- Email: [email protected]
A marketing category management system: a decision support system using scanner data
Decision Support Systems 23 Ž1998. 259–271
A marketing category management system: a decision support system using scanner data James J. Jiang
a,)
, Gary Klein b, Roger Alan Pick
c
a College of Administration and Business, Louisiana Tech UniÕersity, P.O. Box 10318, Ruston, LA 71272-0046, USA School of Business, The UniÕersity of Texas at Permian Basin, 4901 E. UniÕersity BlÕd., Odessa, TX 79762-0001, USA Bloch School of Business and Public Administration, UniÕersity of Missouri-Kansas City, 5110 Cherry Street, Kansas City, MO 64110-2499, USA b
c
Accepted 10 August 1998
Abstract Point-of-sale scanner data provides a unique opportunity for analyzing consumer package goods ŽCPG. trends and patterns. Decisions of ever-increasing complexity are made possible by the amount of data available. Unfortunately, the analysis of such voluminous data requires complex techniques and processing requirements not available to many marketing decision makers. In this paper, we describe a prototype system which allows users to manage the complex models and scanner data to make forecasts in an interactive fashion. A limited test of the prototype allowed users not familiar with the underlying models to develop product forecasts. q 1998 Elsevier Science B.V. All rights reserved. Keywords: Decision support systems; Marketing management; Scanner data; BVAR forecasts
1. Introduction Marketing decision-making in the consumer packaged goods ŽCPG. arena benefits from advances in the collection and compilation of electronic scanner data w2,4,15x. The key problem facing today’s CPG brand managers is not lack of data but a lack of systems that transform voluminous scanner data into decisions of strategic advantage w2,5,15,16x. For example, without new system assistance, the five staff-days spent analyzing bimonthly store audit data would increase to 5000 staff-days to analyze weekly-level scanner data w17x. )
Corresponding author. Tel: q1-318-257-3445; fax: q1-318257-4253; e-mail: [email protected]
A review of the literature reveals that marketing models with scanner data appear unfamiliar to decision support systems ŽDSS. researchers w1,3, 12,18,25,26x. Consequently, problems in scannerbased system design receive little attention in the DSS literature. In addition, full scale applications require large capital investments in hardware and personnel. For this reason, current commercial systems solve isolated marketing problems, for example those focusing on pricing, feature and display effects w5,26x. To help overcome these deficiencies, we propose a marketing category management system that permits CPG brand managers to exert frequent control on in-store pricing and promotion variables as well as on out of store variables such as advertising. This
0167-9236r98r$ - see front matter q 1998 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 - 9 2 3 6 Ž 9 8 . 0 0 0 5 3 - 0
260
J.J. Jiang et al.r Decision Support Systems 23 (1998) 259–271
is termed a ‘level-two’ approach to solve today’s marketing scanner-data dilemma—too much data with too few marketing analysts. At level two, the system controls the marketing-mix variables in a given product category. This expands the more common ‘level-one’ approach of summary reports and exception reporting w2,19x. We will begin with an introduction to singlesource systems, those systems containing multiple measurements of a single source. A later section introduces the prototype implementation in detail. Bayesian vector autoregression ŽBVAR. is the modeling approach on which the prototype system is based. An evaluation of the system’s ability to support user requests follows the system description. We will conclude by summarizing the study and describing future research directions. 2. The data: single-source systems A single-source system is a database containing multiple measurements on single units of analysis, such as stores or households. The multiple measurements may include television viewing, advertising, in-store promotions, direct mail coupons, and scanned grocery purchases. The increasing popularity of single-source data is underscored by the fact that CPG firms spend about 1.3 billion dollars every year to purchase data from the two major data suppliers, Information Resources ŽIRI. and A.C. Nielsen. Single-source data is collected at various stages in the product flow. Shipment data is collected on items from the factory to the warehouse, withdrawal data between the warehouse and retailer, consumer data at take-away time, and data about promotional activity. Five primary databases on which single-source data are accumulated: Ž1. a household database with consumer data; Ž2. a store database with sales and local
