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PPM commercial audience estimates offer insight about consumers’avoidance of tv commercials. Total commercial avoidance, an average7%, is composed of nearly six- tenths channel switching and four-tenthsdue to other “interruptions.” Program content appears to be the strongestpredictor of avoidance. Gender and age exacerbate commercial avoidancewith men, teens, and younger adults showing above-average churn. Thereis also variation in the relationship of exact, commercial-minute audiencelevels to average-minute audience. High-churn formats produce lowerindices with even lower levels for men. In other words, there is potentialfor bias against particular media formats and particular targets in today’scurrency-based “proxy” measures of commercial audience -- averageminute and AQH. PPM and Apollo estimates would help identify sourcesof bias and alternatives going forward. These results represent progresstoward quantifying the mechanics of commercial avoidance for buyersand sellers. They also demonstrate the value of PPM’s near-passive,direct, and precise capture of persons’-level media exposure.

PROGRESS TOWARDSMEDIA MIX ACCOUNTABILITY

Portable People Meters’ (PPM™) previewof commercial audience results

Roberta M. McConochieLeslie WoodBeth UyencoChris Heider

Roberta M. McConochie, Leslie Wood, Beth Uyenco, Chris Heider


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INTRODUCTION

BackgroundIn 2004 and 2005, worldwide advertisers have become increasingly vocalabout the deficiencies of today’s advertising models and repertoire ofmeasurement tools (e.g. Glock, 2004, Stengel 2004).“Today’s marketing model is broken. We’re applying antiquated thinking andwork systems to a new world of possibilities,” according to Stengel. In Glock’swords: “Marketing metrics are not keeping pace with marketing needs. Weneed to establish new metrics.”Marketers’ concerns, perhaps fueled by challenges of demonstrating thecontribution of advertising to ROI (Campbell, 2005), have inspired one recentinnovative information service: Project Apollo, the working title for theArbitron-VNU planned service, with the collaboration of P&G, would linkmulti-media message exposure and consumer attributes with purchasebehavior to quantify ROI (Dupree and Bosarge, 2004; McConochie, 2005).Beyond the measurement and ROI issues, a portion of the challenges facingmarketers emanates from the abundance of competing, conflicting marketingmessages. Commercial television serves up roughly one minute of non-programming content for every three minutes of substantive information andentertainment in the United States. Across all TV networks, total non-programclutter, including commercials, public service announcements and promotions,comprises over one-quarter of total programming (28.5%, according toPapazian, 2004). Primetime television commercial clutter has increased by60% over the past two decades.Increasing clutter threatens the advertiser with the specter of commercialavoidance, particularly for “intrusive media like TV and Radio” (Ephron,2005). Commercial avoidance may describe the behavior of over half of totalprime-time viewers (Knowledge Networks, 2004).

Research QuestionsThis investigation focuses on understanding consumers’ audience choices vis-a-vis television commercials – whether they remain in the audience or notwhen a commercial or a series of commercials occurs. These results buildtowards estimating advertising ROI. More specifically, the authors address thefollowing key questions:

Progress towards media mix accountability


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1. What is the extent of commercial-churn, specifically avoidance of specificadvertisements? How does this relate to program context?

2. How does commercial-minute audience compare with context programaudience at the average-minute and quarter-hour levels?

3. What is the impact of commercial load (commercials per pod and perhour) on commercial-minute audience?

4. Do the first ads in a pod produce less churn than subsequent ads? Howdoes churn cumulate over units in a pod?

The authors report results for the first two questions in this paper. Questionsthree and four will be addressed in the presentation.Though not an intrinsic part of Project Apollo, this investigation movesanother step towards understanding whether and under what conditionscommercials would actually reach consumers. Project Apollo will collect moreprecise information on ad-specific audience via encoding individual 15, 30 and60-second commercials which can be “read” directly via the Portable PeopleMeter (PPM)SM

