WO2001024094A1 - Methodologie d'evaluation de l'impact promotionnel - Google Patents

Methodologie d'evaluation de l'impact promotionnel Download PDF

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Publication number
WO2001024094A1
WO2001024094A1 PCT/US2000/026997 US0026997W WO0124094A1 WO 2001024094 A1 WO2001024094 A1 WO 2001024094A1 US 0026997 W US0026997 W US 0026997W WO 0124094 A1 WO0124094 A1 WO 0124094A1
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WIPO (PCT)
Prior art keywords
product
promotions
residual
relationship
impact
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Application number
PCT/US2000/026997
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English (en)
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WO2001024094A9 (fr
Inventor
Yilian Yuan
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Ims Health Incorporated
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Publication date
Application filed by Ims Health Incorporated filed Critical Ims Health Incorporated
Priority to EP00967185A priority Critical patent/EP1177519A1/fr
Priority to AU77422/00A priority patent/AU7742200A/en
Publication of WO2001024094A1 publication Critical patent/WO2001024094A1/fr
Publication of WO2001024094A9 publication Critical patent/WO2001024094A9/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising

Definitions

  • the present invention is related to statistically measuring the impact of promotions on product sales, and more particularly, to techniques which employ time series analysis as part of an estimation methodology to determine such impact.
  • Time series analysis is one well-known econometric tool that has been applied to study the relationship between advertising and sales.
  • G. E. P. Box and G. M. Jenkins in their book, “Time Series Analysis: Forecasting and Control," San Francisco: Holden-Day, Inc. (1976), laid down the theoretical foundation of time series analysis and Box- Jenkins transfer function analysis.
  • R. M. Helmer and J. K. Johansson applied the Box- Jenkins transfer function analysis to studying the advertising-sales relationship using a vegetable compound data, see “An Exposition of the Box- Jenkins Transfer Function Analysis With an Application to the Advertising-Sales Relationship," Journal of Marketing Research, 227-239 (1977).
  • the procedural steps in applying the transfer function analysis technique are specified and applied to the sample advertising- sales data with particular focus on the advertising lag structure.
  • An object of the present invention is to provide improved techniques for statistically measuring the impact of promotions on product sales.
  • a further object of the present invention is to provide improved techniques which employ time series analysis as part of an estimation methodology to determine the impact of promotions on product sales.
  • Yet another object of the present invention is to provide promotion response methodology for systematically assessing the impact of promotional activity on product performance while taking into account other market variables.
  • the present invention provides techniques for estimating the impact of one or more promotions on product performance for a product.
  • a method is presented which involves determining market events which may impact product performance. The market events are examined to detect any abnormal event and, if abnormal events are detected, generating a description for each detected abnormal event. A relationship between each promotion and the product is then determined, and a promotion lag structure between the promotions and product performance is systematically detected. Functional forms are selected to account for any impact of the determined market events which may impact product performance, and are evaluated to account for the determined market event. The relationship between the promotions and product performance is quantified by taking into account the selected functional forms.
  • the product is a pharmaceutical product
  • the market event determining step involves manually determining one or more pharmaceutical market events which may impact pharmaceutical product performance.
  • the abnormality examining step is then statistically determining whether any of the pharmaceutical market events is an abnormal event and, if one or more abnormal events are detected, generating statistical descriptions for each detected abnormal event.
  • the promotion lag detection step involves fitting a univariate auto-regressive model to each promotion to determine one or more promotion residual series, regressing performance information for the product to determine a product residual, transforming the product residual into a product residual series, determining one or more cross-correlation functions between the promotion residual series and product residual series, plotting the cross- correlation functions to detect any lagged effect from the promotions corresponding to those functions, and selecting appropriate functional form which best fit the plotted functions.
  • FIG. 1 is a functional diagram of a system in accordance with a preferred embodiment of the present invention
  • Fig. 2 is a flow diagram illustrating the basic steps implemented in the system of Fig. 1;
  • Fig. 3 is a flow diagram illustrating the steps that may be implemented in one arrangement of a cross-correlation function useful in the system of Fig. 1;
  • Fig. 4 is a flow diagram illustrating the steps that may be implemented in one arrangement of a functional form evaluation useful in the system of Fig. 1.
  • the present invention adapts and expands upon certain time series analysis techniques so that the impact of any promotional activities on product performance can be determined.
  • cross-correlation functions are used to systematically detect promotion lag structure, and different functional forms are used to account for other market inputs in the model. While as exemplary embodiment of the present invention will be described herein to estimate the number of new prescriptions of a product that are attributable to DTC advertising, the approach has general applicability to study the impact of other forms of promotions such as professional detailing, sampling, and medical journal advertising, continuing medical educational events and meetings on product sales.
  • the present invention applies to studying the promotional impact on increasing both primary and secondary demand.
  • the prescription volume for the therapeutic class is used as the outcome variable.
  • product market share is used as the outcome variable.
  • step 210 the market events that may impact product performance, such as the arrival of new approved indications, the launch of competitive products, any positive or negative publicity, policy changes and the like, are identified. This step may be performed manually by a research analyst who is familiar with the particular market in the pharmaceutical industry.