promotions; Ž3. a retail factors database with pricing, display and features; Ž4. a promotion factors database with coupons and sweepstakes; Ž5. and an advertising database with active TV, print, and radio data. The power of a single-source system comes from a complex set of interactions among these five. Put simply, the system tracks what products are sold Žthe trade environment.; who bought these products Žthe consumer environment.; and why these products were bought Žthe promotion environment. w5x. Table 1 presents an example framework which could represent a single-source dataset as a four dimensional Cartesian product. That is, a singlesource dataset ŽC. s Geographic level ŽG. = Product ŽP. = Time ŽT. = Measurements ŽM.. For example, one Cartesian product could be C1 s Cincinnati metro-market area: at store level for Smith Groceries4 = All packaged beer products4 = Weekly data from 1995–1996 . = Õolume, price, promotion, and display4 . One can see how all the information is captured and how extensive the data quickly becomes. IRI and Neilsen invest tremendous amounts of time and money to develop their single-source data management systems. Although an abundance of data sounds wonderful, the amount is usually too large for effective use. For example, for every single numeric value stored in a research database in 1979, at least 1420 were stored in 1989 w10,15x. Currently, about two gigabytes of numeric values enter into IRI’s single-source systems every week w21x. The challenge facing the CPG industry is to efficiently and effectively analyze scanner data. In other words, in today’s competitive environment, CPG managers need automated systems to help analyze these data in support of decisions w15,20x. Little w14,15x argues that brand managers should make their decisions through model-based DSSs to
Table 1 Single-source dataset Ž1. Geographic Ž2. Product Ž3. Time Ž4. Measurements
ŽG.: store, cluster of stores, metro-market, and national, etc. Žindividual Conditional forecast aggregate at geographic level. ŽP.: sku, brand, etc. Žindividual vs. aggregate at product category level. ŽT.: week, 2 weeks, month, and quarter, etc. Žindividual vs. aggregate at time horizon. ŽM.: price, volume, feature, and display, etc.
J.J. Jiang et al.r Decision Support Systems 23 (1998) 259–271
261
effectively use single-source data. His reasons are: Ž1. Brand managers’ information needs have shifted from traditional marketing status reports Ži.e., what are sales, price, and total revenue?. to marketing response reports Ži.e., what is price elasticity, advertising response, promotional effectiveness.. However, marketing response reports require complex models. Ž2. Marketing response output can help brand managers understand competitive interactions in a given product category. However, many existing models are hard to use, therefore, rarely applied in the marketing industry. Ž3. There is a shortage of qualified marketing analysts in industry to help brand managers construct and use ‘good’ marketing models. Curry w5x also argues that a model-based DSS offers bright long term prospects. His reasons include: Ž1. Contrary to the situation 10 years ago, managers are now accustomed to using models for specific problems; e.g., forecasting, product positioning, pricing. Ž2. Dozens of models have been developed for key decision areas. The main challenge today is to use existing models productively rather than to develop new models. Ž3. Marketing planning typically involves multiple parts, each of which makes use of at least one model. Ž4. Models can find more consistent optimal solutions to problems than can expert systems.
critical to brand management. Previous studies have found BVAR to be an effective forecasting model. The use of BVAR in this precise context has been studied by Curry et al. w6,8x, Curry and Mathew w7x, and Whiteman et al. w24x. Their conclusions state
3. The model: Bayesian vector autoregression
Table 2 Theil values for BVAR, VAR and GARCH models
In order to choose a model for this system, we wanted to pick a model which matches the CPG situation. The CPG situation involves a large volume of data. Furthermore, that data involves numerous variables which interact with each other, and the data is a collection of time series. Marketers in the CPG arena, whether manufacturers or retailers, need to be able to make both conditional Ž‘What If?’. and unconditional forecasts that account for competitive interactions among brands. The BVAR class of model meets all of these needs w6–8,24x. In addition, the BVAR has capabilities and accuracy compared with other popular techniques. BVAR is able to provide forecasts of multiple data series where marketing activities of all players can be considered. Such a vector forecasting technique is
Using POS scanner data, we establish that BVAR is a superior forecasting tool compared to Exponential Smoothing, univariate ARIMA, Box–Jenkins transfer function models, and MARMA. Because BVAR uses few degrees of freedom and is easy to identify, it satisfies the practical requirements of category management. Finally, using impulse response functions and conditional forecasts, we illustrate that BVAR provides important insights for the category manager. ww6x, page 197x Other forecasting techniques capable of handling the data include the vector autoregression models ŽVAR. and the generalized autoregressive conditional heteroskedasticity ŽGARCH. models. Table 2 is a comparison of the three methods for the dataset of this study. As can be seen, the BVAR outperforms these two techniques not previously considered. The results suggest that over-parameterization could be a problem for both the VAR and GARCH models. Especially since a large number of lags are the normal situation in the brand management applications.