MethodsMedia data for the present investigation were obtained via Arbitron’s PortablePeople Meters in Philadelphia in 2003. The in-tab sample includedapproximately 1,000 persons age six and older. This investigation focuses onone week of information, October 20-26, 2003. During this time, the PPMsystem measured six encoded broadcast stations and 26 encoded cable outlets,the largest cable channels in terms of audience. Radio stations were alsomeasured, though these results are not reported in this investigation.The PPM streams of individual persons’ media-exposure episodes, start andstop times, were integrated with independent third-party commercialverification data provided by Nielsen’s Monitor-PlusSM. The Monitor-Pluscommercial start and stop times were merged with PPM start and stop times ofindividual media exposures at the individual person’s level. Over 100,000individual commercials comprise the one-week database of this investigation.These include broadcast network and spot ads and cable network commercials.Local cable commercials are not included in the Monitor-Plus information andtherefore are not part of this research.The present PPM data arguably offer more precise capture of individualpersons’ media exposures than today’s worldwide currency alternatives.According to one of the authors of this paper representing the buying-side ofmedia, Beth Uyenco,



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“While the people meter is acknowledged as the best TV currency system wehave, many have questioned its ability to capture accurate viewing behaviorbecause it requires consistent button pushing for respondents to indicatewhether they are in the viewing room or not. In other words, the people meterdoes not directly track whether people are viewing or not. A viewer may leavethe room during a program break and neglect to push his or her button beforeleaving in which case the people meter identifies the viewing as continuous.The persons-level precision of PPM reflects the fact that individual panelistswear their own individual meters and that the meters passively captureexposure to media. In contrast, traditional Television currency measurestelevision sets rather than persons. Traditional TV measurement also requiresactive participation – either writing in TV use in a paper diary or pushing abutton each time each person starts and stops watching or listening.”The strengths of PPM measurement at the persons’ level also have beenreported to the industry in previous research, e.g. Pellegrini and Purdye, 2004.This investigation’s approximation of commercial audience, the result ofmerging two independent databases, may underestimate “true” commercialimpact on consumer behavior. Also the present results, while arguably themost precise capture of persons-level audience, may be somewhat limited inprecision at the granular level of individual commercials. For example, thepresent minute-level data do not perfectly estimate the audience to individual15 and 30-second commercials. The authors believe that this limitation wouldtend to understate the “true” difference between commercial audiences andprogram audiences. The present one-week investigation thus sets the stage fora fuller, richer look at data from the PPM evaluation in progress in HoustonTexas as well as a hint at what Project Apollo will find when specificcommercials are encoded and the data are precise to the level of 15s as well as30s and 60s.

RESULTS

Extent of Commercial Avoidance: Seven PercentThis investigation defines commercial avoidance as the portion of personsexiting the audience on the base of total audience just prior to the commercialminute. The total commercial-minute audience includes new arrivals,continuing audience, as well as those leaving the audience.Previous limited investigations of PPM data, confined to a subset of brandsand categories, have shown commercial avoidance levels of roughly 5%(McConochie et al, 2004). Including total categories and brands produced anoverall level of avoidance of 7.3%, in the same range as the previous research.



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Since the PPM data track each individual person’s exposure to Television,minute-by-minute, and include each person’s change in media status, in termsof discrete media episodes, PPM also can identify the audience flow from aprevious episode. In this way, the PPM database indicates whether the exitfrom the commercial occurs when consumers switch to another TV channel vs.whether they are no longer exposed to TV. We use the term “channelswitching” to describe the first, and “other interruptions” to describe the latterwhich would occur for example when the consumer turns the set off, exits theroom, or is assaulted by other competing stimuli such as babies crying, dogsbarking, etc.

Majority of Commercial Avoidance Due to Channel SwitchingGiven that most persons in the United States use remote controls to changechannels, and that media and other attention spans appear to be dwindling inthis age of quick-cuts, the authors suspected that the majority of avoidancewould be due to channel switching. It was.Of the 7.3% total avoidance, shown below, 4.1 points came from channelswitching. The remaining 3.2 points of avoidance were due to otherinterruptions of audience to television use.

