- Email: [email protected]
To be convincing or to be right: A question of preciseness
585
Research on forecasting
Finally, their meta-analysis results were used to develop initial estimates, “priors”, in a Bayesian estimation procedure, to forecast the diffusion of room air conditioners as additional diffusion data becomes available. Parameter estimates are updated by using a weighted average of the prior estimates and the values developed from the actual data (by using Srinivasan and Mason’s NLS estimation procedure), with weights equal to the inverses of their variances. The Bayesian scheme was found to produce more robust estimates. _ Paul Bottomley
References Bass, F.M., 1969, “A new product growth model for consumer durable?, Management Science, 15, 215-227. Coleman, J.S., E. Katz and H. Menzel, 1966, Medical Znnouations: A Diffusion Study (Bobbs-Merrill, Indianapolis). Fourt, L.A. and J.W. Woodlock, 1960, “Early prediction of market success of grocery products”, Journal of Marketing, 25, 31-38. Gatignon, H., J. Eliashberg and T.S. Robertson, 1989, “Modelling multinational diffusion patterns: An efficient methodology, Marketing Science, 8, 231-247. Holak, S.L. and D.R. Lehmann, 1990, “Purchase intention and the dimensions of innovation: An exploratory model”, Journal of Product Innovation Management, 7, 59-73. Kalish, S., 1985, “A new product adoption model with pricing, advertising and uncertainty”, Management Science, 31, 1569-1585. Kalish, S. and S.K. Sen, 1986, “Diffusion models and the marketing mix for single products”, in: V. Mahajan and Y. Wind (eds.), Innovation Diffusion Models of New Product Acceptance (Ballinger, Cambridge, MA) 87-116. Mahajan, V., E. Muller and F.M. Bass, 1990, “Dynamics of innovation diffusion: New product growth models in marketing”, Journal of Marketing, l-26. Simon, H. and K.H. Sebastian, 1987, “Diffusion and advertising: The German telephone company”, Management Science, 33, 451-466. Srivastava, R.K., V. Mahajan, S.N. Ramaswami and J. Cherian, 1985, “A multiattribute diffusion model for forecasting the adoption of investment alternatives for consumers”, Technological Forecasting and Social Change, 28, 325-333.
[Dr. Fareena Sultan, Harvard Business Soldiers Field, Boston, MA 02163, USA]
School,
Karl Halvor Teigen, “To be convincing or to be right: A question of preciseness”, in: K.J. Gilhooly,
M.T.G. Lines
Keane, of
Thinking
R.H. Logie and G. Erdiis (eds.), (Wiley, Chichester, 1990) 299-
313. This paper reports on a fascinating set of studies about what Teigen calls the “preciseness paradox”. That is, under a wide variety of circumstances, the more precise the forecast, the more confident we are about the forecast. In fact, we should be less confident. The cause of the paradox is that when a forecaster provides detail, this is a cue that the forecaster has much expertise about the topic. “She must know what she is talking about!” Thus, this effect should be stronger for postdiction than prediction. It was. Consider one of Teigen’s studies. Subjects were asked how much confidence they would have among different informants when they visit Iceland and receive answers to the following question: Owingto various price regulation measures, this year’s inflation rate was down to 5%. Was it higher last year?
Responses:
Olafur said “Yes, it was.” Lams said “Yes, it was about 7%.” Jon said “Yes, it was between 5 and 98.”
Which of these statements will you be most confident about? Rank order the alternatives and try to give some of your reasons.
Teigen says that Olafur’s statement is the most general, and Larus’s the most exact. If Larus is right, so are Olafur and Jon. On the other hand Olafur can be right, while Larus and Jon are wrong (if inflation were to be 14%, for example). However, most of the subjects (16) were most confident in Larus. Eight subjects were most confident in Jon. Only seven subjects were most confident on Olafur. When the statements about inflation were converted from the past to be a forecast about next year’s inflation, the confidence in the most precise forecast (by Larus) decreases (to six of 34 subjects), but does not disappear. Teigen suggests that this occurs because people would not expect forecasters to be able to provide precise forecasts of inflation.
586
Research on forecasting
In situations where it is expected that experts can make good forecasts, added detail and preciseness are likely to lead people to have more confidence about the forecasts. This is often a problem when using scenarios to make forecasts. (I have argued elsewhere that scenarios should not be used to make forecasts; instead they should be used in the implementation phase (see Armstrong, 1985, pp. 40-45). The obvious solution is that the forecaster should provide quantitative estimates of the likelihood associated with forecast. If this is not acceptable within the organization, warming labels can be applied when scenarios are used. I suggest “Caution: It is difficult to assess uncertainty when using scenarios. The purpose of the scenario is to create a situation that seems plausible so that it can be used in contingency planning.” - J. Scott Armstrong
Reference Armstrong, York).