Lag 2
Lag 3
Lag 4
BVAR Brand 1 Brand 2 Brand 3 Brand 4
6.01 3.71 7.17 3.49
6.67 4.11 7.08 3.09
8.67 6.91 6.36 3.39
VAR Brand 1 Brand 2 Brand 3 Brand 4
11.73 10.94 8.29 8.27
12.14 11.56 8.02 9.67
13.25 13.33 7.87 8.52
GARCH Brand 1 Brand 2 Brand 3 Brand 4
5.66 2.44 4.41 4.74
126.87 234.61 156.39 149.57
1345.87 2652.93 1623.99 1769.45
262
J.J. Jiang et al.r Decision Support Systems 23 (1998) 259–271
BVAR forecasts movements in an n-dimensional state vector. Mathematically, vector autoregression can be written: x Ž w q l . s A x Ž w . q Dm Ž w q l . q e Ž w q l . where; x Ž w . is an N = 1 state vector for time period w, A is an N = N state transition matrix, N s Ž n = l . q 1, n s number of time series variables in the system, l s number of lags, D is an N = q coefficient matrix, mŽ w q 1. is a q = 1 vector of merchandising variables for time period w q 1, e Ž w q 1. is an N = 1 white noise vector for time period w q 1, and w is an index for time period. Conceptually, the matrix A represents the ‘natural laws of motion’ influencing a category’s transition from one state to the next, and the matrix D captures the effects of merchandising variables. The state consists of variables which are out of the modeler’s direct control such as volume sales for each UPC and price and merchandising activities for other brands. The merchandising variables include items that the modeler can control in a deterministic way such as displays, features, coupon drops, and ownbrand prices. The system’s dataset consists of n endogenous variables and q exogenous variables. All the relations in the model are linear. There is a total of N = q free coefficients in the model w9x. Since the N = q free coefficients are estimated using data, forecasts using unrestricted vector autoregression often suffer from over-parameterization of the model and, consequently, large out-of-sample forecast error. The Bayesian approach to estimation specifies ‘fuzzy’ restrictions on the coefficients rather than sharply including or dropping a given regressor w13x. Without reviewing the ‘fuzzy’ restrictions in detail, we outline the assumptions and principles of the Bayesian approach to VAR. ŽFor a detailed review see Refs. w6,13x or Ref. w22x.. Bayesian VAR uses Normal prior distributions with means of zero and small standard deviations for long lags w9x. This allows the system to estimate the coefficients using Theil’s mixed estimation technique w23x. The estimation process chooses a set of ‘hyperparameters’ for the prior distribution in the system. Identifying the ‘best’ BVAR model includes the following difficulties: Ža. determining potential
variables, Žb. placing prior distributions on coefficients, and Žc. the lack of clear guidance to evaluate the tremendous number of potential coefficients. The system we propose eliminates many of the complex choices inherent in BVAR modeling. The prototype helps a user select variables by efficiently partitioning them into time series and deterministic variables. The system also helps simplify identification of the prior distribution by reducing the required number of hyperparameter specifications. Finally, the system helps a user digest the huge volume of estimation results generated from the existing system to find a prior distribution and set of variables that maximizes the model’s out of sample forecast accuracy. This model-building activity is the first part of our DSS, which we call the model fitting subsystem. Using the model fitting system is a decision-making activity in which the decision-maker’s goal is to create the best possible model. The system assists in this by allowing the decision-maker to create a sequence of alternative models and examine their forecasting performance. We will go into this system in greater detail in Section 4. The second part of the DSS is the model application subsystem. This subsystem uses the model built by the model selection subsystem. The user of this subsystem could be, but need not be, the same as the user of the model selection subsystem. The model application subsystem is the part of the DSS that actually assists in solving a business problem. Without any further modification, the model will answer unconditional questions such as: ‘What are expected baseline volumes, by week, for the next eight weeks? What are expected total revenues and market shares during the same time period?’ The model can also produce conditional forecasts to answer ‘what-if’ questions. For example, ‘If we lower the price of a given SKU by 10% two weeks in a row, what will our sales, market share, and total revenues be for each week for the 6 weeks that follow?’ The BVAR model application subsystem to answers these types of questions by generating forecasts conditioned upon fixed values for certain variables. The objectives of the system, therefore, are to aid marketing analysts in generating high-quality forecasting models and to help brand managers generate and evaluate marketing strategies and policies. The
J.J. Jiang et al.r Decision Support Systems 23 (1998) 259–271
BVAR model-fitting and model-application sub-systems in the integrated DSS facilitate these services to the users.
