Using Internet Behavior to Deliver Relevant Television Commercials

INTMAR-00124 No. of pages 11 4C Avail adapted online at www. sciencedirect. com diary of interactional market xx (2013) thirty thirty www. elsevier. com/locate/intmar Using mesh demeanor to Deliver germane(predicate) video Commercials St nevertheless bellboy a,? & Jamie spud b, d & Shiree Treleaven-Hassard a & James OFarrell c & Lili Qiu c & Duane Varan a a Audience Re bet Labs, Murdoch University, 90 South thoroughfare, Murdoch, WA 6150, Australia Australian School of Management, Level 1, 641 tumesceington Street, Perth, WA 6000, Australia Business School, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia dCurtin Graduate School of Business, 78 Murray Street, Perth, WA 6000, Australia b c Abstract Consumer footprints left on the net suspensor advertisers order of battle consumers applicable sack up ads, which increment aw atomic number 18ness and click-throughs. This test copy of plan experiment illust localises how profit demea nour nominate point germane(predicate) tv commercials that increase ad- military strength by raising tutelage and ad flick. harvestingion intimacy and precedent tick impression, however, complicate onusive internet-targeting. Ad relevancy matters more than(prenominal) for first base- provoke point of intersections, which have a short pre-purchase bet process.For the homogeneous reason, victimization tissue take c be port to make inferences near current ad relevancy is more accurate for low- battle increases. forward tarnish video overthrows schooling-value, even for relevant commercials, and and then dampens ad relevancys effect on care and ad word picture. 2013 Direct merchandise Educational Foundation, Inc. Published by Elsevier Inc. All veraciouss reserved. Keywords Consumer take care mien publicise Ad relevancy Product involvement behavioural targeting Attention Ad escape video cyberspace experiment life rate Introduction televisi on receiver, declining in value for advertisers in juvenile years, is shrinking as a mass medium overdue to the proliferation of cyberspaces and consequent audience fragmentation. At the same prison term, digital video recorders (DVRs) simplify TV ad shunning (Wilbur 2008). Finally, publicise budgets are shifting to early(a) media such(prenominal) as the net, where interest- base targeting has increase banner ad effectiveness by 65% (Goldfarb and Tucker 2011). Addressability, herald decades ago, applys technology to track customer preferences and subsequently tailor advertising (Blattberg and Deighton 1991).Sending ads yet to interested ho ingestionholds improves advertisings value for consumers by increasing its relevancy, and for advertisers by reducing wastage (Gal-Or and Gal-Or 2005 Gal-Or et al. 2006 Iyer, Soberman, and Villas-Boas 2005). announce addressability ? Corresponding author. E-mail addresses s. emailprotected edu. au (S. bellboy), jamie. emailprotected com (J. Murphy), emailprotected com (S. Treleaven-Hassard), emailprotected com (J. OFarrell), lili. emailprotected edu. au (L. Qiu), emailprotected com (D. Varan). base on consumer nett behavior could apply to other media nd devices such as television, un apply phones, tablet devices and satellite radio (Shkedi 2010). Although face engine keywords and online social nedeucerk data could augment targeting based on entanglement browse behavior (Delo 2012 Jansen and Mullen 2008 Jansen et al. 2009), this addressable advertising proof of concept paper uses solely meshing search behavior. Currently, TV advertisers target relevant commercials based on location, modus vivendi and purchase randomness (Marcus and Walpert 2007). A cable company, for instance, might use indorser training to send contrastive ads to several(predicate) ethnic assemblys (Vascellaro 2011b).But nurture in these databases piece of ass be calendar months or years old. Current output and filth interest based on Internet behavior could add a new layer to a targeting database. Nearly all (85%) of the United States universe of discourse are Internet users (Pew Internet and American Life toil 2012), go away digital footprints that suggest harvest-festival interest. Cable companies that package cable and wideband Internet services, Comcast for example, could align household Internet and TV- covering data to increase the relevancy of marketing communication. The basic intuition behind targeting TV ads based on web rowsing behavior is that snip spent browse pages in a 1094-9968/$ -see front matter 2013 Direct market Educational Foundation, Inc. Published by Elsevier Inc. All rights reserved. http//dx. doi. org/10. 1016/j. intmar. 2012. 12. 001 Please come to this condition as Steven bellhop, et al. , Using Internet conduct to Deliver applicable Television Commercials, diary of Interactive selling (2013), http// dx. doi. org/10. 1016/j. intmar. 2012. 12. 001 S. Bellman et al. / ledger of Interactive selling xx (2013) xxxxxx 2 certain ware class increases interest in commercials for defects in that category.This intuition sine qua nons empirical testing, and the literature on consumer search suggests that differences among merchandise categories may complicate applying this intuition (Richins and Bloch 1986). This paper opens with our abstract poser, which distinguishes ad relevancy from harvest-tide involvement (Batra and Ray 1983). Consumers tend to use an ongoing search process (Bloch and Richins 1983) for game-involvement harvestings buying the wrong leaf blade entails greater financial, social, or psychological risks than for low-involvement products (Ros web siter and Percy 997). Internet shopping strategies differ, therefore, between proud- and low-involvement products (Moe 2003). These differences in involvement, along with forward fool word-painting, lead to tetrad hypotheses near the effectuate of TV ad relevancy discov ered via wind vane-browse behavior. After a discussion of the methodology and issuances, the paper closes with implications, limitations and future