the marketer's dilemma

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  • The Marketer's Dilemma:Focusing on a Target or a Demographic?

    The Utility of Data-integration Techniques

    MrKE HESSNielsenMichael.Hess@nielsen.

    com

    PETE DOENielsenPete.Doe@nielsen.com

    Data-integration techniques can be useful tools as marketers continue to innprove

    overall efficiency and return on investment. This is true because of the value of the

    techniques themselves and also because the current advertising market, based on

    demographic buying, has major opportunities for arbitrage in the range of 10 percentto 25 percent (where in that range depends on the nature of the vertical). The currentstudy reviews different methods of data integration in pursuing such negotiations.

    INTRODUCTIONAdvertisers, agencies, and content providers allare looking for improvement in the placement ofadvertisements in content. If an advertiser canreach more of its customers and potential custom-ers by spending less money, or an agency can helpan advertiser to do the same, this yields a positiveeffect on the advertiser's bottom line. Conversely,a content supplier can enhance its value if it candemonstrate that its content is attractive to par-ticular types of people (e.g., those disposed to aparticular brand or category, or even a particular.psychographic target).

    In this quest for improved advertising effi-ciency and return on investment (ROI), a numberof different methods have evolved. Most market-ers and their agencies use targeting rather thanmass-marketing strategies (Sharp, 2010). Beyondthis, many agencies have their own "secret-sauce"formulas whereby they adjust the value of anadvertising buy as a function of how much"engagement" can be attributed to that vehicle,whether it be a specific television programor a magazine title. A more recent in-marketapproachexemplified by TRA (Harvey, 2012) andNielsen Catalina Serviceshas also shown thatbuying can be improved through the identificationof programs that have more brand and categoryheavy users.

    The authors' own work since 2007 with data-integration techniques has shown that fused data

    DOI: 10.2501/JAR-53-2-231-236

    sets also can improve targeting efficiency by arange from about 10 percent to 25 percent depend-ing on the category vertical. A number of firmsemploy data fusion and integration techniqueson the provider side (e.g., Nielsen, Telmar, Kantar,and Simmons) and the agency business (Hess andFadeyeva, 2008).

    In this study, the authors share some of the defi-nitions and empirical generalizations that haveaccumulated in the past five years of working withthese techniques.

    The practical application of data integrationalready has begun to appear in the marketplace.A large snack-manufacturing company presentedsome of its findings ata recent Advertising ResearchFoundation (ARF) conference (Lion, 2009); a globalsoftware supplier took the stage at a Consumer-360event (Nielsen C-360, 2011); and a media-planningand buying agericy has indicated that it is using itscustom fusion data set to verify and fine-tune com-mitments made in the 2012 Upfront and in all ofits competitive pitches for new business (personalcommunication to M. Hess, 2012).

    In the next section, the various data-integrationtechniques are defined, and some of the advan-tages and disadvantages of each are discussed.

    TYPES OF DATA INTEGRATIONThere are three broad types of data integrationused in media and consumer research for advertis-ing planning.

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  • WHAT WE KNOW ABOUT ADVERTISING II

    EIVIPIRICAL GENERALIZATIONAnalysis with integrated data sets and the national people meter panel has shown usthat if an advertising buy is made based on a marketing target and the programs thatits members viewrather than against a demographic targetthere is empirically arange of between 10 percent and 25 percent improvement in the efficiency of that buy.This marketing target can be based either on consumption pattern segmentation (e.g.,heavy/light category users) or on psychographic/lifestyle segmentation (e.g., prudentsavers versus financial risk takers).

    Directly Matched DataData sets are matched using a common key(e.g., name and address, or cookies). Veryoften, this requires the use of personallyidentifiable information, and appropriateprivacy measures must be in place. Someof the key technical aspects that must beevaluated are completeness and accuracyof matching.

    For marketing purposes, databasesthat are integrated via direct-matchingof address are often referred to as single-source data, but there is a distinctionbetween true single-source and this formof integrated data as the completeness andaccuracy of the match are usually not per-fect. However, it can be considered to bethe next best thing to single source assum-ing the datasets being integrated are ofgood quality and relevance.