4. A category management system 4.1. The conceptual model Fig. 1 shows a conceptual model of the category management system. A defining element of a DSS is that the functionality derives from the interaction between the computer and decision maker. This particular system is designed for two different types of users: retailing managers and marketing analysts. A marketing analyst’s responsibility is to use data and analytical models to provide information for retailing managers’ decision needs. The model then provides forecasts for the model-application subsystem. Category managers use the model-application system to set up their marketing tactics, such as price-setting, featuring, and merchandising. The system allows them to input their tactics and make a forecast conditioned upon these specifications. Category managers can try a number of possibilities until they find a marketing plan with satisfactory forecast results.
263
The system supports brand managers as well as category managers. A brand manager for a manufacturer can only affect the marketing mix of the UPCs under hisrher control. Their usage of the system would generally only be conditioned upon the smaller number of variables that they control, leaving the other variables outside their control to be forecast. If they wish to anticipate the impact of some action by the competition, this system will allow them to make forecasts conditioned upon that action. The dataset used in the prototype system was sponsored by a major grocer and was collected by IRI. At the request of the sponsors, data was aggregated and the category disguised. This dataset retains 2 years of sales histories Že.g., price, and volume., consolidated by 3 SKUs across all stores in a major U.S. metropolitan area, aggregated on a weekly basis. It also includes in-store and out-of-store causal data Že.g., display, feature, advertising.. In general, given the large number of items in a typical CPG category, item aggregation is almost always necessary w5,11x. Variables collected by IRI can be aggregated to the managers’ desired level of inquiry. Three logical candidates for aggregation are single store, store type, and metro-market w11x. We use data aggregated to the market level as requested by the sponsoring company. However, it is possible to implement the BVAR model using pooled store and individual store level data. 4.2. Model-fitting subsystem and interfaces
Fig. 1. Conceptual model of the category management system.
Fig. 2 shows the conceptual structure of the model-fitting subsystem. The following activities are controlled by the model-fitting subsystem: 1. Model specification: specifying the BVAR model. 2. Model generation: formatting user input for solution. 3. Model execution: controlling the actual running of the model. 4. Model output generation: generating the user reports. 5. Model storage: saving the calibrated category model into the model base. The latter four functions are relatively straightforward involving common practices in model management. The first function, model specification, involves complex interaction with the marketing ana-
264
J.J. Jiang et al.r Decision Support Systems 23 (1998) 259–271
4.2.1. Stage 1: specify time-series Õariables In this stage, a user must select the forecast variables for a particular product category. The user can also update the existing variables in a category before parameters are estimated by the model-fitting system. The process of selecting or updating variables is interactive, and driven by menus and question-answering. When specifying a structure for time-series variables, the user first selects time-series variables to be forecast by the system. ŽSee Table 3.. Then, for each continuous variable the user enters the number of time lagged values to include or allow a default value. Finally, the user specifies deterministic variables using a structure similar to that of the forecast variables. For each question, the system provides context-sensitive help messages. The fitting system also allows the user to update part of the specification without revising the entire model. Fig. 2. Conceptual structure of the model-fitting system.
lyst. To illustrate the model generation process, Fig. 3 shows the proposed BVAR model creation decision process. Three major stages can be identified to construct a ‘best’ BVAR model for a particular product category.