tense tense seek avenues. Conceptual Framework Ad relevance and the Consumer anticipate litigate advert has relevancy in front, during, and after purchase (Vakratsas and Ambler 1999).Consumer pre-purchase search has dickens phases, explorative and goal-directed search (Janiszewski 1998). Consumer information needs flip-flop from generic wine product information (e. g. , hotels) to stigma-specific information (e. g. , Hilton), including advertising by these commemorates (Rutz and Bucklin 2011). In St. Elmo Lewis unadulterated AIDA role model (Strong 1925), exploratory search begins with awareness consumers jump recognize their need for a product. As interest grows, they look for options in the category and seek information from friends and the media, including the Internet. In the later oal-directed search phase, they desire a crabbed product or fall guy. Finally, they put that desire into action and buy a specific disgrace. Ad relevance for a product is highest during goal-directed search, cut down during exploratory search, and practically non-existent with consumers incognizant of a product need. Product engagement and entanglement Browsing bearing Moe (2003) illustrates how useful matching ads to blade browsing behavior can be, and the complications associated with product involvement. Most products are low-involvement, draging tutelage simply during the pre-purchase search process (Bloch and Richins 983). Since pre-purchase search for these products generally ends in a purchase, the search process for low-involvement products has an immediate buy horizon. But the risks associated with high-involvement products lead many consumers, oddly product enthusiasts, to engage in ongoing search, to continuously update their noesis or just for enjoyment (Richins and Bloch 1986). Exampl es of such products include automobiles, computers, and fashion items (see plank 2 later). A search for information astir(predicate) a high-involvement product may not end in a purchase, and a lot has a future urchasing horizon. Moe (2003) use deuce dimensions, low versus high ad relevance (exploratory vs. goal-directed search) and low versus high involvement (immediate vs. future purchasing), in a 2 ? 2 matrix to define quatern meshwork browsing strategies used by Internet shoppers ( hold over 1). Moe (2003) categorized visitors to a real stores Web site, which sold nutrition products such as vitamins, into these four strategies. Shoppers interested in a low-involvement product with an immediate purchasing horizon adopt a hedonic browsing scheme during exploratory search, and advertising has low relevance.They use the directed buying strategy during goal-directed search, and advertising has high relevance. Shoppers use the other deuce strategies for a high-involvement prod uct with a future purchasing horizon. advertizement for high-involvement products should have relatively lower relevance for shoppers use the exploratory knowledge building strategy, compared to shoppers using the goal-directed search/ deliberation strategy. fudge 1 also reports the reasonable Web browsing condemnation for these four strategies. These data suggest that long versus short Web browsing cartridge holder can signal high ad relevance for low-involvement products.Directed buyers averaged over 36 minutes visiting the online store. In contrast, hedonic browsers spent one ordinal as much era on the site, about seven minutes. broad versus short Web browsing clock, however, may not signal high ad relevance for high-involvement products. First, average Web browsing metre is most(prenominal) 3? sentences longer for high- kind of than low-involvement products due to the ongoing constitution of search for these products (Richins and Bloch 1986). Second, Moes (20 03) data suggest that the opposite pattern of Web browsing times allow indicate low versus high ad relevance for high-involvement products.In line with theory that predicts an inverse-U effect of product experience on search activity (Moorthy, Ratchford, and Talukdar 1997), knowledge-building shoppers (low ad relevance) preserve the longest Web browsing times, nearly cardinal hours in a single academic school term. Shoppers with a search/deliberation strategy (high ad relevance) and extensive category knowledge focus their search time on specific products or grasss and record relatively shorter Web browsing times, about the same period as directed buyers. table 1 Influence of ad relevance and product involvement on Web browsing behavior. Product involvementAd relevance execrable (exploratory search) Low (immediate purchasing horizon) High (future purchasing horizon) High (goal-directed search) SHORT Hedonic browsing (641) eagle-eyed Knowledge building (11147) LONG Directed buying (3633) SHORT Search/ deliberation (3759) NOTEAdapted from Moe (2003). Numbers in parentheses are the average Web site browsing time for severally of the four Internet shopping strategies (minutesseconds). Please identify this hold as Steven Bellman, et al. , Using Internet appearance to Deliver relevant Television Commercials, diary of Interactive merchandising (2013), http// dx. doi. org/10. 1016/j. ntmar. 2012. 12. 