Disrupting TV Advertising with New Algorithms

Within the traditional framework TV researchers analyze show performance using target share or target index If a marketer identifies its target audience as Hispanic men between the ages of 18 and 24 the marketer may see shows likeamp160The Walking Deadamp160Sundayamp160Night Football andamp160ToshO Still even if many people from within this target watch NFL games they are not necessarily overrepresentedTo solve this potential shortfall Viacom39s Data Strategy team developed a patentpending content scoreamp160The content score is an algorithm that crunchesamp160a mix of behavioral attitudinal and social thirdparty data along with Viacom39s own firstparty multiplatform viewing data It then outputs a score that maximizes both share and index whichamp160helps theamp160team find the right if sometimes unexpected combination of shows talentamp160and music for the custom marketing campaigns it creates for advertisersUsing the above example theamp160team can input the client39s target audience of Hispanic men between the ages of 18 and 24 and the algorithm may show thatamp160The Daily Showamp160has the best share and index score mix even if this target weren39t at the top of either list Viacomamp160can then engage for a custom effort on behalf of the marketerFor more on what went into the development of the algorithm the problems it helps solve and the current state of TV advertising I spokeamp160withamp160Brandon Grabowskiamp160Director of Data Strategy at ViacomAdam Flomenbaum What39s broken with the way advertisers currently buy TVBrandon Grabowskiamp160Well there are two broad problems with the way campaigns are builtThe first problem in the media and advertising industry marketers do research to determine who their target audience is They then extrapolate on the research creating audience narratives that are then sent to agencies to build programs against The problem is that researchers and marketers must interpret results adding bias to their findings For example the marketer might decide that their target audience is quotsocially consciousquot Does that mean the target is financially charitable or does it mean they volunteer in their community These are biases that different data sets and poor communication between players exacerbateThe second problem is target selectionamp160For example if a study shows that Hispanic Millennials overindex in purchases of a specific product the marketing team might decide to target them and at the exclusion of other demographicsamp160But what39s missing here is the relationship between the consumer and the product Advertisers need to truly consider why they are targeting certain people and how to make sense of the data they collectSo what39s the solution We can use data to target but it doesn39t necessarily fix the issues above Algorithms are written by humans and are therefore subject to bias We need to be using data to help us with the decisionmaking process but not to make the decisionsamp160foramp160us That39s where our custom content score comes inHow does theamp160algorithm help correct for the scale versus targeting problemTheamp160custom content score is a tool that explores the affinities of an advertiser39s target audience It does not make decisions it creates a map It looks at affinities like genre preferences celebrity affinityamp160and general interests and then leverages data sets to create a broad affinity map which can then be interpreted by the campaign teamWhat went into the development of the algorithmWhen an ad hoc data request comes in likeamp160quotWhat are the top shows for Millennials who drive a truckquotamp160any researcher will pull share and index to see what shows are big for the audience and what shows overindex If we rank these two lists separately we often get two entirely different top ten lists It is easy to parse through the results manually when we39re looking at a handful of rows but when there are 10000 shows in a database no one wants to evaluate that manually So we rely on algorithms At its simplest this model finds the compromise between share and index maximizing bothHow do you input an advertiser39s target and how do you then analyze the resultsWe start by exploring the target audience before we make any recommendations If an advertiser wants to look at quotfirst person shooter console gamersquot we might break that down into a malefemale split to see if the audience has for example different needs based on gender or based on ethnicity We then refine the query over and over again until we are comfortable that we are looking at something that is true rather than something that39s convenient Once we have the query identified we produce a map of affinities and then interpret the results together with the campaign teamWhat are some of the questions you ask advertisers before making a recommendationThe most important thing we ask for is all the information that the advertiser has about this target audience We see pretty quickly who has thought about things methodically and who is just giving random data points I once had an advertiser say that their audience quotgoes to Coachella but not Bonnarooquot They might have data to support that finding but is it relevant It might actually be a significant predictor but it could just as easily amp160and more likely be random noiseThis commentary was written by Adam Flomenbaum who is an executive editor of The Drum39s future of TV section Found Remote and has also written for Adweek He39s a fan of Comedy Centralamp8217s Nathan For You Broad City and The Daily Show as well as Spikeamp8217s Bar Rescueamp160He can be reached at TwitterClick the social buttons above or below to share this story with your friends and colleaguesThe opinions and points of view expressed in this article are exclusively the views of the author andor subjects and do not necessarily represent the views of MediaVillagecomMyersBizNet Inc management or associated bloggers

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Disrupting TV Advertising with New Algorithms

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