scenario planning for different media strategies, optimizing for both revenue maximization and return on ad spend (ROAS).   The Results: A Clear Path to Profitable Growth The Advanced Attribution Analysis revealed critical insights that challenged the initial assumption that sales were declining due to inefficient media spend: Media was actually responsible for a higher than expected amount of new customer acquisition and was profitable on the first order. eCommerce sales decline was primarily driven by a drop in organic traffic, not media-driven sales. Retail sales remained stable, reinforcing the importance of omnichannel measurement. Optimizing the media mix could unlock a 9% increase in new customer revenue and in LTV revenue—while remaining profitable.   Key Takeaway: Media is a Growth Accelerator, Not a Cost Center Rather than scaling back media investment, Parachute now has data-backed clarity on where to strategically increase spend to drive both immediate and long-term gains.   The Opportunity: Scaling Smart for Memorial Day and Beyond With Memorial Day approaching, Parachute has a prime opportunity to leverage these insights and make data-driven media investments that fuel scalable revenue growth. Next Steps: Increase investment in proven, high-impact media channels. Refine media mix to optimize for ROAS and LTV growth. Leverage MMM and Flywheel insights to build a long-term omnichannel strategy.   The Conclusion: A Data-Driven Future for Parachute’s Growth The results are clear—Parachute is in a strong position to accelerate growth through smart media investment. Rather than seeing media as a cost to be managed, it should be viewed as a powerful lever for unlocking revenue potential. This data-backed roadmap provides the Parachute team with the confidence to lean into media investment, optimize for profitability, and scale strategically. With the right insights in place, Parachute is well-positioned to turn this next phase into its strongest growth chapter yet.  

The Brand Dr. Scholl's is a renowned foot care and orthopedic footwear brand with over a century of history. Founded in 1906 by podiatrist William Mathias Scholl, the company has become a global leader in foot care products and solutions. With over $600MM in international retail sales between the Dr. Scholls brand in the US and the Scholl brand internationally   The Challenge The brand lacked a good sense of what media was driving in terms of retail sales and wanted to understand where the opportunities were to invest and drive efficient growth. The company was also embarking on a Performance Marketing initiative and needed to understand what measurement could look like in that context.  The brand operates in a multi-channel retail environment that includes brick-and-mortar retailers like pharmacies and supermarkets, Amazon and its own D2C channel. Exploratory Data Analysis As part of the exploratory analysis, marketing measurement teams have to frame outcomes that marketing can be reasonably tasked to drive, but also contemplate certain marketing investments in the context of their business relationship with retail partners. This is particularly the case with trade and promotional investments with specific retailers. The M-Squared team had several data sets to assess and synthesize. These datasets included media investment and delivery data across a diverse mix of channels and tactics going back 2 years. The data also included retailer-specific trade and promotional investment. The media channels evaluated included Google Search (brand and non-brand), Facebook, Retail Media Networks (RMN), Digital Display, TV+VOD, and Out of Home (OOH). The Approach These large and diverse datasets needed to be processed and transformed to build the Analytics plan. The analytics plan developed as part of a structured Advanced Attribution Audit was designed to address the questions inspired by the business and entailed: Data Harmonization: Collection of the historical data (2 years) on units sold, retail sales, and media spend/activity which was reviewed and processed as per modeling needs. Preliminary Analysis: Performing trend analysis, correlations, and a basic MMM to understand the fit of individual variables driving units sold and retail sales. Further reviewed the retail store categories and created a hypothesis to determine the number of MMMs to run. Media Mix Modeling: Running 1000’s of iterations and 100’s of tranches for the different segments to come up with the best-fit models explaining the drivers of retail sales - the result of which was a 6-model structure based on retail category groupings.  Triangulation: Using the MMM decomps, did a triangulation exercise to understand the impact of media, promotions, and the value they bring in driving units sold and retail sales. The Marketing Accounting Framework (MAF) was oriented around incremental retail sales (revenue) driven by media across the retail category groupings. This was distilled into incremental ROAS (iROAS) by media channel/platform. This was a 6 P&L structure - meaning 6 different models were built - one for each of the retail categories. Learn about Marketing Accounting Frameworks with this short course from M-Squared: The Results Media drives incremental impact/revenue - nearly  12% of retail sales were driven by media and promotions on an incremental basis with a iROAS range of 0.2 - 2.4. Trade and promo spend had nearly  double the investment  as National Media & RMN’s but  less than half the iROAS. An opportunity to optimize trade and promo spend based on top-performing tactics. Amazon and other retailer-specific trade and promotional spend  have a halo on sales in other retail categories. National media influences  nearly all retail categories. Amazon RMN exhibits significant  cross-retailer halo  which suggests consumers are potentially  using Amazon to comparison shop. Retailer-specific promos have an impact outside the specific retailer running them. This may also be a comparison shopping situation. Overall, the insights suggested there was growth opportunity within the existing budget based on making some investment shifts to higher-performing (based on incremental retail sales & iROAS) media tactics. There also appeared to be an opportunity to optimize spend within trade and promo spend understanding investment there is required to maintain the relationship and is something of a “cost of doing business”. Working with individual retail chains to invest in the highest performing tactics based on their incremental contribution and ROAS is a branded or presented recommended approach. The Conclusion The model outputs and Triangulation surfaced some interesting and actionable insights. The project also raised some additional hypotheses and questions. What might this look like across different regions/geography? Would different product segments perform differently based on media-influenced retail sales? These are questions that can be answered through testing and model iterations to contine to refine the insights and decision-making information. As the Scholl brand continues to diversify and modernize its media mix, M-Squared will continue to identify opportunities to leverage advanced attribution as a competitive advantage, make smart bets, and identify growth opportunities. This is just the first step in the beginning of a journey.