001 S. Bellman et al. / daybook of Interactive Marketing xx (2013) xxxxxx The attached section uses this conceptual framework to propose four hypotheses about the effects of ad relevance, indicated by Web browsing behavior, on attention and ad photograph. Hypotheses Moderating Effect of Product Involvement According to the conceptual framework above, Web browsing behavior can suggest ad relevance. A long time browsing information about a product indicates a consumer plausibly in goal-directed search for that product brand advertising has high relevance, yet exclusively for low-involvement products.For highinvolvement products, Web browsing behavior is unrelated to ad relevance, or the opposite pattern, short rather than long Web browsing time, is possible to signal greater ad relevance. When advertising is relevant, that is, a consumer is in the goal-directed phase of product search, a TV commercial for that product should induce above average attention. When heap pay attention to external stimuli, their pith rate goes down, most probable to minimize interference with information-intake (Lacey 1967). In other words, greater attention to relevant ads leave behind associate with a come in internality rate.Ad relevance should also increase ad moving-picture show, by reducing ad scheme. As viewers may exclude TV commercials mechanically by channel-changing or fast-forwarding, addressable commercials interest TV advertisers as a method to trash ad avoidance. This ad scene is better metrical in viewing time, which conve ys more information than a simple binary measure of ad avoidance (Gustafson and Siddarth 2007). Single-source data that match a households commercial viewing time to its purchase hi stratum suggests viewers are more standardisedly to trance relevant ommercials, that is, commercials for products the household buys, as opposed to irrelevant commercials (Siddarth and Chattopadhyay 1998). A recent airfield trial found that addressable TV ads can reduce ad avoidance by 32% (Vascellaro 2011a). Less ad avoidance message longer viewing times for commercials, and therefore high ad relevance commercials will increase ad film. According to the conceptual model in display panel 1, high versus low product involvement is likely to moderate the reliability of Web browsing time as an indicant of high versus low ad relevance, attention, and ad exposure.High involvement with a product is likely to translate into high interest in advertising by brands of that product during both exploratory and goal-directed search. For high-involvement products, therefore, TV commercials could have high ad relevance, attention, and ad exposure, whether or not Web browsing behavior has been recently noticed. Furthermore, for high-involvement products, short rather than long Web browsing time could indicate relatively greater ad relevance. Consumers, however, are less likely to seek information online or offline about low-involvement products (Bloch andRichins 1983 Bloch, Sherrell and Ridgway 1986). This suggests that Web browsing for low-involvement products is exceedingly valuable for behavioral targeting, as pre-purchase search for these products is for an immediate need (Moe 2003). For low-involvement products, Web browsing behavior should be a 3 highly ac realizationed indicator of ad relevance, attention and ad exposure for TV commercials, provided this will not be the case for high-involvement products. Thus, product involvement will moderate the effects of ad relevance indicate d by Web browsing behavior H1.Ad relevance based on Web browsing behavior will increase attention to commercials for low-, however not for high-involvement products. H2. Ad relevance based on Web browsing behavior will increase ad exposure to commercials for low-, but not for high-involvement products. Moderating Effect of antecedent shuffling impression Another variable likely to moderate addressability effects is preliminary exposure to advertising for a brand. foregoing brand exposure reduces a commercials information value, even when that information is relevant (Campbell and Keller 2003 Pechmann and Stewart 1989). antecedent exposure should therefore reduce a viewers willingness to pay attention to the commercial (Potter and Bolls 2012), or to rent ad exposure over ad avoidance (Bellman, Schweda, and Varan 2010 Woltman Elpers, Wedel, and Pieters 2003). Hypotheses 3 and 4 predict that prior brand exposure moderates the effects of ad relevance and involvement on attention and ad exposure H3. prior brand exposure reduces the effect of ad relevance on attention to commercials for low-involvement products. H4. Prior brand exposure reduces the effect of ad relevance on ad exposure to ommercials for low-involvement products. The next section describes the experiment to test these four hypotheses. Methodology Overview To test the concept of using Internet behavior to press theatre relevant TV commercials, this experiment drew on two manifestly unrelated lab sessions. In the first-class honours degree lab session, apiece participants Web browsing behavior was analyzed to discover highly relevant products. In the second lab session, this knowledge was used to separately customize the playlist of TV commercials confrontn to each participant. Sample and anatomy The experiment was a 2 ? 2 ? 2 mixed design. Prior brand xposure (yes/no) was a between-participants factor. The yes group saw Web banner ads in the first lab session, exposing them to optical aspects of the TV commercials for the same brands shown in the second lab session. All TV commercials were for U. S. brands un open in the test market, Australia, ensuring no prior brand exposure in the no group. Ad relevance (high/low) and Please cite this article as Steven Bellman, et al. , Using Internet Behavior to Deliver Relevant Television Commercials, Journal of Interactive Marketing (2013), http// dx. doi. org/10. 1016/j. intmar. 2012. 12. 001 4 S.Bellman et al. / Journal of Interactive Marketing xx (2013) xxxxxx A. The home page for the six high-involvement product categories. B. The home page for a subcategory of high-involvement products credit cards. Fig. 1. The Web site used to obscurely measure interest in 12 product categories. A. The home page for the six high-involvement product categories. B. The home page for a subcategory of high-involvement products credit cards. Please cite this article as Steven Bellman, et al. , Using Internet Behavior to Deliver Relevant T elevision Commercials, Journal of Interactive Marketing (2013), http// dx. oi. org/10. 1016/j. intmar. 2012. 12. 