Field
Experts in marketing measurement and their latest posts.
Founder of Middlegame Marketing Sciences, turning marketing data into action
Todd argues that siloed retail media ROAS misleads in two ways: it claims credit for demand built elsewhere and ignores halo effects on other channels. He recommends modeling channels together and validating incrementality with geo-lift experiments and holdouts.
Todd argues that the buzz around Meta's Seller app misses the point. New platforms should be treated as customer signals that feed a decision support system, validating which investments drive immediate incrementality and longer-term organic growth, rather than as more reporting.
Todd argues that the search for one proof-of-success metric is outdated. Citing the IAB's updated podcast measurement guidelines, he says measurement is shifting from counting events to understanding how varied customer responses combine to create business value.
Todd argues that the brand versus performance debate is really investments versus customers. Channels, price, distribution and promotions interact along the buying journey, so AI should help optimize them as one connected system rather than speed up media optimization alone.
Todd argues that unused rewards are often an early sign of disappearing engagement, not happy savers. He says loyalty, media, pricing and other investments trigger linked customer responses, so teams should study the whole system instead of optimizing single metrics.
Todd argues that dashboards report what happened instead of guiding decisions. Predictive metrics like Google's are a step forward, but they must work at the level of investment choices and model interactions across the whole marketing portfolio to guide budget allocation.
Todd argues that most measurement systems optimize for one customer response, though marketing triggers many. He presents Agile Insights Modeling as a middle-out approach between top-down MMM and bottom-up MTA, separating incremental response from organic demand across the customer journey.
Todd argues that the "in-game advertising versus CTV" debate frames the choice wrongly. A new channel can amplify, complement, or cannibalize existing ones, so marketers should ask which combination of investments produces the greatest incremental business outcome.
Todd argues the K-shaped economy shows there is no one winning channel, not that Connected TV is the answer. Models that average responses across diverse customer segments mislead, so measurement should treat investments, customer responses, and outcomes as one system.
Todd uses a MediaPost piece on the K-shaped ad economy to argue that marketing measurement leans too heavily on averages. Segments respond to price, merchandising, assortment and media differently, so models should capture that variation instead of compressing it.
Todd argues that CMOs must move from measuring individual marketing activities to understanding the full commercial system. Models should cover multiple investments, responses, and outcomes together, and separate truly incremental sales from demand that would have happened or merely shifted.
Todd argues that analysts too quickly blame predictive models when the real fault lies upstream. Inconsistent definitions, broken processes, stale data and weak governance compound over time, so fixing the surrounding data system should come before replacing the model.
Todd argues that Walmart's acquisition of Vibe signals media, commerce and analytics merging into one system. He says channels measured separately hide how investments reinforce or compete with each other, and that modeling the whole system changed a retail client's decisions.
Todd argues that retail media data should ground marketing decisions in observed shopper behavior, not demographic assumptions. Its real value is showing how content, media, promotions, and merchandising interact, so brands stop optimizing channels in isolation.
Todd argues that evaluating retail media in isolation hides whether it creates new demand. Using a World Cup example, he describes modeling TV, retail media, digital, social and promotions together to find which investments amplified response and which merely shifted existing demand.
Todd argues that brands and agencies waste the World Cup by running isolated promotions during key matches. He says using search data to adjust strategy in real time keeps engagement going beyond the event, even for brands that are not FIFA sponsors.
Todd argues that judging AI one tool at a time yields what Melissa Reeves calls random acts of AI. He says the unit of analysis should be the connected system, and links this to Provalytics' Agile Impact Modeling approach to measuring how investments interact.
Todd argues that impressions and attention are not rival metrics. Impressions create the opportunity for engagement and attention raises the odds of action, so both should sit in one predictive framework judged by whether spend produces incremental growth.
Todd argues that AI in retail, such as Walmart's real-time recommendations, changes how customers buy across channels in ways traditional measurement overlooks. He recommends AI-powered approaches like Provalytics' Agile Impact Modeling to connect marketing activity, customer behavior, and financial results.
Todd argues that brand managers who stick to traditional campaigns miss how demand works as a network of variables that affect each other. Adaptive AI advertising surfaces those feedback loops and supports continuous learning over one-variable-at-a-time seasonal optimization.