  • step 220 any abnormalities in the data collected by the research analyst are detected, and descriptive statistics for each variable under study are generated.
  • descriptive statistics for example, off the shelf statistical software may be used to check the data collected by the data analyst to determine whether an abnormal number of prescriptions occurred in a time period to flag a probable human error.
  • the descriptive statistics such as the mean, standard deviation, minimum and maximum quantities are calculated for each of the variables under study. This step is required to ensure the quality of the data.
  • step 230 the model structure of multiplicative or additive which reflects the relationships between promotions and product prescription data to be multiplicative or additive is specified by the research analyst.
  • the research analyst may repeat the analysis for both multiplicative and additive model structures and then select the better model based on the modeling fitting information, the reasonableness of the coefficients estimates, and model robustness to changes in specification.
  • step 240 a cross-correlation function is used to systematically detect promotion lag structure. Referring to Fig. 3, a highly preferred arrangement of step 240 is further discussed.
  • an univariate auto-regressive model is fit to the promotion data "X" in 310, as fully described in the Box et al. article. This fitting is performed to remove the trend and seasonal components from the promotion variable. The model structure and the coefficients will be used in later steps.
  • the residual series is called XX.
  • the prescription data Y is regressed on variables which are known to have impact on product performance 320, such as trend, new product launch, new indication approved, and the like, to determine the residual of Y. Any standard multiple regression algorithm may be used, such as PROC REG in SAS®. This regression is implemented to remove the impact of other market events on the prescription data.
  • the model structure and coefficients estimated from the first sub- step are used 330 to transform the residual series of prescription data Y generated in 320 to determine a new residual series, YY.
  • cross- correlation function between the residual series XX determined in 310 and the residual series YY determined in 330 is calculated 340.
  • the detailed definition of cross-covariance and cross-correlation is fully described in the Box and Jenkins book which provides that the estimation of cross - co variance of Y and X is:
  • r ⁇ is plotted on one axis and k on the other axis to plot the cross correlation function 350, which is examined to detect the initial lag and the length of lagged effect.
  • the time period when the cross correlation function starts to increase/decrease is the time period the promotional effect starts, i.e. the initial lag.
  • the time period when the cross correlation function reduces to a low level indicates the time period the promotion effect disappeared, the difference between the time period of the initial lag and the time period when the effect disappeared is the length of the promotion lagged effect. This provides an initial assessment of the lag structure.
  • step 250 appropriate functional forms to account for other market events, such as new product launch, new indications approved, positive/negative publicity, etc., are evaluated. Referring to Fig. 4, a highly preferred arrangement of step 250 is further discussed.
  • the prescription data is plotted against time to examine the temporal pattern of the data 410.
  • the data may be plotted using prescription data as the vertical axis and time as the horizontal axis.
  • a functional form is selected 420.
  • a functional form is selected 420.
  • the prescription data exhibits shift in trend, marked by a changing slope: 0, t ⁇ T
  • M(t) ⁇ 1, t ⁇ T (4) either M(t) is included as an independent variable in the model , or the interaction between M(t) and the trend variable is included as an independent variable in the model , with the model producing a smaller residual being selected.
  • residual of the model and the coefficient estimate are examined 430 to determine whether the T is correctly specified. Since some market input may have lagged effect, a small integer such as 1 , 2 or 3 may need to be added to T to account for the lag. A big residual at time T indicates a problematic model fit. The pattern of the residual will suggest the choice of the T. For example, if there are big residuals for two time periods after T, the T should increase by two time periods.
  • step 260 the impact on prescription data of other variables such as product price, managed care impact, and the like, are evaluated, using the steps specified in step 240, by an analyst to determine whether the other variables are also impacting the prescription data and the lag structure.
  • step 270 the multiplicative or additive models specified in step 230 are fit to quantify the relationship between prescription variables and promotion variables, and other market events and market inputs.
  • step 280 multicollinearity problems between independent variables are checked. Some variables may have to be dropped due to the multicollinearity problem.
  • step 290 the model residual is evaluated to detect any auto-correlation in residual. If there is auto-correlation in the residual, an autoregressive structure for the residual should be included in the model.
  • step 295 the model is evaluated and validated to examine the stability and reasonableness of the model coefficients. For example, the model may be tested using the next several months of data to validate the model. The model estimates of the variables are applied to the next several months of data to compute sales, and are compared actual sales.
  • one advantage of the present invention is to combine econometric modeling techniques with transfer function analysis techniques in time series analysis to detect the lag structure, and to quantify the effects of various promotions controlling for the impact of other factors.
  • this methodology can isolate and quantify promotional effects on products' prescription share and the total therapeutic class prescription volume.
  • This methodology is applicable to measuring a wide range of promotional effects including DTC, professional detailing, sampling, and medical journal spending, and continuing medical educational events and meetings while controlling factors known to influence market share such as prescription price, competitive product launch, new indication approved, positive/negative publicity etc. Without accounting for the impact of these factors appropriately, it would not be possible to accurately measure the incremental prescriptions attributable to promotions.

