Planning Media Schedules in the Presence of Dynamic Advertising Quality

A key task of advertising media planners is to determine the best media schedule of advertising exposures for a certainbudget. Conceptually, the planner could choose to do continuous advertising (i.e., schedule ad exposures evenly overall weeks) or follow a strategy of pulsing (i.e., advertise in some weeks of the year and not at other times). Previous theoretical analyses have shown that continuous advertising is optimal for nearly all situations. However, pulsing schedules are very common in practice. Either the pract ice of pulsing is inappropriate or extant models have not adequately conceptualized the effects of advertising spending over time. This paper offers a model that shows pulsing strategies can generate greater total awareness than the continuous advertising when the effectiveness of advertisement (i.e., adquality) varies overtime. Specifically, ad quality declines because of advertising wearout during periods of continuous advertising and it restores, due to forgetting effects, during periods of no advertising. Such dynamics make it worth-while for advertisers to stop advertising when ad quality becomes very low and wait for ad quality to restore beforestarting the next "burst" again, as is common in practice. Based on the extensive behavioral research on advertising repetition and advertising wearout, we extend the classical Nerlove and Arrow (1962) model by incorporating the notions of repetition wearout, copy wearout, and ad quality restoration. Repetition wearout is a result of excessive frequency beca use ad viewers perceive that there is nothing new to be gained from processing the ad, they withdraw their attention, or they become unmotivated to react to advertising information. Copy wearout refers to the decline inad quality due to passage of time independent of the level of frequency. Ad quality restoration is the enhancement of ad quality during media hiatus as a consequence of viewers forgetting the details of the advertised messages, thus making ads appear "like new" when reintroduced later. The proposed model has the property that, when wearout effects are present, a strategy of pulsing is superior to continuous advertising even when the advertising response function is concave. This is illustrated by a numerical example that compares the total awareness generated by a single concentrated pulse of varying duration (blitz schedules) and continuous advertising (the even schedule). This property can be explained by the tension between the pressure to spend the fixed media budget quickly to avoid copy wearout and the opposing pressure to spread out the media spending over time to mitigate repetition wearout. The proposed model is empirically tested by using brand level data from two advertising awareness tracking studies that also include the actual spen ding schedules. The first data set is for a major cereal brand, while the other is for a brand of milk chocolate. Such advertising tracking studies are now a common and popular means for evaluating advertising effectiveness in many markets (e.g., Millward Brown, NarketMind). In the empirical tests, the model parameters are estimated by using the Kalman filter procedure, which is eminently suited for dynamic models because it attends to the intertemporal dependencies in awareness build-up and decay via the use of conditional densities. The estimated parameters are statistically significant, have the expected signs, and are meaningful from both theoretical and managerial viewpoints. The proposed model fits both the data sets rather well and better than several well-known advertising models, namely, the Vidale-Wolfe, Brandaid, Litmus, and Trackermodels, but not decisively better than the Nerlove-Arrow model. However, unlike the Nerlove-Arrow model, the proposed model yields different total awareness for different strategies of spending the same fixed budget, thus allowing media planners to discriminate among several media schedules. Given the empirical support for the model, the paper presents an implementable approach for utilizing it to evaluate large numbers of alternative media schedules and determine the best set of media schedules for consideration in media planning. This approach is based on an algorithm that combines a genetic algorithm with the Kalman filter procedure. The paper presents the results of applying this approach in the case studies of the cereal and milk chocolate brands. The form of the best advertising spending strategies in each case was a pulsing strategy, and there were many schedules that were an improvement over the media schedule actually used in each campaign.

[1]  J. Simon.  The shape of the advertising response function , 1980 .

[2]  T. Oum,et al.  Advertising Quality in Sales Response Models , 1987 .

[3]  Maurice W. Sasieni,et al.  Optimal Advertising Strategies , 1989 .

[4]  J. Rossiter,et al.  Advertising communications & promotion management , 1997 .

[5]  M. Blair,et al.  An Empirical Investigation of Advertising Wearin and Wearout , 2000, Journal of Advertising Research.

[6]  Margaret Hender Blair,et al.  Convergent findings increase our understanding of how advertising works , 1994 .

[7]  Michael A. West,et al.  Bayesian Forecasting and Dynamic Models (2nd edn) , 1997, J. Oper. Res. Soc..

[8]  E. Phelps.  Microeconomic Foundations of Employment and Inflation Theory , 1970 .

[9]  Hermann Simon,et al.  ADPULS: An Advertising Model with Wearout and Pulsation , 1982 .

[10]  Edward C. Strong.  The Use of Field Experimental Observations in Estimating Advertising Recall , 1974 .

[11]  Banwari Mittal.  Public assessment of TV advertising: faint praise and harsh criticism , 1994 .

[12]  Leonard M. Lodish,et al.  A Media Planning Calculus , 1969, Oper. Res..

[13]  Maurice W. Sasieni,et al.  Optimal Advertising Expenditure , 1971 .

[14]  Ram C. Rao,et al.  Estimating Continuous Time Advertising-Sales Models , 1986 .

[15]  Randall L. Schultz,et al.  Marketing Models and Econometric Research , 1976 .