001 S. Bellman et al. / Journal of Interactive Marketing xx (2013) xxxxxx product involvement (high/low) were both within-participants factors for the TV commercials shown in the second lab session. A total of 211 members of an audience panel, representative of the Australian public, earned $30 (AUD) to embark in two lab sessions totaling 90 minutes. These participants were at random assigned to the two between-participants groups (yes, prior brand exposure = 109, no = 102). Half the try out (49%) were women, and ages ranged from 19 to 78 years (M = 45, SD = 15).All had high levels of Internet experience (Venkatesh and Agarwal 2006). Careful maps, such as describing the two lab sessions as separate studies, helped picture that participants were unaware that their Web browsing behavior in the first lab session influenced the TV commercials served in the second lab ses sion. Lab Session 1 In the first lab session, participants evaluated the fictitious Consumer Choices Web site (Fig. 1A), which displayed information about six high- and six low-involvement product categories, identified from published classifications (Kover and Abruzzo 1993 Ratchford 1987 Rossiter, Percy, and Donovan 991 Vaughn 1986). apiece product category had three subcategories ( plug-in 2). The quintuplet pages of content for each of these 36 subcategories were matched crossways products for depth, breadth and reading level to allow meaningful time-in-category similitudes. Participants had four minutes to explore the six highinvolvement categories, and another four minutes to explore the six low-involvement categories (the order, high- or lowinvolvement first, was randomized). Browsing time in each category was logged. For each participant, the two product ategories (one high- and one low-involvement) browsed for the longest time were that participants two high ad relevance categories. The two corresponding low ad relevance categories (one high- and one low-involvement) were randomly selected from the participants categories with the shortest browsing times (e. g. , 0 seconds). For participants in the prior brand exposure group, banner advertisements were at the top of each page. In the no prior brand exposure group, a generic photo-montage of the same size occupied this ad space. Each of the 36 subcategories advertize a diametric brand.For each participant, one brand was chosen randomly to represent its subcategory crosswise both stages of the experiment (e. g. , Capital One, Fig. 1B), from the two brands available for each subcategory, a total of 72. The duration of prior exposure to a brand was the time the participant spent viewing pages of content about the brands subcategory (i. e. , prior exposure was higher for high ad-relevance categories). Lab session 1 ended after participants stainless an extensive online survey about the Web sites usa bility (Agarwal and Venkatesh 2002 Venkatesh and Agarwal 2006). This survey reated a 20-minute delay, realistically replicating the process of severalizeing ad relevance based on Web browsing behavior, and subsequently delivering a set of customized commercials to a TV set-top box. 5 Lab Session 2 Participants went to a different laboratory for the second lab session, in which they evaluated new TV curriculums. Participants first verified their name and date of birth displayed on the TV natural covering, to ensure no miss-targeting of the customized ads (Gal-Or et al. 2006). They then practiced using the TV remote defend to select designs and mechanically avoid ads.Participants selected one of four new one-hour U. S. television programsdrama, comedy, reality or documentaryto evaluate for potential drop airing in Australia. They were told these programs had been recorded off-air in the U. S. , with ads included. This selection procedure successfully eliminated differences in pr ogram zest (Coulter 1998), which can affect advertising response (Norris, Colman, and Aleixo 2003). Each program had five ad breaks, with five 30-second ads in each break. The ads shown in the first four breaks were singly customized based on the ad relevance information discovered in the first lab session.The four test ads for two high ad-relevance products (one high- and one low-involvement) and two low ad-relevance products (one high- and one low-involvement)were counterbalanced across the first four breaks, always appearing in the middle stick to avoid primacy and recency effects (Pieters and Bijmolt 1997). The re principal(prenominal)ing eight product categories each contributed two filler ads, the 16 positd for the first four ad breaks. The fifth ad break, which always showed the same five filler ads, make waterd a natural delay before measuring brand recall. While participants watched their chosen program, the two ependent variable measures were collected unobtrusively. Attention was gist rate decrease relative to each participants pre-program baseline heart rate (Potter and Bolls 2012). The slowest heart rate during a commercialrepresenting the peak of attention (Lang et al. 1993)was subtracted from the participants slowest resting-baseline heart rate (Wainer 1991). Heart rate was careful via pulse photoplethysmography at two places the lobule of the ear and the distal phalanx of the non-dominant hands ring finger. The signal, ear or finger, with the fewest(prenominal) artifacts (mainly caused by movement) was retained.Sixty-four participants (30% of 211, women = 47%, age range 19-75 yrs) consented to this procedure and yielded usable heart rate data. None of these participants was on medication that affects heart rate (Andreassi 2007). give thanks to an efficient mixed-level design, the size of this sub-sample was sufficient to test the two attention hypotheses with 99. 9% power (Faul et al. 2007). Ad exposure was the number of seconds that th e commercial displayed on the examine before avoidance. Participants avoided ads by pressing the remote controls skip button, which jumped to the next ad or program segment.In this experiment skipping was impossible during the program and during the first five seconds of each commercial, to ensure that each skip decision was on the merits of the ad rather than a general goal of avoiding all commercials. A matched sample (n = 81) affirm that this procedure added a non pregnant 1. 