4.2.2. Stage 2: specifying a prior distribution In this stage, the system requires input for four values: mean, weight, tightness, and decay. For greater support, each question has its own help message to guide users who are not familiar with these terms ŽAppendix A.. Furthermore, the system checks the specification for each parameter and automatically replaces any values that are out-of-range with default values. The user can review the existing specifications at any time and update these values on the screen.
Table 3 Time series variable selection Stage ŽI.: Question 1—selecting time series variables S S S S S S S
Fig. 3. The proposed BVA model-fitting decision process.
TSUN1: Brand 1’s ŽScotties. PRCD1: Brand 1’s ŽScotties. TCOM1: Brand 1’s ŽScotties. TSUN2: Brand 2’s ŽKleenex. PRCD2: Brand 2’s ŽKleenex. TCOM2: Brand 2’s ŽKleenex. TSUN3: Brand 3’s ŽPuffs. PRCD3: Brand 3’s ŽPuffs. TCOM3: Brand 3’s ŽPuffs. HELP: Help screen!!!
Total volume sales Store price on deal Total TV commercial time Total volume sales Store price on deal Total TV commercial time Total volume sales Store price on deal Total TV commercial time
Cursor ´ Move UprDn; Enter ´Select. S indicates a selected variable.
J.J. Jiang et al.r Decision Support Systems 23 (1998) 259–271
265
Table 4 In-sample fit report—Report I: Summary report of system equations Equation no.
Dependent variables
Total observations
R Unadjusted
Durbin Watson
Q statistic
Adjusted
Degree of freedom
Significance level
1 2 3 4 5 6 7
TSUN1 PRCD1 PRCN1 PRCD2 PRCN2 PRCD3 PRCN3
112 112 112 112 112 112 112
0.79 0.65 0.77 0.67 0.78 0.62 0.79
0.74 0.61 0.74 0.64 0.75 0.58 0.74
2.13 2.14 2.20 2.05 2.06 1.95 1.93
39.32 49.96 20.16 20.03 25.67 19.92 34.61
104 104 104 104 104 104 104
0.12 0.01 0.91 0.92 0.69 0.92 0.26
The higher the value of R, the better the model fits the data ‘in-sample.’ The closer the Durbin Watson statistics is to 2, the better are forecasts. High significance for the Q-statistic indicates excessive residual autocorrelation.
4.2.3. Stage 3 (IteratiÕe improÕements): find candidate solutions When the user decides that srhe wants a solution, the model generation system generates a BVAR model in Regression Analysis Time Series’ ŽRATS. format. Then the model execution system uses RATS time series modules to compute the results. RATS produces voluminous output; more than 100 pages
for a category with only four products. The output generation system scans the output and produces a concise report for the user. Tables 4–6 show example reports of the in-sample fit, model structure, and out-of-sample forecasting ability of a user’s specified model. The model structure diagnosis report in Table 5 shows the relationships in the current model and allows the user to judge the model against experi-
Table 5 Model structure diagnosis report—Report II: Model structure diagnosis Part ŽI.: T-test a Variableb
Lag
Parameter
Standard error
T statistic
TSUN1 TSUN1 PRCD1 PRCD2 FEAT1 SUMMER FALL
1 4 1 2 0 0 0
0.329 0.160 0.534 y0.161 0.010 0.159 0.237
0.09 0.07 0.08 0.07 0.01 0.03 0.08
3.42 2.13 6.31 y2.28 2.15 2.97 3.21
Independent variable
F-test
Significance level
TSUN1 ŽScotties. Žall previous weeks. PRCD1 ŽScotties. Žall previous weeks. PRCD2 ŽKleenex. Žall previous weeks.
3.25 3.87 3.11
Part ŽII.: F-test c
a
0.02 0.01 0.05
Equation no: 1; dependent variable: TSUN1 ŽScotties.; significant Ž t G 2.00. individual influence on TSUN1. In your model, the dependent variable ŽTSUN1. is highly related to the above variableŽs.. c Dependent variable: TSUN1 ŽScotties. Žcurrent week.; influence total effects are significant Ž F G 3.0.. The F-test summarizes the T-tests shown in the previous section. ŽThe T-tests are ‘week specific’ while an F-test integrates over all weeks included in a model.. b
J.J. Jiang et al.r Decision Support Systems 23 (1998) 259–271
266
Table 6 Output for Theil statistics—Report III: Out of sample forecasts Weeks
Theil
Attention!
