The frequency and timing of advertising message exposures plays an important role in advertising campaign management, specifically media planning. However, when planning an advertising campaign, this is not as important as addressing questions about the advertising goal (what is to be achieved), the target groups (who is to be reached), and the budget (amount). The question at the heart of the overall strategy is how many exposures are ideally needed to reach a target person and within what time frame.
A number of restrictions must be faced when resolving this question: the budget is limited, the time frame of the advertising campaign is often determined by exterior circumstances, the chosen media are not endlessly available, etc. Determination of the exposure frequency and timing is also closely linked to other elements of the media schedule. Generally though, this is a multilayered, interactive, and, in the ideal case, integrated process. In practice, the decisions of media planners are strongly influenced by their own experiences and the standard work processes in their companies. However, there are a whole range of supportive tools and theories.
Advertising Exposure and Coverage in Media Planning
The number of times a target person is exposed to an advertisement is by no means the only control parameter used in media planning. One of the most important planning parameters is the total gross number of exposures, also known as gross rating points (GRPs), i.e., the total number of exposures achieved with one advertising campaign, usually expressed as a percentage of the target group but written without the percentage sign. If, for example, a target group of persons aged between 20 and 40 years is reached with 200 GRPs, then this means that each person has an average of two exposures to the ad. However, this provides no information about how many exposures reach each individual. Even people with zero exposures are included in this calculation. In media planning practice, only the average number of exposures, based on the total number of persons reached, is used. This opportunity-to-see (OTS) parameter is obtained by dividing the total number of exposures by the net coverage or the total number of persons who were reached at least once. OTS is therefore the average number of times the target persons are reached by an advertising campaign.
A more detailed breakdown provides information about the exposure distribution, i.e., the exact number of persons reached by what dosage of exposures. This exposure distribution can form the basis for defining “effective coverage” if one assumes, for instance, that a certain number of exposures is necessary in order to achieve a specific advertising goal. Only a certain number or dosage of exposures, for instance three or more exposures, is then taken into account in the effective coverage. All of these parameters can then be used as a control parameter for media planning, when addressing the issue of which advertising agent in what genre of media is selected and occupied – in other words, which TV station, and which programs and advertising blocks are chosen for broadcasting the spots; in which magazines ads are placed, etc. Large institutionalized market media studies or electronic measuring devices such as “people-meters” form the basis of the media plans, which also supply the basic data on coverage and exposure build-up. The measures and processed data are then incorporated into media planning programs. The probable use and probability distribution, frequently the binominal model, are often used as a reference. Here, the intermedial or cross-media genre planning poses an ongoing challenge, in which the exposure levels of various media must be compared and evaluated. The type and quality of the planning data prepared for various advertising media also vary considerably, and very heavily influence the planning process.
How Many Exposures Are Necessary?
With regard to the planning requirements for frequencies of exposure and timing, it is difficult to define general rules. Time and again, research projects have confirmed the tremendous individual-level effects of advertising. A lot of studies from the 1960s and 1970s emphasized the amount of time and number of exposures. “Needless to say, the results of this research have been inconclusive and sometimes contradictory,” state Wicken and Solomon (1998). Nevertheless, media agencies have rules of thumb, e.g., for the desired advertising pressure in an advertising medium during a campaign in the shape of GRPs or OTS per week or month. These rules of thumb have most certainly arisen from everyday experience, from “heuristic” insights, in the best possible sense. However, they do not remain constant, either in terms of the time factor or by international comparison. After all, media environments are subject to fundamental and ever faster processes of transformation that are characterized by different cultural and technical starting conditions.
Thus, suggestions for achieving advertising pressure in specific cases are generally stated quite broadly. In their annotated bibliography of condensed research on advertising effects over a quarter of a century, Schönbach et al. (2002) write that the optimal frequency of exposure to an advertisement is somewhere between 2 and 20. However, the majority of research summaries that make a statement are probably closer to three or four exposures. A large number of individual studies also suggest that three or more exposures is a good control parameter for “effective coverage.” On the other hand, one can assume that if an individual is subjected to a large number of exposures, it will have a wear-out effect on him or her (Wicken & Solomon 1982). Too much of the same advertising leads to saturation, boredom, no longer noticing an ad, or even – in rare cases, presumably – to reactance. As a rule, too much exposure to the same ad will no longer have an effect and, in financial terms, will simply lead to overspending. Good advertising campaigns try to avoid such wear-out effects by clearly changing the motif, mostly within the context of a general theme and a solid degree of recognition.
Effective Frequency Versus Recency Planning
The theory that a consumer requires several exposures for an ad to have a certain effect ultimately draws on scholarly thinking and the assumption that there exists an effect or response curve. One basically assumes that every exposure produces a certain effect. This usually leads to a diminishing rise in the saturation function, which corresponds to the law of declining marginal utility.
In addition to the theory of effective frequency, another partly competing theory called “recency planning,” conceived by Erwin Ephron (1997), was first introduced in the mid- 1990s. The theory of recency planning is essentially concerned with the sales promotion function of advertising, and it has played a major role in media planning and research in the United States, in particular. It is based on the assumption that advertising is most effective when a consumer wants to buy or replace a certain product. Hence, from this perspective, the receiver determines the effectiveness of the exposures. Advertising messages are most effective shortly before a potential buyer makes his or her purchase decision. The first exposure is the most effective. In recency planning it is, therefore, not important whether a certain frequency of exposure is achieved, but rather whether the consumer is driven in the right direction prior to an act of purchasing. In the case of doubt, one exposure is sufficient. And since one is generally unaware when the purchasing act will take place, the recency model advocates continuous advertising and an advertising plan over short periods of time. With its recommendation for week-based advertising plans, the recency planning model harmonizes with measuring instruments such as John Philip Jones’s short-term advertising strength formula (STAS), which, to put it succinctly, measures the success of advertising according to the weekly sales following the advertising exposure (Jones 1995; Ephron 1997; McDonald 2004).