J.S., 1985,
Long-Range
Forecusring
(Wiley,
New
[Karl H. Teigen, Department of Cognitive Psychology, University of Bergen, Sydneshaugen 2, N-5001 Bergen, Norway]
Gerald J. Tellis, “The price elasticity of selective demand: A meta-analysis of econometric models of sales”, Journal of Marketing Research 25 (1988) 331-341. I’ve been aware of this article since the time it was published, but I repeatedly failed to find it. The search was worth the effort. Tellis has reviewed a large number of studies that attempted to estimate the price elasticity of demand for a brand, estimated through market share or brand sales equations. Some 40 studies yielded 367 usable esti-
mates. He then examined each study, categorising it on a number of factors: the other variables included in the model, the environmental characteristics including the type of product, the data characteristics, and the estimating method. The average price elasticity found was - 1.76, however certain biases apparently existed in many of the studies such as the omission of distribution or quality, use of cross-sectional data and temporal aggregation. Tellis suggests that correction for these biases increases the size of the elasticity to about - 2.5. After describing the various influences that might affect the observed elasticity, Tellis estimated a regression equation that purported to explain variation in these estimates. The differential effect of product category proved to be important in explaining where a priori arguments about the nature of the consumer’s response in the product market would suggest a greater price sensitivity to, for example, detergents than to pharmaceuticals or to mature rather than to new products. The model specification also proved to be important, where the ommision of such variables as distribution and quality led to biased estimates. The price elasticity has proved notoriously difficult to estimate for many companies, in part, I guess, due to poor data quality. The study gives any company a good figure to use in developing a preliminary understanding of its brand share-price relationship ( - 2.5). It also suggests many avenues of further research, in particular the use of these prior estimates in Bayesian type models of brand share. (In passing I note it gives little support for the view that econometric issues are important in modelling.) More research is needed over a wide variety of markets. - Robert
Fildes
[Profesor Gerald J. Tellis, School of Business Administration, University of Southern California, Los Angeles, CA 90084-1421, USA]
Research on forecasting
Finally, their meta-analysis results were used to develop initial estimates, “priors”, in a Bayesian estimation procedure, to forecast the diffusion of room air conditioners as additional diffusion data becomes available. Parameter estimates are updated by using a weighted average of the prior estimates and the values developed from the actual data (by using Srinivasan and Mason’s NLS estimation procedure), with weights equal to the inverses of their variances. The Bayesian scheme was found to produce more robust estimates. _ Paul Bottomley
References Bass, F.M., 1969, “A new product growth model for consumer durable?, Management Science, 15, 215-227. Coleman, J.S., E. Katz and H. Menzel, 1966, Medical Znnouations: A Diffusion Study (Bobbs-Merrill, Indianapolis). Fourt, L.A. and J.W. Woodlock, 1960, “Early prediction of market success of grocery products”, Journal of Marketing, 25, 31-38. Gatignon, H., J. Eliashberg and T.S. Robertson, 1989, “Modelling multinational diffusion patterns: An efficient methodology, Marketing Science, 8, 231-247. Holak, S.L. and D.R. Lehmann, 1990, “Purchase intention and the dimensions of innovation: An exploratory model”, Journal of Product Innovation Management, 7, 59-73. Kalish, S., 1985, “A new product adoption model with pricing, advertising and uncertainty”, Management Science, 31, 1569-1585. Kalish, S. and S.K. Sen, 1986, “Diffusion models and the marketing mix for single products”, in: V. Mahajan and Y. Wind (eds.), Innovation Diffusion Models of New Product Acceptance (Ballinger, Cambridge, MA) 87-116. Mahajan, V., E. Muller and F.M. Bass, 1990, “Dynamics of innovation diffusion: New product growth models in marketing”, Journal of Marketing, l-26. Simon, H. and K.H. Sebastian, 1987, “Diffusion and advertising: The German telephone company”, Management Science, 33, 451-466. Srivastava, R.K., V. Mahajan, S.N. Ramaswami and J. Cherian, 1985, “A multiattribute diffusion model for forecasting the adoption of investment alternatives for consumers”, Technological Forecasting and Social Change, 28, 325-333.