67 seconds of ad exposure, compared to participants able to skip at any time. Although previous studies have used ad viewing time to measure ad attention (Olney, Holbrook, and Batra 1991), in this admit Please cite this article as Steven Bellman, et al. Using Internet Behavior to Deliver Relevant Television Commercials, Journal of Interactive Marketing (2013), http// dx. doi. org/10. 1016/j. intmar. 2012. 12. 001 S. Bellman et al. / Journal of Interactive Marketing xx (2013) xxxxxx 6 Table 2 Product categ ories and subcategories. Involvement Category Subcategories High automotive 1. Luxury Cars 2. Compact 4WDs 3. Sedans 4. Credit Cards 5. Financial Planning 6. retail Banking 7. Digital Televisions 8. Computers 9. Kitchen and Laundry Appliances 10. Jewellery 11. Casual Wear 12. Sportswear 13. base Insurance 14. Automotive Insurance 15. Life Insurance 16. Deodorant 7. vibrissa Care 18. Allergy Medication 19. Hamburgers 20. Mexican Food 21. Chicken 22. kinsperson Cleaners 23. Laundry Detergent 24. Cleaning Tools 25. Gardening 26. Tools 27. Pest pull strings 28. Chocolate Bars 29. Mints 30. Chewing Gum 31. Soft Drinks 32. Energy Drinks 33. chocolate 34. Frozen Meals 35. Packaged Meats 36. Desserts Financial Services Technology Fashion fit Insurance Health & Well-Being Low Fast Food abode Cleansers Home Maintenance Candy Beverages Packaged Food NOTEFor all(prenominal) subcategory, two brands were available for selection (i. e. , 72 brands). attention and ad exposure were uncorrel ated (r = ? 06, p = . 665), justifying the use of both measures. After watching the one-hour program, participants completed a second online survey on the same flat screen monitor used to watch the program. In line with the cover story for lab session 2, this survey began by measuring program liking (Coulter 1998 Cronbachs alpha = . 96). The survey went on to measure manipulation checks of ad relevance and product involvement, and managerially relevant outcomes associated with greater attention and ad exposure (see the appurtenance A). After completing this survey, participants were debriefed, hanked, and given their gift-card. products for which they were in the goal-directed search phase. This was confirmed by significant differences in self-reported purchasing horizon, careful in the stead test (Table 3). Products classified as high ad-relevance, based on Web browsing time, were more likely to be used or purchased in the next month than those classified as low ad-relevance (Ml ow ad-relevance = 3. 65 times per month vs. Mhigh ad-relevance = 6. 78). As predicted by the conceptual framework in Table 1, a significant two-party interaction between ad relevance and product involvement ualified this Internet-targeting main effect (Table 3). Using Web browsing time, ad relevance was inferred more accurately for low- rather than high-involvement products. For high-involvement products, purchase/ practice was more likely for products inferred as low ad-relevance, based on Web browsing time (Mlow ad-relevance = . 20 times per month vs. Mhigh ad-relevance = . 10). Failure to observe Web browsing did not indicate low ad-relevance for high-involvement products, and as shown in Table 1, short rather than long Web browsing time could indicate relatively greater ad relevance.Also in line with Table 1, low-involvement products had a significantly shorter purchasing horizon compared to highinvolvement products (Mlow-involvement = 10. 28 times per month vs. Mhigh-involvem ent = . 15 Table 3). Product Involvement The manipulation of product involvement was also successful, metrical by self-reported product involvement (Mlow-involvement = 4. 02 on a 7-pt scale vs. Mhigh-involvement = 4. 93, p b . 001, partial tone ? 2 = . 27), even without individual customization. No other effects were significant (e. g. , ad relevance Mlow ad-relevance = 4. 40 vs.Mhigh ad-relevance = 4. 55, p = . 213, partial ? 2 = . 007). Table 3 ANOVA results. Effect Within-participants effects Ad relevance Product involvement Purchasing horizon (monthly frequency) Attention (heart rate decrease) Ad exposure (viewing time in seconds) 10. 08** (. 05) 122. 15*** (. 37) 10. 78** (. 05) 1. 26 (. 01) .19 (. 001) 1. 40 (. 01) 3. 67 (. 06) 1. 34 (. 02) 1. 64 (. 03) 2. 17 (. 03) .27 (. 004) 4. 64* (. 07) 7. 14** (. 03) 2. 42 (. 01) 1. 90 (. 01) .38 (. 002) 2. 47 (. 01) 1. 02 (. 005) .17 (. 001) 209 .01 (b . 001) 62 .56 (. 003) 209 Independent Variable ChecksAd relevance ? product involv ement Ad relevance ? prior brand exposure Product involvement ? prior brand exposure Ad relevance ? product involvement ? prior brand exposure Between-participants effect Prior brand exposure via Web banner ads Error degrees of freedom Ad Relevance The validity of the ad relevance factor depends critically on whether participants spent more time in lab session 1 looking at NOTESF ratios (hypothesis degrees of freedom = 1). Numbers in parentheses are effect sizes (partial ? 2) small = . 01, medium = . 06, large = . 14. Significant effects in bold. p = . 06, * p b . 05, ** p b . 01, *** p b . 001. Results Please cite this article as Steven Bellman, et al. , Using Internet Behavior to Deliver Relevant Television Commercials, Journal of Interactive Marketing (2013), http// dx. doi. org/10. 1016/j. intmar. 2012. 12. 