The argument behind this approach is not so much the declining efficiency of multiple exposures with a number of people as the higher efficiency of exposures with people who would not have been reached otherwise. An advertising plan based on this view would not be concerned with trying to achieve a certain effective frequency within a given time frame, but would instead be focused on trying to reach as many people as possible within that period. In other words, the recency model demands as high a coverage as possible. It is driven by very strong, short-term, sales-oriented research and an “instrument single source” household panel for measuring effectiveness.
Yet the discussion is not limited to a debate between effective frequency and recency planning. According to Colin McDonald (2004), it merely adopts a “new level of complexity.” Instead of merely applying rules of thumb, the planner’s or media researcher’s task is to optimize advertising measures in individual cases. Needless to say, certain rules apply that are not only aimed at directly conveying impetuses to buy, but also, in the classical sense, to conveying a message, as with “instruction.” This message might be concerned with the creation of an image and brand management or establishing purchase preferences. Here, product launches, repositioning, or simply “new ideas” constitute special cases. Results from panel research also confirm that, e.g., when buying a car, new exposures that build on previous exposures over a longer period of time are considerably more effective than exposures that have no antecedents (Buhr & Hallemann 2005). At the same time, many studies have demonstrated that purchase decisions and product uses are strongly influenced by attitudes, brand preferences, images, etc. However, from a purely short-term perspective, the influence of advertising on these long-term tendencies that flow into the brand depot can hardly be measured.
Timing and Monitoring Advertising Success
To measure the effect of their advertising campaigns, many advertisers and agencies perform ongoing advertising tracking, in which indicators such as ad awareness, a brand’s degree of popularity, images, willingness to buy, etc. are asked about. Advertising recall or recognition tests are conducted to measure how well an ad is remembered. The trend of the measurement data allows one to draw conclusions about the effectiveness of advertising campaigns, the need for a change of motif, or the level of advertising pressure needed.
Modeling aimed at trying to establish a statistical link between advertising effectiveness indicators and the budget is also frequently carried out. Ideally, sales modeling will show how much advertising pressure (in terms of GRPs) is necessary, how long-term its effect is (“ad stock”), and in which periods one should advertise in order to support sales.
Modeling also offers clues about the timing of advertising. There are no general rules concerning this. Recency planning clearly recommends continuous advertising over very short planning periods. The only exception would be purely seasonal products, for which there are no buyers on the market during certain periods, e.g., Christmas trees. Yet so-called “flighting” is very common. This is when there are several clearly defined advertising phases per year, with intervals in between. Flightings are often used when budgetary constraints make it impossible to conduct continuous advertising. Special cases include “pulsing,” the fast interchange between high and low advertising pressure and intervals; “waving,” with rising and falling advertising pressure; as well as the “burst,” a short, massive advertising campaign, used, for instance, for launching a product. “Frontloading” is often used with product launches, i.e., the campaign starts with high advertising pressure, which then steadily declines. But the opposite, “backloading,” which is the steady increase of advertising pressure in order to make an advertising campaign more dramatic, is also possible (Hofsäss & Engel 2003).
Great importance is often assigned to observing competitors’ advertisements. If it is not possible to keep pace with the main competitor and to achieve a high share of advertising (share of spending) or share of voice (share of exposures), we recommend a niche strategy of periodic evasions, with little or no active competition. Based on this view of achieving a high share of advertising in order not to be completely engulfed by the competitor, the “flighting” strategy of applying short, intense advertising pressure is a logical choice. Yet this theory is contentious and has in any case not been empirically substantiated.
There is certainly no ideal method of planning advertising pressure and timing. Both are closely interlinked and interdependent. The experience of practitioners in media agencies and of major advertisers plays a large role, supported by the continuous observation of campaigns with tracking, panel research, and statistical modeling as a control instrument of increasing importance.
- Broadbent, S. (1999). When to advertise. Henley on Thames: Admap.
- Buhr, J. O., & Hallemann, M. (2005). Experience with ad hoc panels to measure advertising effectiveness. In International Readership Symposium, session papers, pp. 185 –195.
- Ephron, E. (1997). Recency planning. Admap, pp. 32 –34 (February).
- Hofsäss, M., & Engel, D. (2003). Praxishandbuch Mediaplanung. Berlin: Cornelsen.
- Jones, J. P. (1995). When ads work: New proof that advertising triggers sales. Lanham, MD:
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- McDonald, C. (1995). Advertising reach and frequency: Maximizing advertising results through effective frequency. Lincolnwood, IL: NTC.
- McDonald, C. (2004). Advertising reach and frequency. Admap, 452, 13 –14.
- Schönbach, K., Henzgen, U., Müller, T., Rector, T., & Scholz, I. (2002). Werbewirkung: Eine Inventur der Inventare. Übersichten zu Effekten von Anzeigen: Eine annotierte Bibliografie. Hamburg: ICW.
- Wicken, G., & Solomon, D. (1998). What is wearout anyway? Journal of Advertising Research, 38(5), 19 –28.