[Dr. Fareena Sultan, Harvard Business Soldiers Field, Boston, MA 02163, USA]
School,
Karl Halvor Teigen, “To be convincing or to be right: A question of preciseness”, in: K.J. Gilhooly,
M.T.G. Lines
Keane, of
Thinking
R.H. Logie and G. Erdiis (eds.), (Wiley, Chichester, 1990) 299-
313. This paper reports on a fascinating set of studies about what Teigen calls the “preciseness paradox”. That is, under a wide variety of circumstances, the more precise the forecast, the more confident we are about the forecast. In fact, we should be less confident. The cause of the paradox is that when a forecaster provides detail, this is a cue that the forecaster has much expertise about the topic. “She must know what she is talking about!” Thus, this effect should be stronger for postdiction than prediction. It was. Consider one of Teigen’s studies. Subjects were asked how much confidence they would have among different informants when they visit Iceland and receive answers to the following question: Owingto various price regulation measures, this year’s inflation rate was down to 5%. Was it higher last year?
Responses:
Olafur said “Yes, it was.” Lams said “Yes, it was about 7%.” Jon said “Yes, it was between 5 and 98.”
Which of these statements will you be most confident about? Rank order the alternatives and try to give some of your reasons.
Teigen says that Olafur’s statement is the most general, and Larus’s the most exact. If Larus is right, so are Olafur and Jon. On the other hand Olafur can be right, while Larus and Jon are wrong (if inflation were to be 14%, for example). However, most of the subjects (16) were most confident in Larus. Eight subjects were most confident in Jon. Only seven subjects were most confident on Olafur. When the statements about inflation were converted from the past to be a forecast about next year’s inflation, the confidence in the most precise forecast (by Larus) decreases (to six of 34 subjects), but does not disappear. Teigen suggests that this occurs because people would not expect forecasters to be able to provide precise forecasts of inflation.
586
Research on forecasting
In situations where it is expected that experts can make good forecasts, added detail and preciseness are likely to lead people to have more confidence about the forecasts. This is often a problem when using scenarios to make forecasts. (I have argued elsewhere that scenarios should not be used to make forecasts; instead they should be used in the implementation phase (see Armstrong, 1985, pp. 40-45). The obvious solution is that the forecaster should provide quantitative estimates of the likelihood associated with forecast. If this is not acceptable within the organization, warming labels can be applied when scenarios are used. I suggest “Caution: It is difficult to assess uncertainty when using scenarios. The purpose of the scenario is to create a situation that seems plausible so that it can be used in contingency planning.” - J. Scott Armstrong
Reference Armstrong, York).
J.S., 1985,
Long-Range
Forecusring
(Wiley,
New
[Karl H. Teigen, Department of Cognitive Psychology, University of Bergen, Sydneshaugen 2, N-5001 Bergen, Norway]
Gerald J. Tellis, “The price elasticity of selective demand: A meta-analysis of econometric models of sales”, Journal of Marketing Research 25 (1988) 331-341. I’ve been aware of this article since the time it was published, but I repeatedly failed to find it. The search was worth the effort. Tellis has reviewed a large number of studies that attempted to estimate the price elasticity of demand for a brand, estimated through market share or brand sales equations. Some 40 studies yielded 367 usable esti-
mates. He then examined each study, categorising it on a number of factors: the other variables included in the model, the environmental characteristics including the type of product, the data characteristics, and the estimating method. The average price elasticity found was - 1.76, however certain biases apparently existed in many of the studies such as the omission of distribution or quality, use of cross-sectional data and temporal aggregation. Tellis suggests that correction for these biases increases the size of the elasticity to about - 2.5. After describing the various influences that might affect the observed elasticity, Tellis estimated a regression equation that purported to explain variation in these estimates. The differential effect of product category proved to be important in explaining where a priori arguments about the nature of the consumer’s response in the product market would suggest a greater price sensitivity to, for example, detergents than to pharmaceuticals or to mature rather than to new products. The model specification also proved to be important, where the ommision of such variables as distribution and quality led to biased estimates. The price elasticity has proved notoriously difficult to estimate for many companies, in part, I guess, due to poor data quality. The study gives any company a good figure to use in developing a preliminary understanding of its brand share-price relationship ( - 2.5). It also suggests many avenues of further research, in particular the use of these prior estimates in Bayesian type models of brand share. (In passing I note it gives little support for the view that econometric issues are important in modelling.) More research is needed over a wide variety of markets. - Robert
Fildes
[Profesor Gerald J. Tellis, School of Business Administration, University of Southern California, Los Angeles, CA 90084-1421, USA]