Total Commercial Avoidance 7.3%Channel Switching 4.1%Other Interruption of Audience 3.2%

On the base of total commercial avoidance, in other words, setting the 7.3% to100%, nearly six of every 10 commercial-avoidance episodes are followed bychannel switching. Channel switching levels are similar for broadcast networkaffiliate and Cable channels (55% for Broadcast and 59% for Cable channels)as shown in figure 1.



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Figure 1CHANNEL SWITCHING COMPRISES NEARLY 60%

OF TOTAL COMMERCIAL AVOIDANCE

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Philadelphia PPM Evaluation, 10/20/03 - 10/26/03, Mon-Sun 7AM-1AM, P6+

The remaining portion of avoidance resulting from other interruptions ofviewing comprises 45% for Broadcast and 41% for Cable channels. In otherwords, the majority of audience exits from a commercial arguably reflect adecision to look for alternative Television content.To this end, the authors mine the data to look for the variables that account forswitching and other avoidance, variables that provide clues for media sellersand buyers to retain their commercial audience. First we look at variations bybroad dayparts.

Daypart Variation Accounts for a Small Portion of AvoidanceGiven that viewing and other consumer behavior varies by daypart, weexpected variation in commercial avoidance as well. The authors suspectedhigher avoidance when people were able to sit in front of the TV, focus on thescreen, and potentially react to content as is the case during Prime Time. Atother times, we thought that less viewing with less concentration and morecompeting activities might result in less avoidance, though possibly more



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“other interruptions” in viewing. Indeed the results showed some variation inavoidance in the expected direction.As expected, the highest levels of avoidance occur during eveningprogramming. During this time, total commercial avoidance exceeded therespective averages for broadcast and cable channels. Total avoidance for8 pm – 11 pm broadcast was 8% vs. the broadcast average across dayparts of7% (figure 2). For 8 pm – 11 pm cable, total avoidance was 11% vs. the cableaverage of 10%. The slight differences imply that broad dayparts account for asmall portion of variance in commercial avoidance

Figure 2FROM 8PM-11PM, TOTAL COMMERCIAL AVOIDANCE AT HIGHEST LEVELS

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Figure two also shows consistent difference in commercial avoidance levelsbetween Cable and Broadcast channels. These differences and the likely causalfactors are discussed further below.



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Channel Switching also Highest During Evening, Prime TimeAlso consistent with expectations, channel switching away from commercialsreach maximum levels during Prime Time, and do so for both Broadcast andCable channels (figure 3). For Broadcast, the Prime Time channel-switchinglevel was 4.9% as opposed to less than half that in the morning. Similarly forCable, Prime-Time switching was nearly twice that for mid-morning and earlyafternoon.

Figure 3EXTENT OF CHANNEL-SWITCHING ALSO HIGHEST BETWEEN 8 – 11 PM

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Next, we take a deeper dive into the daypart data to look for furtherexplanatory clues to explain variations in commercial avoidance. The hour-by-hour avoidance data against the background of audience average-quarter-hourratings build shed further light on avoidance variation.

Broadcast Commercial Avoidance Builds to Evening HighThe hour-by-hour avoidance results show a build over the day peaking from7 pm to Midnight. Some of the lowest avoidance levels occur at 8 am (6%), vs.the 8% levels between seven pm and Midnight.



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Figure 4BROADCAST NETS’ AVOIDANCE LEVELS HIGHEST DURING EVENING

5 .8 5 .5 5 .5 5 .7 5.9 5 .8 5.7 5 .5 6.2 6 .5 6 .4 5.97 .7 7.5 7 .6 7.4 7 .0

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The cable hour-by-hour data also show a smooth build to the highestavoidance levels at 9 pm (11%) which exceed those occurring over most othertimes (figure 5). These avoidance curves for both Broadcast and Cable appearto confirm our hypothesis. During evening viewing, when people focus ontheir televisions, perhaps more so than at other times of daily routines, there isalso more channel switching and commercial avoidance in general.



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Figure 5CABLE COMMERCIAL AVOIDANCE ALSO HIGHEST DURING EVENING

10 .48 .7 8.9 8.1 8 .3 8.9

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Despite the build in avoidance over the media day, daypart variation appears toaccount for a small portion of commercial avoidance. But this small variationis quite likely related to programming content as well as daily routines. Wetherefore progress to programming to help explain commercial avoidance. Welook first at channel-by-channel variation then at program genres and specificprograms where there are sufficient commercial data.

