How To Use AU In Your MMM

Today with renewed interest from brands many of which never left it behind and with new datahandling capabilities making MMMs faster and more flexible marketing mix modeling is flourishing At the same time the industry is coalescing around new prescriptions and frameworks for what amp8220modern MMMamp8221 should look likeIn December 2025 the IAB Measurement Addressability ampamp Data Center published Modernizing MMM Best Practices for Marketers identifying six ingredients that characterize a modern MMM stronger foundations omnichannel coverage speed and flexibility integrated measurement actionable outputs and organizational adoptionOther industry participants have pointed to similar related needs In 2023 Marketing Science Instituteamp8217s MSI Blue Ribbon Panel Report Charting the Future of Marketing Mix Modeling MMM Best Practices named data granularity as the most pressing needed MMM improvement A 2025 KantarMeta global study of 1935 measurement professionals at large advertisers found that 34 prioritize MMM above all other measurement solutions yet 37 identified greater granularity and actionability as the leading opportunities to improve itamp160 And just this spring eMarketer called for MMM providers to deliver more actionable findingsSome of MMMamp8217s core components havenamp8217t changed much over the years Bayesian priors saturation curves and adstock analyses exist for most MMMs But in response to the industryamp8217s call for modernization and to new competition from Google and other market entrants traditional MMM is evolving New data elements can now be incorporated into modernized marketing mix models allowing them to surface granular and actionable findings that models in past years could notAdelaide built its AU metric for exactly this kind of use case AU is a 0amp8211100 media quality score that predicts the probability a media placement will capture attention and contribute to a business outcome It is trained and validated against outcomes data and advertisers have long used it to connect media quality to business performance In the amp8220Integrated Measurementamp8221 section of its MMM Best Practices the IAB explicitly names attention as a metric that should be mapped to business outcomes consistently as an operational feature not as an adhoc indicator That is a role AU has already played for years Bringing AU into MMM extends its value beyond the individual campaign into broader brand and business analyticsWhere AU Fits In A Brandamp8217s MMMAU can play three useful roles in MMM predictor multiplier and confirmer Each role serves a different purposeAs a predictor AU can be analyzed against historical outcome data before being introduced as an ingredient into a marketing mix model This analysis validates AUamp8217s ability to predict a brandamp8217s KPIs and helps identify what levels of media quality business performance requiresAs a multiplier AU can be loaded as an impression weight inside a mix model Each impression is scaled by its AU score before the media data is loaded into the MMM The mix modelamp8217s data structure does not need to change but the model inputs become much more specific Instead of treating every impression within a channel as equivalent the mix model can distinguish between impressions with different levels of media quality Weighted inputs are already compatible with Recast Googleamp8217s Meridian and every major commercial MMM platformAs a confirmer AU can be analyzed alongside a mix modelamp8217s outputs to validate whether the modelamp8217s recommendations point toward media that AU mediaquality measurement also predicts will performThe multiplier role is where AU has the most direct effect on an MMM AU delivers two distinct benefits improved model fit and increased model output granularityImproving Model FitIn traditional MMM channel impressions are loaded into models as undifferentiated chunks A TV impression is a TV impression A display impression is a display impressionBut media does not work that way in practice A TV impression during a major sports tentpole event may affect outcomes differently than a TV impression during a forgettable midtier reality show A highquality CTV impression may not carry the same value as a lowquality one even if MMMs count them the sameWhen mix models have no direct way to account for that variance its effect is concealed It may show up as noise in the residuals overstated channel coefficients or digital channels that appear to deliver implausibly high returns because they generate enormous impression volumesWeighting impressions by AU gives MMM a media quality signal to work with It allows mix models to account for the difference between impressions that are merely delivered and impressions that are more likely to capture attention and contribute to outcomesAdelaide39s published case study with MMM provider Arima and a global CPG brand illustrates this point In this example the brand39s spendbased MMM attributed 21 of sales to media An unweighted impressionsbased MMM analysis attributed 76 of sales to media nearly four times the benchmark and an implausible overstatement When impressions were weighted by AU the new model restated mediaamp8217s contribution to sales at 35amp8212much closer to the spend benchmarkamp8212while also improving model fitamp160Arima founder Winston Li described the result as amp8220an impressionsbased view of media performance we could trustamp8212one that aligns with spend and opens up entirely new possibilities for our clientsamp8221Sourceamp160 Arima x Adelaide MetricsThe same principle has shown up in other MMM applications In a test for a global media brand AUoptimized campaigns lifted ROI per dollar by 51 overall versus businessasusual bidding with even stronger lifts in certain geographiesIncreasing Model Output GranularityThe second benefit of integrating AU into MMM is a new visibility that oldfashioned models could not previously provide AU works at the finest level of granularity possible often more