001 S. Bellman et al. / Journal of Interactive Marketing xx (2013) xxxxxx Fig. 2B shows that, in line with H1, ad relevance based on Web browsing time increased attention to commercials for low-, but not for high-involvement products. Attention was measured by heart rate decrease (HRD) the greater the ecrease, the more attention to the commercial. But H1 was only partially supported, as this effect was significant only without prior brand exposure (H1 in Table 4), as predicted by H3 (see below). The effect of ad relevance on ads for low-involvement products generated a marginally significant main effect of ad relevance on attention (Tables 3 and 4). Similarly, planned contrasts (Winer 1991) showed that in line with H2, ad relevance based on Web browsing time increased ad exposure to commercials for low-, but not for high-involvement products (Fig. A and H2 in Table 4). Ad exposure was measured by ad viewing time how much of an ad was seen before pressing the skip button. A longer ad viewing time means more ad exposure and less ad-avoidance. This effect delivered a significant effect of ad relevance even after collapsing across low- and high-involvement products (Table 3). Moderating Effects of Prior stigmatize photo Hypotheses 3 and 4 The effect of ad relevance on attention to commercials for low-involvement products predicted by H1 was suffice by the significant three-way interaction predicted by H3, among ad elevance, product involvement and prior brand exposure (Table 3). Prior brand exposure reduced the effect of ad relevance on attention to commercials for low-involvement products, most likely because prior brand exposure reduced their information-value. After prior brand exposure, viewers paid equal attention to the test commercials, no matter what their ad relevance (Fig. 2B and H3 in Table 4). Prior brand exposure also reduced the effect of ad relevance on ad exposure to commercials for low-involvement products, as predicted by H4. After prior brand exposure, ad exposure DiscussionThis study tested the effectiveness of Internet-targeted TV advertising, using recent Web browsing to identify a households relevant TV commer cials. The results suggest that this method of Internet-targeting significantly increases attention and ad exposure, even when based only on Web browsing behavior rather than search-engine keywords. These results withdraw similar field trials of addressable TV ads (Vascellaro 2011a) and single-source data (Siddarth and Chattopadhyay 1998), which have shown how ad relevance can increase TV ad exposure. However, these results also show that product nvolvement and prior brand exposure complicate Internettargeting of TV commercials. First, the boilers suit effect of Internet-targeting on ad exposure in this study was due solely to its effect on commercials for A. No Prior Brand Exposure -5 Attention (heart rate decrease bpm) Effects of Ad Relevance Hypotheses 1 and 2 was not significantly longer for high- versus low ad-relevance commercials for low-involvement products (Fig. 3B and H4 in Table 4). The results of the four hypothesis tests are summarized in Table 5. -6 -5. 84 -7 -8 -7. 88 -8. 43 -9 -9. 11 -10 Low Ad Relevance -11 High Ad Relevance -12Low High Product Involvement B. Prior Brand Exposure -5 Attention (heart rate decrease bpm) Prior Brand Exposure Prior brand exposure, via Web banner ads, increased brand recall but not significantly (Mno = 4. 3% vs. Myes = 6. 8%, p = . 132, partial ? 2 = . 011). Prior brand exposure did, however, have a significant two-way interaction with ad relevance (p = . 017, partial ? 2 = . 027). When prior brand exposure was present, brand recall was significantly higher for high versus low ad-relevance TV commercials (Mlow ad-relevance = 3. 2% vs. Mhigh ad-relevance = 9. 6%, p = . 016, partial ? 2 = . 053).When prior brand exposure was absent, brand recall was not significantly different for high versus low ad-relevance commercials (Mlow ad-relevance = 5. 4% vs. Mhigh ad-relevance = 3. 9%, p = . 441, partial ? 2 = . 006). Since ad relevance was dictated by Web browsing time, participants who recorded zero browsing times for their low ad-relevance categories had no prior brand exposure. No other effects were significant. In particular, prior brand exposure did not interact with product involvement, suggesting no differences in cognitive avoidance of Web banner ads in the first lab session for lowversus high-involvement products. -6 -7 -8 -7. 76 -8. 07 -7. 84 -8. 51 -9 -10 Low Ad Relevance -11 High Ad Relevance -12 Low High Product Involvement Fig. 2. The effects of ad relevance and product involvement on attention to TV commercials, measured by heart rate decrease, for the two prior brand exposure groups (A) no prior brand exposure, and (B) prior brand exposure via Web banner ads. Please cite this article as Steven Bellman, et al. , Using Internet Behavior to Deliver Relevant Television Commercials, Journal of Interactive Marketing (2013), http// dx. doi. org/10. 1016/j. intmar. 2012. 12. 001 S. Bellman et al. Journal of Interactive Marketing xx (2013) xxxxxx 8 Table 4 Cell means. Low ad relevance Varia ble ? 7. 55 Attention (heart rate decrease) No prior brand exposure Prior brand exposure Ad exposure (viewing time in seconds) No prior brand exposure Prior brand exposure High ad relevance Test Low product High product Low product High product involvement involvement involvement involvement H1 ? 6. 95 ? 7. 13x ? 5. 84x H3 ? 7. 96 ? 8. 07 H2 19. 99x 19. 18x ? 8. 32 ? 8. 43 ? 8. 44 ? 8. 49x ? 9. 11x ? 7. 88 ? 7. 84 ? 8. 14 ? 7. 76 ? 8. 51 20. 79 21. 23x 21. 22x 21. 25 19. 48x 18. 79x H4 8. 14 ? 8. 19 20. 16 21. 01x 21. 70x 20. 33 20. 50 19. 58 21. 42 21. 46 20. 75 22. 17 NOTESMeans in the same row with the same superscript letters differ significantly (p b . 05) using planned contrast tests (except p b . 06). which in flake increases ad liking (r = . 25, p b . 001). Although consumers have retirement concerns about targeted advertising (Spangler, Hartzel, and Gal-Or 2006), these concerns about Internet-targeted TV commercials could be alleviated if these commercials displayed the Digital Advertising Alliances Advertising Choices Icon and viewers could opt out from eceiving these commercials (youradchoices. com). For advertisers, these results support the concept of using Internet-targeting to reduce wastage in advertising budgets. Internet targeting also increases the effectiveness of TV commercials, by increasing ad exposure, which increases brand recall (r = . 14, p b . 05) and purchase intention (r = . 34, p b . 