Broadcast Channels, Genres and Programs Show SubstantialVariation in AvoidanceProprietary research conducted by DDB leads the authors to predict that genresand program content would account for substantial variation in commercialavoidance. Results confirm this prediction.The extent of commercial-audience variation across broadcast channels isroughly on par with that for dayparts. The highest overall avoidance level is8% for Fox vs. a low of 6% for CBS (figure 6). Fox also shows the highestswitching level (5%) vs. a low of 3% for low-avoidance ABC. The range ofavoidance across broadcast channels is slightly higher than that for dayparts.



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Figure 6AVOIDANCE AND CHANNEL-SWITCHINGVARY ACROSS BROADCAST NETWORKS

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Focusing on specific program genres across broadcast channels yields greatercommercial-churn variation.

Program Genre and Individual Program Content ExplainSubstantial Variation in Broadcast Commercial AvoidanceThe impact of genre on avoidance gives more pronounced “effects” than forindividual stations. For example, sports events show 9% overall avoidancelevels vs. only 4% for daytime drama. The range of switching is also large:from 5% for sports events and adventure programs vs. only 1% for daytimedrama (figure 7). In other words, sports and other action programming showmore than twice the commercial churn of Soap Operas, confirming thehypothesized powerful impact of program content on commercial stickiness.



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Figure 7BROADCAST GENRES’ TOTAL AVOIDANCE RANGES

FROM 9% FOR SPORTS TO 4% FOR DAYTIME DRAMA

5.1 5.4 4.4 4.52.7 2.9 2.6 2.6

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Variation of commercial avoidance among specific programs is even greaterthan that for genres and channels, further confirming the impact of programcontext on commercial audience. For example, commercial avoidance rangesfrom 13% to 6% across a set of fall football and baseball events (figure 8). TheBaseball World Series foregone-conclusion event earns the highest avoidancescore at 11%. A pre-game event for football shows the second highest“avoidance” at 14%. Perhaps not surprising, the Philadelphia home teamfootball game, a close scoring win for the Philadelphia Eagles, shows thehighest holding power with only 6% commercial-audience loss. Variation inchannel-switching shows similar large variation – from 8% switching for theCBS NFL pre-game to only 3% switching for the Eagles game. The largevariation implies the importance of program context in planning effectivecommunications campaigns and in marketing commercial inventory on theselling side.



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Figure 8ONE-SIDED WORLD SERIES GAME YIELDS TWICE THE

AVOIDANCE OF HOME-TEAM EAGLES FOOTBALL

7.4 7.5 7.95.8

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In contrast to the large variation among specific sports programs, individualsoap operas show more consistently low avoidance. All soap opera programsfor this investigation show 5% or less total avoidance: The range is from 5%for The Bold and the Beautiful to 3% for The Young and the Restless and Asthe World Turns (figure 9). In departure from overall averages, channelswitching was less than half that of total avoidance, ranging from a “high” of2% to a low of under 1%. The implied higher levels of “other interruptions” toviewing appears consistent with daytime routines of house husbands andwives, dealing with a relatively constant flow of typical interruptions ofchildren, telephones, dogs, visitors, etc.



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Figure 9DAYTIME DRAMA SHOWS CONSISTENTLY LOW TOTAL AVOIDANCE

AND CHANNEL SWITCHING

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Channel Format and Genre Yield Even More Variation in CableCommercial AvoidanceTurning to Cable channels, we see large variation across the channels, more sothan among Broadcast stations. The format-driven Cable networks providemarkedly different contexts for marketing messages with large differences incommercial avoidance. At the high end, VH1, Weather Channel, and MTVshow commercial avoidance levels of over 14%, nearly three times that for the5% levels of A&E and Lifetime (figure 10). As with the Daytime Dramagenre, A&E and Lifetime show extremely low levels of channel switching, 3%or less.