granular than the model itselfamp160A CTV impression weighted into highAU mediumAU and lowAU tiers gives the model three separate elasticities adstock parameters and saturation curves where there was previously one Instead of treating all CTV impressions as equivalent MMMs can begin to show how different levels of media quality impact campaignsThis is the granularity that MMM users across the industry have been asking forSourceamp160 Adelaide Metrics illustrativeThis granularity also makes the outputs more actionable If a mix model can distinguish between high and lowAU inventory it can also support AU floors minimum quality thresholds that represent the level of AU required to drive a specific KPI The MMM can then recommend not just where to spend but how much to spend against media with different levels of qualityFor a global beverage brand KPIspecific AU floors ranged from 26 to 33 AU with media above the 26 AU threshold delivering up to 2x stronger brand impact Following this logic a 20 lift above an AU floor is worth roughly a 10 higher CPM a number the AUweighted MMM can use in scenarioplanning and forecasting exercisesHow To Bring AU Into Your MMMAU is an MMM input not an outputamp160amp160There are six steps to incorporating AU into an MMM The first three determine whether and how AU becomes a model input The last three establish how to interpret and operationalize the MMM outputs once AU is included1 amp160 amp160 Establish the AUoutcome relationship Analyze AU scores from a fresh audit ongoing scoring or Adelaide channel norms against historical KPIs Identify the AU level where KPI performance begins to inflect This is the predictor role proving that AU and outcomes align before any model adjustments begin2 amp160 amp160 Define your AU score groups Bundle scored impressions by AU level typically high mediumaverage and low though the right groupings may vary based on a brandamp8217s needs as well as placement channel or daypartamp8212whichever data cut maps to the buying decisions your team needs to make3 amp160 amp160 Load AU as an impression weight Scale each impression39s raw count by its AU score before the spend impression and outcome data are loaded into the MMM The data model structure does not need to change4 amp160 amp160 Run the MMM both ways and compare Build the AUweighted version alongside the unweighted baseline Examine adjusted Ramp178 coefficient stability posterior shrinkage and whether the bandlevel coefficients rankorder with AU They should even though nothing in the modeling is forcing them to5 amp160 amp160 Translate the model outputs into action Use the bandlevel coefficients and saturation curves to set AU thresholds qualityadjusted CPM benchmarks and placementlevel spend recommendations Adelaideamp8217s Flight Control and ScaleLiftThreshold tools help translate MMM outputs into format and placementlevel buying decision aids6 amp160 amp160 Validate alongside the model and iterate As campaigns run compare AU measurement of the purchased media against the MMMamp8217s predicted outcomes This is the confirmer role Where the two diverge the divergence is itself a finding which can lead to more insightful evaluations in the next analysis cycleConnecting The MMM To ActionThe IAB39s fifth capability amp8220Actionable Outputsamp8221 is what MMM users are most explicitly asking forA traditional MMM produces channellevel recommendations spend more on CTV less on display An AUweighted MMM produces more specific placementlevel recommendations spend more on topquartileAU CTV less on bottomquartileAU display That level of specificity maps more directly to how advertisers buy media todayThis is where AUweighted MMM starts to move from analysis to clear buying guidance One QSR brandamp8217s MMM provideramp8212after running an AUweighted analysisamp8212used it to amp8220inform placement decisionsamp8221 and amp8220test future dollars leveraging AU resultsamp8221amp160Qualityadjusted CPM calculated as CPM amp215 AU CostPerAU and AU thresholds all become buying language that the MMM can quantify in dollarsWhat AUWeighted MMM Can Reveal NextImproved model fit is AUamp8217s starting point with MMM Coming next is learning what AUweighted MMM can show about temporal and pricing dynamicsamp8212adstock decay rates incrementality patterns and the behavior of CostPerAU across the marketplaceamp160Early evidence suggests highAU impressions may decay more slowly longer adstock and resist saturation longer than lowAU impressionsDoes the same logic extend to incrementality Does the marketplace price quality efficiently or is there persistent dispersion that an AUweighted MMM can surface Can attention measurement tell us something about how media effects compound over a campaign These are the questions Adelaide is working on answering nowThe future of MMM that the IAB MSI and others are pointing toward is built on better more granular data inputs that can be translated into actions a media buying team can take Qualityweighted impressions are among the most consequential of those inputsamp160 Adelaideamp8217s AU is here today and available to be put to work on behalf of brands buyers and mix modelersReferences Kantar and Meta amp8220Understanding Marketing Mix Modeling MMM Adoption Dynamics in the Industryamp8221 May 2025 IAB Measurement Addressability ampamp Data Center amp8220Modernizing MMM Best Practices for Marketersamp8221 December 2025 Marketing Science Institute amp8220MSI Blue Ribbon Panel Reportamp8221 July 2023 Adelaide amp215 Arima case study with a global CPG brand Adelaide engagements with a global beverage brand and a global media brandPosted at MediaVillage through the Thought Leadership selfpublishing platformClick the social buttons to share this story with colleagues and friendsThe opinions expressed here are the author39s views and do not necessarily represent the views of MediaVillageorgMyersBizNet

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How To Use AU In Your MMM

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