1 2 3 4 5 6 7 8 8 weeks Theil totals
1.02 0.87 0.74 0.66 0.32 0.21 0.19 0.15 4.16
X
Theil statistics for dependent variable: TSUN1 ŽScotties.. Theil-1: Your model outperforms the Naive Žbaseline model. for this forecast period. Theil)1: Your model fails to outperform the Naive model for this forecast period.
ence and intuition. Once the user is satisfied with the specified model, the estimated model is stored in the model base for the model-application system. If the user is not satisfied with the model, srhe can revisit the variables andror prior distribution. Model construction is an iterative process until the user is satisfied with the model’s out-of-sample forecast performance. Model generation, model computing, output generation, and model storage are transparent to the user.
3. Model execution: forecasting the specified marketing conditions. 4. Output generation: Generating the reports to users 5. Scenario history maintenance: store the 16 most recent scenarios for later reference. In the dialogue manager of Fig. 4, a brand manager specifies the marketing scenario that srhe wants to forecast. For example, if our competitors lower the price of their product 10% 2 weeks in a row, what will our sales, market share, and total revenues be for each week in the 8 weeks that follow? The user is allowed to specify any scenario by setting prices, features and displays for one or more brands in a category. The requirements of specifying a marketing scenario are shown in Table 7. The user provides input via a form on screen. Any invalid inputs, such as typing a character instead of numeric key for a number are rejected by the system. In addition, the user can review previously attempted scenarios managed by the scenario history management system. After the user has chosen a marketing scenario, the system generates a conditional forecast model based on the model developed. The conditional forecast model generation is controlled by the model generation system. The model execution system uses
4.3. Model-application subsystem and interface Today, success in the PG business boils down to who can best predict the future w5,17x. Brand managers want to know what is going to happen to their business if they change their prices, promotional strategy or other elements of the marketing mix, either nationwide or market by market. The modelapplication system provides this kind of predictive ability. The model-application system transports the abstract model developed by the model-fitting system to the practical world of retailing or manufacturing. Fig. 4 shows the conceptual structure of the prototype model-application system. The following activities are conducted by the system: 1. Marketing scenario specification: marketing conditions specified by users. 2. Model generation: format BVAR model for execution.
Fig. 4. Conceptual structure of the model-application system.
J.J. Jiang et al.r Decision Support Systems 23 (1998) 259–271 Table 7 Marketing scenario input Conditional marketing scenario Week 1 Week 2
Base period ŽDate: 3r10r97.
Price 1 Price 2 Price 3 Adv 1 Adv 2 Adv 3 M1 M2 M3
0.49 1.09 1.19 155 17 35 ON OFF ON
0.69 1.09 1.29 10 17 100 OFF OFF ON
0.69 1.09 1.39 10 25 150 OFF OFF ON
267
dures are transparent to a user. Table 8 shows the sample 8-week forecast for one brand Žeach brand in a category has a similar report.. Table 9 illustrates how the system summarizes sales volume. A similar report is generated for total revenues.
5. Evaluation
Brand 1: Scotties. Brand 2: Kleenex. Brand 3: Puffs.
RATS to compute the conditional forecast results. An output generation system is used to digest the huge amount of printed output generated by RATS, and isolate certain statistics useful to the managers, such as market share. These algorithms and proce-
Can marketing managers use the prototype system effectively? Twenty-one marketing managers were asked to use the prototype system to construct a BVAR model for a particular product category. All twenty-one successfully constructed the BVAR model. The mean quality of the specified BVAR model was 4.03 with a variance of 0.33. Model quality is measured by the out-of-sample forecasting ability of a user’s model Theil statistics. A Theil less than 8 indicates that a model performs better than a naive model defined as a function of predicted and actual values w23x. Smaller Theil values mean better forecasts.