001). The results also show that Internet targeting is more critical for advertising low-involvement products, such as food, as opposed to high-involvement products like durables. Although changing the habitual nature of low-involvement onsumption is hard, commercials for low-involvement products may often suffer from bad timing. To combat this, many advertisers use continuous advertising (Ephron 1995), which is expensive and counterproductive by increasing prior brand exposure. Internet-targeting provides a way of continually monitoring household interest in low-involvement products, showing ads only when they are relevant and minimizing prior exposure. Relevance for habitual purchases, for which the A. No Prior Brand Exposure Implications Ad Exposure (ad viewing time seconds) 25 21. 70 20 0. 16 20. 33 18. 79 15 Low Ad 10 Relevance 5 High Ad Relevance 0 Low High Product Involvement B. Prior Brand Exposure Ad Exposure 30 (ad viewing time seconds) low-involvement products. But targeting-accuracy may not matter for high-involvement products, such as durables. Meta-analysis shows that advertising is more effective, on average, for durables rather than non-durables (Sethuraman, Tellis, and Briesch 2011). Consumers often gather information about high-involvement products they are not planning to purchase immediately (Moe 2003 Richins and Bloch 1986).Commercials for high-involvement products attract consistently high levels of attention and ad viewing time, as sources of information during the ongoing search process for these pro ducts. For this reason, ad-relevance can be high for high-involvement products, whether or not Web browsing behavior is observed. Second, prior brand exposure reduces the information-value of advertising (Campbell and Keller 2003). Consumers pay less attention to TV commercials, evaluate them more negatively, and are more likely to avoid them (Bellman, Schweda, and Varan 2010 Woltman-Elpers, Wedel, and Pieters 2003).In this study, prior brand exposure dampens the effects of ad relevance and product involvement. Relevant commercials for low-involvement products receive more attention and ad exposure only when prior brand exposure is not present. 30 25 20 19. 58 20. 75 21. 42 22. 17 15 Low Ad 10 Relevance 5 High Ad Relevance 0 For consumers, the results of this study suggest that Internet targeting can improve their TV viewing experience. Internet targeting increases ad relevance, which means TV commercials are worth watching rather than avoiding. In this study, greater ad relevance d ue to Internet targeting increases ad exposure, Low HighProduct Involvement Fig. 3. The effects of ad relevance and product involvement on ad exposure, measured by ad viewing time for the two prior brand exposure groups (A) no prior brand exposure, and (B) prior brand exposure via Web banner ads. Please cite this article as Steven Bellman, et al. , Using Internet Behavior to Deliver Relevant Television Commercials, Journal of Interactive Marketing (2013), http// dx. doi. org/10. 1016/j. intmar. 2012. 12. 001 S. Bellman et al. / Journal of Interactive Marketing xx (2013) xxxxxx Table 5 Results of hypothesis tests. guess Accepted? H1. Ad relevance, based on Web browsing ehavior, will increase attention to commercials for low-, but not for high-involvement products. H2. Ad relevance, based on Web browsing behavior, will increase ad exposure to commercials for low-, but not for high-involvement products. H3. Prior brand exposure reduces the effect of ad relevance on attention to commer cials for low-involvement products. H4. Prior brand exposure reduces the effect of ad relevance on ad exposure to commercials for low-involvement products. PARTIALLY (with no prior brand exposure) YES YES YES household does not search online, might be determined by knowledge of the households shopping cycle.For advertisers of high-involvement products, ad timing is less critical, and tralatitious databases derived from cable subscription data, or warranty cards, seem adequate for targeting. And advertising compose plays a role outside the consumer search process, most importantly to create awareness and interest in new purchases (Vakratsas and Ambler 1999). Conclusions Limitations withstanding, this study demonstrates how Webbased targeting can deliver the right TV commercial to the right person, and at the right time. Timeliness is particularly important for low-involvement products, as their relevance may change aily or even hourly. Timely Internet activity data can help TV adve rtisers identify commercials that currently interest a consumer. Digital-targetings potential heightens as individuals and households increasingly add devices and applications for online multi-tasking (Pilotta and Schultz 2005). This article illustrates a viable technique to tempt marketing practitioners and academics, and fuel information privacy concerns. A framework for information privacy research builds on three broad dimensions (1) quadruple publics, (2) information channel developments, and (3) public responses to privacy ctions (Peltier, Milne, and Phelps 2009). Failure to address privacy concerns is one of several limitations to this study and a promising future research avenue. Limitations and Future Research Suggestions This studys main limitation is customizing ad relevance individually rather than group-wise (Richins and Bloch 1986) in order to test the concept of Internet targeting. undivided differences provide alternative explanations and add mental disturbance t o the observed ad relevance effect (Cook and Campbell 1979). Using over 30 product subcategories helps distribute this noise evenly. The procedure in this article resembles how Fazio et al. 1986) investigated attitude accessibility. In two experiments, they individually customized a list of 16 attitude objects on the 9 priming coat of each participants reaction times in a pretest, and validated this procedure in a third experiment by obtaining identical results using manipulated stimuli. Future experiments could use a similar procedure to manipulate ad relevance (Perkins and Forehand 2012). Another limitation is using