The Total Long-Term Sales Effects of Advertising: Lessons from Single Source

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1 The Total Long-Term Sales Effects of Advertising: Lessons from Single Source KATE NEWSTEAD Kate.Newstead I ivlarketingscience.info JENNIFER TAYLOR Jenhifer.Tayior Marl<etlngScience.info This article brings together the knowiedge gained from two different approaches to analyzing singie source data: aggregate-ievei experimental spiit-cable tests and individual-level analysis without experimental controls. From very different approaches, two common findings emerge: If advertising is to be sales effective in the long term, it must first work in the short term. Advertising typically has a half-life of three to four weeks. In terms of scheduling, a continuity strategy appears preferable. There may be conditions under which bursting is more appropriate, but these circumstances are not RACHEL KENNEDY Rachei.Kennedy i\^arketingscience.info j BYRON SHARP! Byron.Sharp Mari<etingScience.info yet at all well documented. INTRODUCTION If advertising drives sales and more than 40 years of single source behavior measurement clearly shows that most advertising does (see Wood, this issue, p. 186) it does so by nudging the propensity to buy the brand of the individuals who saw the commercial. This nudge is the behavioral effect of advertising and should be the metric upon which it is judged. This change in buying propensity, however, can be difficult to detect, let alone measure correctly in aggregate (e.g., weekly sales figures). The effect of advertising can be subtle: after all, in a single TV spot, the cost-per-viewer is only a few cents, and other marketing activities can swamp the effect. The effect of advertising on aggregate sales depends not just on the power of the advertisement, but also the effectiveness of the media placement (how many consumers did it reach?). Finally, and very importantly, advertising's effect on sales figures is spread out over time; only a fraction of the individuals exposed to a commercial actually buy from the category in the following week. Single-source data are so powerful because they overcome many of the aforementioned problems. Single-source data track the same individuals or households over time for both their purchases and opportunity to see advertising. They allow for the immediate nudge in propensity to be measured just among those consumers who actually were exposed to a single commercial (allowing media and competitive advertising effects to be filtered out). To date, two very different approaches have been taken to analyzing such data: aggregate-level analysis from experimental splitcable test marketing environments; individual-level analysis without experimental controls. Both approaches produce some valuable empirical generalizations (EGs) about the total long-term effects of advertising. BACKGROUND TO THE TWO APPROACHES Experimental split-cable analysis Split-cable field environments enable researchers to control advertising treatments delivered to groups of consumers and to observe their subsequent brand purchase behaviors. Under such an DOi : I Í0.2501/S June JOURIIIIL OF HDUERTISIflG RESEHRCH 2 0 7

2 TOTAL LONG-TERM SALES EFFEOTS OF ADVERTISING EMPIRICAL GENERALIZATIONS The total long-term sales effect of advertising depends (positively) on the size of the initial effect and (negatively) on competitive advertising. An advertising exposure typically has a half-life of three to four weeks. Several pure single-source panel data sets have been collected over the last 40 years. Colin McDonald pioneered the use of singlesource data in the United Kingdom with his 1960s analysis of purchasing sequences. Within the United Kingdom, only a handful of panels have been established (including Adlab in the 1980s and TVSpan in the early 2000s). In the United States, IRI made the first attempt at single-source data collection in the 1980s. This was followed by panels by Arbitron and Nielsen with their most recent joint venture. Project Apollo, which was disbanded in New approaches are now in trial. experimental approach, respondents shop normally, allowing for observation of advertising effects on brand purchase in a real-world environment. To observe the effect of advertising alone on brand purchase, other variables (including pricing and competitor advertising) are either held constant across groups or controlled in the analysis. Information Resources, Inc.'s (IRI's) BehaviorScan provided a large-scale testmarketing environment that recorded individual households' brand purchases and advertising viewing behavior. Advertisers commissioned advertising tests in the BehaviorScan environments (i.e., in a "town" in the United States, France, or Germany). Within the test environments, researchers controlled variables that might impact purchase results. Groups of participants were matched based on demographic and prior category as well as brand-purchase behavior; variables such as in-store materials and pricing were controlled for during the analysis stage. Only the advertising treatment was manipulated between test and control groups. The resulting penetrations and market shares for each brand were compared between control and test groups at points throughout the test period and at the conclusion of each test. While each BehaviorScan test was unique to the sponsor's requirements, a variety of research has drawn together and summarized tests with similar objectives. A 1995 "How TV Advertising Works" study in the Journal of Marketing Research born out of clients' requests for generalizations that rephcated across time, brands. and test conditions ^brought together findings from 389 advertising experiments (Lodish et al, 1995). European BehaviorScan researchers also published findings that replicated across tests (Fulgoni, 1986; Litzenroth, 1991; Merzereau and Battais, 2000; Stahl, 1999). Individual-level analysis Individual (or household-level) analysis of single-source data is different from the experimental split-cable designs in its ability to show the effect of brand advertising on the same individuals' purchases over time. Unlike split-cable experiments based on aggregate brand-level measures, nonexperimental single-source data can measure the increased (or decreased) likelihood of a particular person (or household) purchasing a brand after advertising exposure. Importantly, advertising's defensive role in nudging sales among exposed individuals can be measured even when there is no aggregate change in sales for the brand. Because brand purchase and advertising exposure are matched at the individual level rather than through demographics, the influences of confounding variables are largely controlled. GENERALIZATIONS IN SHORT- AND LONG TERM ADVERTISING EFFECTS We now discuss the findings from aggregate- and individual-level analysis of single-source data concerning the total long-term effects of advertising. Both types of single-source analysis support the view that the total sales effect of advertising very much depends on the initial effect (as seen in single-source, not aggregate sales figures). Based on their individual level findings, John Philip Jones (1995) and Colin McDonald (2003) suggest that short-term advertising effects are a precondition for long-term effects that advertising does not work "like a time bomb." McDonald argues, "Each advertisement, if it works at all, stimulates its immediate response: the continuum, however far into the future, is merely the continued repetition of such responses" (McDonald, 1992). The extent to which the nudging of individuals' purchase propensities plays out into total sales gain will depend on the media strategy and spend (how many consumers were exposed), and competitor marketing activity. These factors are independent of the advertising (its creativity, its branding, and how it is presented to consumers) so should not be used to judge it. Similarly, the total long-term sales effect of an advertising exposure will depend on the circumstances that occur weeks/months from the date the commercial aired. Advertising that produces only a weak behavioral effect still can have a moderate total long-term effect if circumstances work in its favor and if competitors do little or ineffectual advertising. The total long-term effect. 208 JOURIIHL or HDUERTISIOG RESEHRCH June 200g