Table 8 Output for single brand forecast—Brand name: Brand 1 ŽScotties. Week 1
Week 2
Week 3
Week 4
Baseline forecast Volume Price Total revenue Market share
228.57 0.50 114.38 69
237.30 0.50 117.71 68
238.86 0.50 118.51 69
237.96 0.49 117.43 69
Conditional forecast Volume Price Total revenue Market share
168.45 0.69 116.23 63
187.83 0.69 129.61 66
233.87 0.61 142.04 70
240.86 0.56 135.29 71
Week 5
Week 6
Week 7
Week 8
Total
Baseline forecast Volume Price Total revenue Market share
236.56 0.49 115.83 69
255.31 0.47 119.96 70
264.38 0.46 120.98 70
268.35 0.45 121.03 70
1967.5 0.49 945.83 69
Conditional forecast Volume Price Total revenue Market share
233.19 0.54 125.37 71
250.16 0.57 126.73 71
257.83 0.49 125.95 71
260.98 0.48 125.09 70
1833.5 0.58 1026.3 70
J.J. Jiang et al.r Decision Support Systems 23 (1998) 259–271
268
Table 9 Sales volume summary report for the entire category Week
1
2
3
4
5
6
7
8
8 weeks total
Brand 1 2 3
Ž97:5:18. 168.5 73.3 22.0
Ž97:5:25. 187.8 73.4 20.6
Ž97:6:1. 233.9 75.7 20.7
Ž97:6:1. 240.9 75.1 20.5
Ž97:6:15. 233.2 73.4 19.8
Ž97:6:22. 250.2 79.0 22.0
Ž97:6:29. 257.8 82.3 23.4
Ž97:7:5. 261.0 84.3 24.0
1833.2 616.5 172.9
Sales volume conditional forecasts ŽUnit: 1000..
The managers were able to use the system after less than an hour of training. None of them were statisticians; they could not have constructed, used, or interpreted BVAR models using RATS alone without this DSS to assist them. Managers were asked to measure the ease of use of the prototype system and their confidence in the constructed model. The mean responses were 8.24 out of 10 Ž10— extremely easy to use. and 8.14 out of 10 Žextremely confident., respectively. Subjects with the aid of the proposed system not only successfully constructed a BVAR model but also felt that the system is quite easy to use and are confident that they built a good model. It is fair to say that system usage leads to successful model construction; each subject was successful. Stronger evidence of the system’s role in the process would be provided by comparing these results to cases where no support system were available or where some alternative systems were used. However, it is an extremely difficult task to find subjects who are familiar with BVAR modeling and are qualified to be subjects in such an experiment. Subjects in the present experiment were asked, ‘Can you write a BVAR model and run it in RATS environment?’ The answer was always ‘no.’ 6. Conclusions and future research Checkout scanners generate a tremendous volume of marketing data for firms in the consumer packaged goods industry. These data create a new opportunity for brand managers to better understand their customers, to make better forecasts, and to better plan their tactics. However, the overwhelming amount of scanner data in conjunction with the complexity of marketing environments turns the CPG-brand manager’s dream into a nightmare. To
overcome this nightmare, researchers argue the use of model-based DSS to automate data analysis. We report on a prototype Category Management DSS that introduces the use of BVAR model to practitioners. The contributions of this system include the following. Ž1. A system that is designed for category management. Current scanner-based DSSs can only either solve isolated marketing problems Ži.e., pricing. or one brand in a given category at a time w5,26x. Ž2. The system simplifies the usage of an econometric BVAR for scanner datasets. The model can digest the volume of scanner data and is able to effectively capture all marketing-mix variables in a product category. The prototype system simplifies the procedure of constructing a BVAR model and provides a user-friendly interface. Ž3. Instead of adopting a traditional approach, the prototype system separated model management into two subsystems, modeling fitting and model application. This allows the DSS to be interactive to marketing analysts and marketing brand managers, separately. This allows brand managers, instead of asking them to construct analytical models, to focus on their business problems. Marketing analysts can use the model-fitting subsystem to automate model creation. Ž4. The prototype system demonstrates an effective way to integrate an advanced RATS package into the marketing DSS. By implementing an output generating subsystem in model-fitting and model-applications subsystems, the prototype DSS reports only significant and relevant information to the users. The traditional volume of output generated by advanced time-series statistical systems are not presented to the users. In order to completely automate the BVAR model fitting stage, future research on BVAR model fitting
J.J. Jiang et al.r Decision Support Systems 23 (1998) 259–271