Web-browsing rather than search-engine keywords to identify ad relevance. Parameters for the former were more feasible for a controlled experiment (e. g. only 72 commercials were needed). However, searchengine queries provide a more direct and accurate means of identifying the consumers stage in the search process (Rutz and Bucklin 2011). Future studies may catch out the benefits of using search-engine queries are greater (Langheinrich et al. 1999). Internet-based targeting for high-involvement products might be improved by using search-engine queries, and more sophisticated analysis of Web browsing behavior. For example, Cai, Feng, and Breiter (2004) identify travel sites as highly relevant when a visitor views pages transportation specific as pposed to general information. Moe (2006) demonstrates how clickstream data can be used to infer both the stage of the decision process and the decision rule, which unneurotic might help identify abnormally high ad relevance for highinvolvement products. This study used ad viewing time as a measure of ad exposure. But in other studies, especially field studies, the relationship between ad viewing time and effectiveness may not be positive (cf. Tse and Lee 2001). For example, Greene (1988) observed that an ad avoider in the field has to really watch the set to see/know/ apprehend what she or he is doing nd ends up with more commercial exposure value (p. 15). Future studies should attempt to replicate these findings in field trials. Also, ad exposure may have nonlinear door effects, 1 or be affected by differences between commercials (Woltman Elpers et al. 2003). A promising future research avenue is experimentally manipulating the content of ads (e. g. , Teixera, Wedel, and Pieters 2010), as well as their ad relevance. Ideally, other psychophysiological measures of attention (Potter and Bolls 2012) could have been used but in the current setting eart rate was the least invasive. The manipulation of prior brand exposure was too weak to generate a main effect on explicit memory, but did have a significant interaction effect. The explanation is most likely that prior brand exposure was manipulated by the presence of Web banner ads and these ads tend to be processed preattentively or cognitively avoided (Chatterjee 2008 Dreze and Hussherr 2003). Future studies could manipulate prior exposure using more attention-getting stimuli, such as brand integrations in Web site editorial. If Web banners are used, implicit measures 1For example, brand recall may require a minimum ad exposure equal to 70% of an ads duration (21 s for a 30 s ad). To test for a non-linear threshold effect of ad exposure on brand recall, ad exposure was categorized into ? ve bins, 09 s, 1015 s, 1621 s, 2225 s, and 2630 s. This analysis revealed only a signi? cant linear trend (p b . 001, partial ? 2 = . 040) in the means for these bins 0%, 1. 6%, 2. 5%, 3. 9%, 10. 5%. This result may have differed, however, if the study had measured message recall. The authors thank an unknown reviewer for suggesting this analysis. Please cite this article as Steven Bellman, et al. Using Internet Behavior to Deliver Relevant Television Commercials, Journal of Interactive Marketing (2013), http// dx. doi. org/10. 1016/j. intmar. 2012. 12. 001 10 S. Bellman et al. / Journal of Interactive Marketing xx (2013) xx xxxx of banner ad effectiveness could be used as manipulation checks (Perkins and Forehand 2012). A final limitation of this study is analyze the effect of targeting ads solely by interest in a product category. Future studies could examine the effects of other personalization strategies, such as interest in specific brands, programs, creative execution styles, and offers (Verhoef et al. 010). Each of these strategies merits evaluation and comparison in order to determine effective methods of targeting addressable TV advertising. Acknowledgments The authors would like to thank the editor and the two anonymous reviewers for their constructive feedback during the review process. The authors are also grateful to Adrian Duffell, Karl Dyktinski, Emily Fielder, Michael Gell, Shannon Longville, and a team of research assistants for their considerable help in conducting the experiment reported here. This research was funded by the sponsors of the Beyond 30 project (www. beyond30. org). App endix A.Manipulation-checks and other measures In addition to the two unobtrusive measures of attention and ad exposure collected during lab session 2, which were the main dependent variables, an online survey at the end of the second lab session collected self-report measures of manipulation checks and managerially relevant outcome measures. Except for product involvement (Mittal 1995 alpha = . 97), the survey used validated single-item measures (e. g. , ad liking Bergkvist and Rossiter 2007). To restrain the slightly different question wording required for each of the 72 brands, plus selecting only the articipants four test brands to ask questions about, the survey did not use a random order of questions, but the following fixed, minimally biasing order (Rossiter and Percy 1997). Brand recall (unaided correct brand recall = 1, else = 0) was measured after program liking. purchase intention was measured next, using Justers (1966) 11-point scale for high-involvement products and J amieson and Basss (1989) 5-point scale for low-involvement products. Ad liking was next, followed by product involvement, and finally purchasing horizon purchase/usage frequency per month, measured by different 8-point scales for low- and igh-involvement products (low never to 3 or more times a twenty-four hours high do not plan to purchase to within the next month Goldberg and Gorn 1987). For every measure except purchasing horizon, dont know options helped avoid over-use of scale mid-points (Green, Goldman, and Salovey 1993). Missing data were replaced by the subjects mean, a conservative strategy (Blumenthal et al. 2005). 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