    An example of this sort of database isthe Nielsen Catalina Services integrationof Catalina frequent shopper data withtelevision data obtained from NielsenNational People Meter data and ReturnPath Set Top Box data.

    Unit-Level (e.g., respondent-level)AscriptionIn many cases, direct matching of datais unfeasible, perhaps because of pri-vacy concerns or because the intersectionbetween the data sets is minimal (this isusually the case with samples, where pop-ulation sampling fractions are very small);assuming no exclusion criteria for researcheligibility, the chance of a respondent

    being in two samples with sampling frac-tions of 1/10,000 is 1 in 100 million.

    In these cases, statistical ascription tech-niques can be used to impute data. Forexample, product-purchase data can beascribed onto the members of a researchpanel that measures television audiences,using common variables on the televisionpanel and a product-purchase database toguide the ascription. This enables viewinghabits of product users to be estimated.

    Data fusion is one example of a unit-level ascription technique that is increas-ingly being used to create integrateddatabases. (The topic is discussed in moredetail later in this article.)

    Some of the advantages of this approach:

    There is no additional burden on therespondent. Because the ascription is sta-tistical, it can be applied to anonymizeddata. Additional data are obtained with-out affecting existing response rates orworsening respondent fatigue.

    There are no privacy concerns. Alongwith the previous point, it makes this aparticularly valuable approach to add-ing additional data fields to media cur-rency measurements, which typicallyhave tight constraints on respondentaccess and measurement specifications.

    As the ascription is applied at the urt/respondent level, the database createddelivers complete analytic flexibility.A particularly relevant and valuable

    consequence of this for media databasesis that advertising reach and frequencyanalyses can be created.

    The cost of ascription is low in com-parison to the cost of additional primaryresearch.

    Caveats associated with this approach:

    Ascription techniques contain the pos-sibility of model bias. This needs to becarefully assessed. Model validation isessential.

    In the majority of cases, ascriptionmodels have aggregate- rather thanrespondent-level validity. For example,a model that overlays brand purchasingonto a television measurement panelmay not be able to predict the actualbrand purchases of an individual house-hold on the panel, but it will be able tareliably predict the viewing of brandpurchasers as a group. This meansthat the approach is relevant to advei-tising planning but less applicable totest-control ROI analyses where directassessment of purchase versus exposureis required.

    Aggregate-Level IntegrationAggregate-level integration uses segmen-tation to group and then link types cfrespondent on data sets. The segmentationtypically uses combinations of demograph-ics and geography, though any informationcommon to the data sets can be employed.

    An example of a commonly used seg-mentation is Prizm, which segments thepopulation into 60 geo-demographicgroups. An assessment of viewing habitsof brand users can be obtained by iden-tifying Prizm codes strongly associatedwith particular brands (using a consumerpanel) and looking at viewing traits associ-ated with these groups (using a television

    2 3 2 JOUIRL or flDUERTISlOG RESEflflCH June 2 0 1 3

  • THE MARKETER'S DILEMMA: FOCUSING ON A TARGET OR A DEMOGRAPHIC?

    panel with Prizm classification). Alterna-tively, purchase, propensity scores acrossall segments can be calculated on the con-sumer panels and used as media weightson television audiences.

    Advantages of this approach:

    Segmentations can cover a widescopelinking data sets throughgeo-demographic segmentation, forexample, allows consumer and mediaresearch databases to be connected andsubsequently linked with geographicaldata such as retail areas.

    Understanding a brand through the lensof a suitably constructed segmentationdelivers insights beyond basic purchasefacts, perhaps guiding advertising crea-tivity as well as media touch-points.

    Limitations of this approach:

    Segmentations, by nature, assumehomogeneity within segments, and thisdelivers less precision and less sensitiv-ity than other approaches.

    Because the integration of data sourcesis not unit/respondent level, there arerestrictions on analysis: in particular,campaign reach and frequency.

    The Pros and Cons of Each ApproachDirect match, unit-level ascription, andaggregate-level ascription can' be consid-ered as a tool for users of research, to beused in the appropriate way (See Table 1).For example, respondent-level ascriptionof brand user attributes on a televisionpanel may be used to plan advertisingfor a specific brand target; a direct-matchdatabase may then be used to