At ARF, Marketers Are Pointed Back by AI to Reach and Frequency

Erwin Ephron looking down from above is surely relieved at this correctionThe kickoff session with Googleamp8217s Senior Director of Data Science ampamp Engineering Harikesh Nair and TransUnionamp8217s Senior Vice President Marketing Solutions Services Mike Finnerty showed how reach and frequency can replace dollars or gross rating points GRPs in marketing mix modeling MMM resulting in better understanding of results more prescriptive guidance regarding how much reach and frequency is needed better out of sample prediction of future results further reduction in the effects of collinearity1 and remarkable improvements in the sensitivity of response curves for different media typesamp8220This is a huge innovationamp8221 commented Mike Finnerty amp8220MMM generally has static response curvesamp8221 meaning that all media types come out looking the same and do not change across very different media mixes making them essentially useless amp8220Reach frequency gives us net new signal which changes the response curvesamp8221Many of us in the business have been talking about the need to go this way for years Jim Spaeth Alice Sylvester and Leslie Wood come to mind as leading exponents My own 2023 CIMM report on the future of ROI measurement pointed out that reach saturation curves are highly correlated with ROI doseresponse curves for the same media type and the wide range of differences in how early ie how low a reach certain media types eg digital advertising via computers saturate top outPete Doe Chief Research Officer and Shweta Shah Vice President of Data Science at Nielsen copresented Nielsenamp8217s approach to outcome measurement which uses AI and a synthesis of proven methods to provide a holistic perspective via an allinclusive dashboard As you can see reach and frequency by media types across TV and digital are shown side by side with full funnel outcome measures of branding and salesPete emphasized that audience and outcome measures must be seamlessly connected collaboration is essential context is key thirdparty independent measurement is crucial for transparency and comparability and methodology mattersMolson Coorsamp8217 Michelle Wojnowski Senior Manager Marketing Analytics and Optimization Googleamp8217s Madison McDonough Food ampamp Beverage Analytical Lead Big Box Retail and Circanaamp8217s Yeimy Garcia Smith Senior Vice President of Global Measurement found that adjusting YouTube reach and frequency improves ROAS return on ad spend The study highlights a scalable datadriven method for optimizing digital media investment based on the solid ground of centuryold reach frequency knowledge and careful experimentationRex Du Professor of Marketing at the University of Texas at Austin analyzed the ordering of home food delivery based on ads in 14 million LG TV homes 53618 homes ordered food delivery during the 4month study Analysis determined that the best strategy is to use addressability to target homes that have ordered home food delivery before and to wait 3 to 5 days after the last order before running the next ad for home delivery This is an example of reachfrequencytiming in intelligent practiceAs Erwin Ephron pointed out if you do not reach someone you cannot expect any outcome from that person2 As he also famously said whatever improvement in likelihood of buying is created by a second exposure it must be a smaller increment than the zerotosomething increment of the first exposure This is why reach is such an important metric quite the opposite of whatever a vanity metric might beErwin did not ignore frequency or consider it to be unimportant His experience with media optimizers was that if he gave any value to frequency at all the optimizer would give him too much frequency and not enough reach Mind you he was using the first generation of media optimizers more recent generations can optimize for both reach and frequency without sacrificing reachErwin was also the worldamp8217s leading champion of recency amp8211 meaning to reach someone while they are in the buying window Like the home delivery case cited above where the 3to5day interval is the predictor of the next buying window Joel Rubinson a mentee of Erwinamp8217s brilliantly used this in an NCS study which increased ROAS dramatically by predicting the next shopping trip using household longitudinal data3Leslie Wood in a ScanAmerica study in the closing years of the 20th Century found that two ad exposures within the 48hour window before a shopping trip increased sales 14 a strong measure of the recency aspect of reachfrequency Looked at from a new vantage point after a hundred years of reach frequency this lens is actually a combination of four factors reach frequency timing recency and sequence interactions Ed Papazian btw has been saying this for yearsThe more technicallyoriented readers will also appreciate a couple of other reports below on the ARF Accelerator 2025Bharath Gaddam CEO ampamp Founder of DATA POEM presented a case study for one of the worldamp8217s largest advertisers which had been using five siloed MMM models marketing trade pricing product innovation eCom that became integrated as one through the DATA POEM Large Causal AI model Including not only reachfrequency but also the synergy of each combination of media types to provide a brand revenue growth optimizer The basic innovation of DATA POEM is to shift from correlation to causation operationalizing the theories of Judea Pearl into the worldamp8217s most advanced Causal AI platformSequencebased phenomena can also be measured using AI Transformer technology as presented by in4mation insightsamp8217 Senior Director Marketing Science Client Services Grant West and Memorial Sloan Kettering Cancer Center Senior Director Marketing Measurement Analytics ampamp Insights Adam Graves Traditional multitouch attribution MTA models struggle with dependencies among sequences of touchpoints this equates to duplication patterns within the reach frequency lens and fail to integrate marketing channel attribution with onsite clickstream behavior This makes MTA more predictive of a larger number of discrete variables including lifetime value variance confidence intervals degree of risk who to target and their likelihood of conversion Technically their model uses a multihead selfattention matrix where different variables are focused on by different neural heads whose findings are collaborated together in real timeamp1601 The difficulty of separating out effects by media types when all of them are generally running at the same times2 It is true of course that an unreachedbyadvertising person might hear about the ad from someone else This secondary exposure has value especially if it is a positive endorsement of the brand from a trusted third party However many of these secondary exposures might not be positive or might only relate to irrelevant but amusing aspects of the ad3 As written about in recent posts one of my new passion projects UltiMedia will be using this method along with RMT and other proven ROAS optimization strategiesCover Image Source The ARForgPosted 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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At ARF, Marketers Are Pointed Back by AI to Reach and Frequency

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