3 TOTAL LONG-TERM SALES EFFECTS OF ADVERTISING however, still very much depends on the power of the advertising as shown in the initial individual-level measurement of the behavioral nudge. This is the true measure of advertising's sales power. The BehaviorScan experimental findings in relation to short- and long-term advertising effects support this contention. They are based on 44 split cable tests in which one of the tested approaches had a "successful" sales impact the brand sales for the test group statistically were higher than the control group (at the 80 percent confidence level on a one-tailed test) during the first year of the advertising test. All tests were reanalyzed at years two' and three to check for long-term advertising effects. On average, there was a carry-over effect' from the first year's sales increase into the second year, which, in turn, carried over into the third year, but at diminishing rates. In these tests, sales increases in the initial year, second year, and third year were due to a higher impact on sales volume than market share. In the 13 tests where there were no successful sales increases in the first year of testing, reanalysis at the second and third year revealed no carry-over effects (Lodish et al., 1995). Thus, the effects of advertising can last in the long term (over a year), but must be preceded by short-term effects. I Decay effects The nudge in purchase propensity that is produced by an advertising exposure fades as competitive effects (e.g., competitor advertising) interfere with memory. Quantifying these decay effects is useful for knanaging a "continuous advertising presence," that is, for determining how big the gaps should be between exposures. In an examination of decay effects, Behavior- Scan and individual-level analysis results converge. Roberts' (1999) individual-level analysis found that the probability of The nudge in purchase propensity that is produced by an advertising exposure fades as competitive effects (e.g., competitor advertising) interfere with memory. brand purchase increased when an advertisement was seen during the day immediately prior to purchase and that an advertisement's rate of decay was approximately 4 percent per day (Roberts, 1999). An analysis of purchases and corresponding brand advertising "opportunity to see" in the 28 days before purchase found that three-quarters of the full initial effect of advertising had been achieved within a month (Roberts, 1999). Similarly, BehaviorScan studies reported, on average, half-lives for packaged goods of four weeks (Lodish et al, 1995). GENERALIZATIONS IN CONTINUITY VERSUS BURSTING SCHEDULING While there is no widespread agreement on the choice between continuity and bursting schedules, we find that BehaviorScan experimental results and the findings from most individual-level analyses are broadly consistent. Overall, both approaches present a strong case for the preference of continuity. Fifteen tests in the French Behavior- Scan environment compared spread-out constant media strategies with heavy, periodic bursting strategies (Merzereau and Battais, 2000). In two-thirds of the test cases, both strategies produced the same final market shares, penetrations, and purchase intensities. These effects, however, cumulated differently, as the bursting strategy built sales more quickly. The authors suggested that, when continuity and bursting schedules produce the same results, continuity is often preferable for existing brands since, over time, it leads to more regular purchases, has better coverage of consumers' random category purchase cycles, and builds light buyers' purchase rates gradually. This sentiment was previewed by Jones who, in 1995, argued for advertising in close proximity to each buyer's time of purchase and reinforced by Erwin Ephron in 2001 when he observed that, because buying is spread out, advertising should be too. One-third of the BehaviorScan test case results, however, showed that a bursting strategy was better than a continuity strategy (Merzereau and Battais, 2000). We note that in these cases the media plans tended to have large blocks of "on" and "off" time (in some cases up to six months of continuous "on" time). That same year, Merzereau and Battais (2000) summarized that bursting tended to be more suited to new brands or impulse products. Broadbent's econometric modeling of fast-moving consumer goods brands supported a continuous advertising schedule in many cases. However, he cautions that there is no evidence of universal applicability (product launches are again a noted exception) (Broadbent, 1999). In 1991, Litzenroth, a BehaviorScan researcher, found that a heavy-up initial advertising burst (equivalent to 300 percent of a normal media spend) followed by a "normalized" continuity strategy outperformed a constant continuity strategy. In this scenario, the heavy-up strategy produced high sales during and immediately after the initial burst period and an additional 17 percent sales increase by the end of the 72-week test period. June 2009 JOURIIIIL DF HDUERTISIIIG RESEHRCII 209