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Figure 10AVOIDANCE FOR MUSIC AND WEATHER FORMATSTHREE TIMES HIGHER THAN FOR A&E, LIFETIME

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Genre of programming across cable channels also shows substantial variationin commercial avoidance. Talk and music programming show the highestlevels, over 14% total avoidance with Talk showing the highest level ofswitching, 13%. Cable genres with the least avoidance are similar to those forBroadcast channels: General Drama and Sitcoms, both with about 7% totalavoidance.



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Figure 11SIMILAR DIFFERENCES IN AVOIDANCE RANGE

FOR CABLE PROGRAM GENRES


Thus far, program content appears to explain a substantial portion of variationin commercial avoidance and churn due to channel switching, far more thanthat for daypart per se. Obviously many of the high vs. lower-avoidancechannels target quite different demos. So we proceed to look at the impact ofage and gender on avoidance. The expectation here is for higher avoidance foryounger demos and for Men. That’s generally what the results show.

Teens, Adults 25-44, and Men Show Highest CommercialAvoidanceConsistent with expectations, Teens 12 - 17 and Adults under 45 show thehighest avoidance levels of any broad age group, for both Broadcast and Cablechannels (figure 12). The highest broadcast avoidance is for Teens with 9%total avoidance. For Cable, Adults 35-44 show the highest level, 12%. Asexpected, the lowest avoidance levels occur for the oldest panelists, 65+ whoshow only 5% avoidance for Broadcast and 8% for Cable.



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Figure 12COMMERCIAL AVOIDANCE HIGHEST FOR TEENS, YOUNGER ADULTS

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Philadelphia PPM Evaluation, 10/20/03 - 10/26/03, Mon-Sun 7AM-1AM

Also as expected, Men in virtually every age group show higher avoidance(figure 13). From age 25 and up, Men show slightly or considerably highertotal commercial avoidance than women. The greatest gender gap occurs forcable-channel avoidance for older men, 55+. For example, Men 65+ show totalcable commercial avoidance levels of 11% vs. 6% for Women.Though some of the age/gender differences are substantial, the estimatedimpact of age/gender is not as extreme as that for program content with itsthree-fold multipliers of commercial-audience avoidance.



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Figure 13MEN SHOW HIGHER AVOIDANCE THAN WOMEN, ESPECIALLY FOR CABLE

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Philadelphia PPM Evaluation, 10/20/03 - 10/26/03, Mon-Sun 7AM-1AM

Currency Measurement Units vs. Commercial RatingsWe turn now from the topic of churn to that of the unit of measurement andconsider the appropriateness of today’s average-minute and average-quarter-hour currency “proxy” measures for commercial minute audience.Two factors potentially affect the relationships between commercial-minuteaudience and currency measures: computation rules and viewer behavior.

Computation rules for minute-level processing. Procedures for the PPM2003 database of this investigation awarded one minute of credit for each30 seconds of encoded-media capture. Arbitron’s investigations of thePhiladelphia data indicate that the vast majority of media episodes extendwell beyond 30 seconds. Therefore PPM processing would round up 30seconds to one minute in what we estimate to be well under half ofindividual minute-level computations.To compute average minute estimates for this investigation, we calculatedthe arithmetic average audience over the 15 minutes in which thecommercial occurred. In contrast, the commercial minute estimates focuson the exact-minute audience in which the specific commercial occurred.



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Average quarter-hour processing. For this investigation as for today’squarter-hour currency, a minimum of five minutes of audience per quarter-hour, whether adjacent minutes or not, earned credit for the entire fifteenminutes. The authors’ guesstimate that the potential triple crediting ofobserved raw media-use data probably occurs in fewer than half ofpossible instances.

Viewer behavior. We have seen above that viewer characteristics affectchurn. Specifically, the genre forms of cable channels, and the viewingbehavior of younger viewers and of men produce above averagecommercial avoidance, particularly for sports programs, and formats ofcable channels such as music and weather.