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  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Accounting & Taxation (AREA)
  • Development Economics (AREA)
  • Strategic Management (AREA)
  • Finance (AREA)
  • Game Theory and Decision Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Physics & Mathematics (AREA)
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  • Theoretical Computer Science (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

L'invention concerne des techniques d'estimation de l'impact, pour un produit, d'une ou de plusieurs promotions sur les performances d'un produit. Dans un mode de réalisation préféré, un procédé est présenté, lequel consiste à déterminer les événements du marché pouvant avoir un impact sur les performances d'un produit. Les événements du marché sont examinés afin de détecter tout événement anormal et, si des événements anormaux sont détectés, une description est produite pour chaque événement anormal détecté. Une relation entre chaque promotion et le produit est alors déterminée et une structure de décalage de promotion entre les promotions et les performances du produit est détectée de manière systématique (voir figure, articles 210, 220, 230, 240). Des formes fonctionnelles sont sélectionnées pour prendre en compte tout impact des événements déterminés du marché pouvant avoir un effet sur les performances du produit, et elles sont évaluées afin de tenir compte de l'événement déterminé du marché. La relation entre les promotions et les performances du produit est quantifiée par prise en compte des formes fonctionnelles sélectionnées (voir figure, article 270).
PCT/US2000/026997 1999-09-30 2000-09-29 Methodologie d'evaluation de l'impact promotionnel WO2001024094A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
EP00967185A EP1177519A1 (fr) 1999-09-30 2000-09-29 Methodologie d'evaluation de l'impact promotionnel
AU77422/00A AU7742200A (en) 1999-09-30 2000-09-29 A promotional impact assessment methodology

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US15713999P 1999-09-30 1999-09-30
US60/157,139 1999-09-30

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WO2001024094A1 true WO2001024094A1 (fr) 2001-04-05
WO2001024094A9 WO2001024094A9 (fr) 2002-09-26

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7809601B2 (en) 2000-10-18 2010-10-05 Johnson & Johnson Consumer Companies Intelligent performance-based product recommendation system

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6029139A (en) * 1998-01-28 2000-02-22 Ncr Corporation Method and apparatus for optimizing promotional sale of products based upon historical data
US6035284A (en) * 1995-12-13 2000-03-07 Ralston Purina Company System and method for product rationalization

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6035284A (en) * 1995-12-13 2000-03-07 Ralston Purina Company System and method for product rationalization
US6029139A (en) * 1998-01-28 2000-02-22 Ncr Corporation Method and apparatus for optimizing promotional sale of products based upon historical data

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP1177519A4 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7809601B2 (en) 2000-10-18 2010-10-05 Johnson & Johnson Consumer Companies Intelligent performance-based product recommendation system
US8666844B2 (en) 2000-10-18 2014-03-04 Johnson & Johnson Consumer Companies Intelligent performance-based product recommendation system

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AU7742200A (en) 2001-04-30
WO2001024094A9 (fr) 2002-09-26
EP1177519A4 (fr) 2002-02-06
EP1177519A1 (fr) 2002-02-06

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