4 TOTAL LONG-TERM SALES EFFECTS OF ADVERTISING Given the extra expense associated with a 300 percent initial media budget, however, such a media strategy may still result in a negative return on investment. Continuity schedules are supported. BehaviorScan also provides evidence in support of bursts or pulses in some circumstances. CONCLUSiON The findings of aggregate level analysis from experimental marketing environments largely converge with findings from individual-level analyses. Our review of key studies in this area illustrates the common findings that: advertisements work immediately and the strength of this effect very much influences the total sales effect; advertising exposure typically has a halflife of three to four weeks. This review also suggests that continuity schedule is generally the most appropriate advertising strategy, (f^ KATE NEWSTEAD is a research associate at the at the Australia. Her key research areas are advertising effectiveness and media strategy. Her recent work at the Institute includes exploring the relative effects of advertisement length on advertisement effectiveness and investigating the relationship between engagement with media content and advertising avoidance behavior. JENNIFER TAYLOR is a research associate at the for Marketing Science, University of South Australia. Her area of research includes media planning and advertising. She is currently completing a Ph.D. examining the advertising response function and its measurement. RACHEL KENNEDY, associate professor at the for Marketing Science, University of South Australia, is head of the Advertising Research Group. She is involved in commercial market research to understand buyer or social behavior and marketing interventions like advertising, as well as academic research into advertising and the dynamics of market structure. BYRON SHARP is a professor and director of the at the Australia. The Institute's fundamental research is used and financially supported by many of the world's leading corporations including Coca-Cola, Kraft, Keiiogg's, British Airways, Procter & Gamble, Nielsen, TNS, Turner Broadcasting, Network Ten, Simpiot, Mars, and many others. Dr. Sharp has published over 100 academic papers and is on the editorial board of five journals. REFERENCES BROADBENT, S. When to Advertise. Henley-on- Thames, U.K.: Admap Publications, EPHRON, E. "Teaching Tap to the Elephant. Media Planners Have Fewer Scheduling Options Than They Think." Advertising Research Foundation Workshop, October FULGONI, G. M. "Advertising Weight Testing: The BehaviorScan Experience." Admap, March JONES, J. P. Wlien Ads Work New Proof That Advertising Triggers Sales. New York: Lexington Books, LITZENROTH, H. "A Small Town in Germany: Single-Source Data from a Controlled Micromarket." Admap, May LODISH, L. M., M. ABRAHAM, S. KALMENSON, J. LiVELSBERGER, B. LUBETKIN, B. RiCHARDSON, and M. E. STEVENS. "HOW T.V. Advertising Works: A Meta-Analysis of 389 Real World Split Cable T.V. Advertising Experiments." Journal of Marketing Research 32, 3 (1995): MCDONALD, C. HOW Advertising Works: A Review of Current Thinking. Henley-on-Thames, U.K.: Advertising Association/NTC Publications Ltd., is Your Advertising Working: A Guide to Evaluating Campaign Effectiveness. Henley-on- Thames, U.K.: World Advertising Research Centre, MERZEREAU, P., and L. BATTAIS. "Continuity or Burst? How Do TV Strategies Compare?" Admap, October ROBERTS, A. "Recency, Frequency and the Sales Effects of TV Advertising." Admap, February STAHL, C. "Added Value of the Media Mix. Experiences and Lessons from an Experimental Test Marker." Strategic Publishing: Looking to the Future. Amsterdam: European Society for Opinion and Marketing Research, Milan, JOURnm OF HDUERTISIOG RESEHIICH June 200g

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