On average, we would expect exact commercial minute estimates to closelymatch average-minute estimates. However, high churn could occur forprogram content as well as for the commercials. We suspect that this is thecase for those contents comprising the context for the high-churn commercials:sporadic, episodic, briefer discrete media-use episodes. If and when this is thecase, then it is possible that the commercial minute estimates could indexabove 100 against the base of quarter-hour estimates. If this effect occurs, itwould focus on those formats and demos that have shown higher-than-averageavoidance and channel switching above.To the extent that the indices for commercial minute closely match those forthe index base, average minute and average quarter, the currency procedureswould appear to be a good proxy for commercial audience – as they areintended to be. To the extent there are differences, especially complex ones,then today’s proxy measures for buying and selling may bias the selling,planning, and buying of specific media and targets.

Commercial Minute Audience Index against Average Minute:98 for Broadcast; 89 for CableBecause of the observed evidence of commercial avoidance, we expectcommercial-minute audience would be less than that for average-minuteprogram audience. For the quarter-hour indices, because of the greaterrounding-up of quarter-hour estimation, we expect commercial minuteestimates to fall further under the AQH standard.We report these comparisons as indices – computed against the appropriatebase of average-minute data or against the base of AQH. Audience wasmeasured in terms of GRPs for these estimates.



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As expected, commercial minute data index at slightly less than 100 vis-a-visthe average minute data, 98 for Broadcast and 89 for Cable channels,reflecting the higher levels of audience churn for Cable formats (figure 14).The results imply that average minute is a good stand-in for commercialaudience for broadcast, but possibly not so good for cable channels.

Figure 14COMMERCIAL-MINUTE GRP INDEX < 100 AGAINSTBASE OF AVERAGE MINUTE: LOWER FOR CABLE

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Figure 14 also shows the index of AQH programming content against the baseof commercial minute. Given the greater rounding-up factor for quarter-hourprocessing, we expected that the AQH index would be over 100. This is thecase for Broadcast, with an index of 108. However, it is not the case for Cableprogramming which indexes at 96% against the average-minute base. Wesuspect this reflects the relatively brief discrete media-use episodes for anumber of cable-channel formats. Again, these results point to theshortcomings of average-quarter-hour as a proxy for commercial minute – forboth Broadcast and for Cable.When we display GRP indices against the base of AQH program viewing, wesee the expected results inferred from the previous comparisons – and thelimitations for today’s U.S. local-market use of AQH to plan and buy



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commercial time. Commercial minute indices for both Broadcast and Cableare well under 100: 91 and 93 against AQH the base of program viewing. TheBroadcast average-minute index is also under 100 (at 93% of AQH). However,consistent with the previous index results, the Cable average-minute GRPindexes above 100, exceeding the AQH audience.

Figure 15COMMERCIAL-MINUTE GRPS INDEX

LOWER THAN AVERAGE MINUTE AGAINST BASE OF AQH

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Given the impact of program genre and channel on commercial avoidance(reported above), we expected these to make a difference on commercial indexvalues. They do. For the remaining results, we focus on indexing againstaverage-minute data.As expected, the broadcast channels with the highest levels of commercialavoidance do show lower indices against the average-minute audience (figure16). For example, the Fox affiliate shows an index of 96 in contrast with the 98index for all of the “Big Three” affiliates, NBC, CBS, and ABC. In otherwords, the bias of using average minute for placing commercials is probablynot the same for different channel formats, nor for specific program content.



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Figure 16SLIGHTLY LOWER COMMERCIAL-TO AVERAGE-MINUTE INDEXES

FOR NETWORKS WITH MOST COMMERCIAL AVOIDANCE

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Philadelphia PPM Evaluation, 10/20/03 - 10/26/03, Mon-Sun 7AM-12Mid, P6+

The same sort of expected results occur for the high-churn Cable-channelformats. For example, music channels MTV and VH1 show indices of 89 and86 against their respective program minute audiences (figure 17). In contrast,Nickelodeon, A&E and Lifetime are close to or slightly higher than 100 indexlevels.