[16]  Robert C. Blattberg,et al.  Tracker: An Early Test Market Forecasting and Diagnostic Model for New Product Planning , 1978 .

[17]  J. Farley,et al.  How Advertising Affects Sales: Meta-Analysis of Econometric Results , 1984 .

[18]  G. David Hughes.  Realtime response measures redefine advertising wearout. , 1992 .

[19]  Suresh P. Sethi,et al.  Advertising Budgeting, Wearout and Copy Replacement , 1978 .

[20]  John Philip Jones,et al.  When Ads Work: New Proof That Advertising Triggers Sales , 1995 .

[21]  Hani I. Mesak,et al.  An Aggregate Advertising Pulsing Model with Wearout Effects , 1992 .

[22]  J. M. Villas-Boas.  Predicting Advertising Pulsing Policies in an Oligopoly: A Model and Empirical Test , 1993 .

[23]  Varghese S. Jacob,et al.  Genetic Algorithms for Product Design , 1996 .

[24]  Clark Leavitt,et al.  Advertising Wearout: An Experimental Analysis , 1976 .

[25]  Richard E. Petty,et al.  Repetition, Cognitive Responses and Persuasion , 1981 .

[26]  D. Berlyne.  Novelty, complexity, and hedonic value , 1970 .

[27]  A. Greenberg,et al.  Television commercial wearout. , 1973 .

[28]  Roger J. Calantone,et al.  The Stability of Benefit Segments , 1978 .

[29]  Andrew Harvey,et al.  Forecasting, Structural Time Series Models and the Kalman Filter , 1990 .

[30]  Robert C. Blattberg,et al.  A Micromodeling Approach To Investigate The Advertising-Sales Relationship , 1981 .

[31]  Vijay Mahajan,et al.  An Empirical Comparison of Awareness Forecasting Models of New Product Introduction , 1984 .

[32]  G. Owen.  Multilinear Extensions of Games , 1972 .

[33]  Georges R. Harik,et al.  Foundations of Genetic Algorithms , 1997 .

[34]  Andrew Harvey,et al.  Forecasting, Structural Time Series Models and the Kalman Filter. , 1991 .

[35]  Fred M. Feinberg,et al.  Pulsing Policies for Aggregate Advertising Models , 1992 .

[36]  M. L. Vidale,et al.  An Operations-Research Study of Sales Response to Advertising , 1957 .

[37]  W. M. Weilbacher.  WHAT HAPPENS TO ADVERTISEMENTS WHEN THEY GROW UP , 1970 .

[38]  John D. C. Little,et al.  Feature Article - Aggregate Advertising Models: The State of the Art , 1979, Oper. Res..

[39]  R. Hartl,et al.  Dynamic Optimal Control Models in Advertising: Recent Developments , 1994 .

[40]  J. P. Gould,et al.  Diffusion Processes and Optimal Advertising Policy , 1976 .

[41]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[42]  Jonathan D. Cryer,et al.  Time Series Analysis , 1986 .

[43]  Minhi Hahn,et al.  Pulsing in a Discrete Model of Advertising Competition , 1991 .

[44]  Richard F. Hartl,et al.  A simple proof of the monotonicity of the state trajectories in autonomous control problems , 1987 .

[45]  A. Mcglashan.  Remembering and forgetting. , 1955, Lancet.

[46]  Richard F. Hartl,et al.  Adpuls in continuous time , 1988 .

[47]  Michael J. Naples.  Effective frequency : the relationship between frequency and advertising effectiveness , 1979 .

[48]  Vijay Mahajan,et al.  Advertising Pulsing Policies for Generating Awareness for New Products , 1986 .

[49]  John D. C. Little,et al.  BRANDAID: A Marketing-Mix Model, Part 1: Structure , 1975, Oper. Res..

[50]  Leonard M. Lodish,et al.  How T.V. Advertising Works: A Meta-Analysis of 389 Real World Split Cable T.V. Advertising Experiments , 1995 .

[51]  J. Cacioppo,et al.  Effects of message repetition and position on cognitive response, recall, and persuasion. , 1979 .

[52]  G. Belch,et al.  The Effects of Television Commercial Repetition on Cognitive Response and Message Acceptance , 1982 .

[53]  Robert Fildes,et al.  Marketing Models and Econometric Research , 1977 .

[54]  Hubert A. Zielske.  The Remembering and Forgetting of Advertising , 1959 .

[55]  K. Arrow,et al.  OPTIMAL ADVERTISING POLICY UNDER DYNAMIC CONDITIONS , 1962 .

[56]  Chris Beaumont.  Marketing Planning Models , 1982 .

[57]  Max Sutherland,et al.  Advertising and the Mind of the Consumer: What works, what doesn't and why , 2020 .

[58]  Julian Lincoln Simon,et al.  What do Zielske's Real Data Really Show about Pulsing? , 1978 .

[59]  Michael L. Ray,et al.  Advertising and Communication Management , 1981 .

[60]  Alan G. Sawyer,et al.  Behavioral Measurement for Marketing Models: Estimating the Effects of Advertising Repetition for Media Planning , 1971 .

[61]  B. Calder,et al.  Television Commercial Wearout: An Information Processing View , 1980 .