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Figure 17EVEN LOWER COMMERCIAL-TO AVERAGE-MINUTE INDICES

FOR HIGH-CHURN FORMATS

102.1 100.1 99.3 98.5 98.0 97.0 96.7 96 .6 95 .4 93.688.6 86.0

0

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Further investigation confirms that, as expected, gender plays a part inexplaining commercial-audience index levels. Results imply an interactionbetween gender and media form – Broadcast vs. Cable channels (figure 18).Men’s viewing (vs. women’s) produces slightly lower broadcast-channelindices for commercial minute against average minute (a 97 index vs. 98 forWomen 18+). For Cable channels, the gender gap is greater: 86 for Men 18+vs. 92 for women.



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Figure 18GENDER DIFFERENCES AFFECT COMMERCIAL-MINUTE INDEX LEVELS

AGAINST AVERAGE MINUTE

98.6 97.0 96.2 96.8 96.7 97.691.3 86.3 82.4 84.2 83.5 83.0

020406080

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99 .3 97 .0 97.5 98 .5 99 .1 99.191 .9 88 .6 90.8 92 .9 91 .5 94.3

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MEN: Broadcast average = 97 Cable average = 86

WOMEN: Broadcast average = 98Cable average = 92


We also expect from the commercial-avoidance/ churn results that youngerdemos might also show slightly lower index levels than older. However noconsistent age effects are discernable in the results shown in figure 18. It maybe that this one-week’s worth of information on relatively small sample sizesmay not be sufficient to discern age effects. We look forward to results fromthe PPM evaluation in Houston and to Project Apollo results to assess theimpact of age on audience churn and commercial-minute index levels vis-a-visaverage minute estimates.



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SUMMARY AND CONCLUSIONS

These results offer important early insights from PPM to both the sellers andbuyers of media. Detailed PPM data confirm that the majority of commercialavoidance reflects channel switching, as opposed to other interruptions of TVuse such as turning off the TV set or leaving the room where the TV is on. Inother words, the majority of audience exits from a commercial arguably reflecta decision to look for alternative Television content. The extent of commercialavoidance depends upon media program content. Drama programs, especiallythe daytime soap operas, show far less avoidance than cable music channelsand pre-game sports programs. Simply put, some contents appear to possessgreater stickiness than others.Gender and age exacerbate program impact on commercial avoidance withMen and younger viewers showing somewhat higher avoidance. In contrast tothe overall average of 7% commercial avoidance over all commercials on allencoded channels, high-churn content can produce three times as muchavoidance.Given the variation in commercial avoidance, it comes as no surprise that thereis also variation in the relationship of exact, commercial-minute audiencelevels to average-minute audience. High-churn formats produce lower indiceswith even lower levels for men than women. In other words, there is thepotential for bias against particular media formats, program contents, andparticular targets in today’s reliance on currency proxy measures, averageminute and AQH, to predict commercial audiences. PPM and Apollo estimateswould help identify the bias potential and offer alternatives going forward.

REFERENCES

Bosarge, John and Dupree, Linda. (2004). Media on the Move: How to Measure In- andOut-of-Home Media Consumption. Consumer Insight, ACNielsen, Winter.

Campbell, Mike. (2005). Is ROI Dead? Admap, March.

Ephron, Irwin. (2005). The Ephron Letter, January.

Glock, Bernhardt. (2004). Keynote Address to ESOMAR/ARF. Geneva, June 18.

Knowledge Networks. (2004). The People Look at Television, December.

McConochie, R., Uyenco, B., Wood, L. and Heider, C. (2004). Toward Accountability.Proceedings of the ARF Conference, November.

McConochie, Roberta. (2005). The Difficult Balance of Media Measurement. Admap,April.

Papazian, E. (2004). TV Dimensions. Media Dynamics, New York.



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Pellegrini, Pasquale and Purdye, Ken. (2004). Passive vs. Button Pushing. Proceedingsof the ESOMAR/ARF WAM Conference, June.

THE AUTHORS

Roberta M. McConochie is Director, Portable People Meter Client Relations, ArbitronInc., United States.

Leslie Wood is President, Leslie Wood Research, United States.

Beth Uyenco is US Director of Strategic Resarch and Analysis, OMD, United States.

Chris Heider is Senior Research Analyst, Arbitron Inc., United States.

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