heuristics is encouraged. Instead of playing ‘what if’ marketing scenarios, a brand manager may seek an ‘optimal control’ solution that generates ‘next week’s’ marketing mix as a closed-loop feedback function of ‘this week’s’ market results. Optimal marketing algorithms based on BVAR modeling represent a promising direction for future research. The automation of other applications using BVAR, such as pricing and advertising strategies, provides challenges for DSS researchers w7,18,24x. Another promising direction for future research would be to include more complex interactions among brands. For example, we could assume that other brand managers would have this tool or a similar one available to them. Then they would presumably run this same system to make forecasts conditioned upon their own actions and choose the best action that they are able to find. A better system would take this kind of activity into account and searching for a game-theoretic minimax solution. IRI has developed a very complex single-source database management system for the marketing industry. However, an easy-to-query database interface should be designed in a DSS to provide the managers a way to specify a subset for their needs Že.g., a particular set of stores, products, or time periods.. The proposed single-source dataset framework of Table 1 could be used as the basis for designing such an interface to retrieve the data. Acknowledgements The authors would like to thank the companies of Information Resources ŽIRI. for their support of this research through the Integrated Research Center at the University of Cincinnati. Dr. Curry’s ŽUniversity of Cincinnati. comments on this project is highly appreciated. Dr. Doan’s ŽEstima. Regression Analysis of Time Series software grant is also appreciated. Appendix A. Help screen for prior specification phase Ž1. MEAN: w0.1 to 1.0x governs a variable’s own first-week lag in each equation. Setting MEAN at 1.0 centers the prior distribution on a model that assumes this week’s value will be identical to last week’s value except for random movements.
269
The MEAN index controls how much weight a variable’s own one-week lag has on its current level. ŽFor example, how much do last week’s sales influence this week’s sales.. A MEAN value of 0.0 indicates no CARRYOVER effect from last week while a value of 1.0 means that Žon average. last week’s level DETERMINES this weeks level. Remember that no matter what value you specify, the data can override your judgement. However, by setting MEAN at a low value Ž0.1 to 0.4. you are effectively demanding that carry-over effects—revealed by the data—be very large before they can impact results. Normally, MEAN is set to 1.0 because carry-over effects are typically important for most time series. Ž2. OTHER’S WEIGHT: w0.1 to 1.0x controls how much weight one variable Žsay price. may have when affecting another variable Žsay sales.. Note that the MEAN index controls a variable’s own first lag. Its value is typically higher than the values for WEIGHT which controls the impact of all other variables’ first lags. If WEIGHTs 0.0 your model is effectively a set of univariate autoregressions. That is, each variable is being predicted only its own past values. Setting WEIGHTs 0.0, therefore, removes competitive interactions and marketing mix effects from the model. Ž3. TIGHTNESS: w0.1 to 2.0x directly controls the standard deviation of your prior, that is, how much weight is given to each variable’s own lags other than week one. Setting TIGHTNESS at a very small value, say 0.01 virtually eliminates the effects of 2nd, 3rd, and higher order lags even if you have specified such lags in your model. Normal values for this parameters are in the range 0.1 to 0.4. Generally values in the range 0.8 to 1.2 are considered ‘loose’ and permit the data to override your judgement. Ž4. DECAY: w0.1 to 1.0x summarizes how quickly carry-over effects Žfrom one week to the next. dissipate. The higher the value the more quickly effects dissipate; e.g., the less successive weight put on each lag as you move back in time: last week, 2 weeks ago, 3 weeks ago, etc. Normal values for this index are in the range w0.1 to 0.2x. The system default is 0.2.
270
J.J. Jiang et al.r Decision Support Systems 23 (1998) 259–271
References w19x w1x M.M. Abraham, L.M. Lodish, An implemented system for improving promotion productivity using store scanner data, Marketing Science 12 Ž1993. 248–269. w2x R. Blattberg, Learning How the Market Works, Marketing Science Institute Conference, Cambridge, MA, Sep. 21–22, 1989. w3x R. Blattberg, R. Briesch, E.J. Fox, How promotions work, Marketing Science 14 Ž1995. 122–132. w4x D. Curry, Single-source systems: retail management present and future, Journal of Retailing 65 Ž1989. 1–20. w5x D. Curry, The New Marketing Research Systems, Wiley, New York, 1993. w6x D. Curry, S. Divakar, S. Mathur, C. Whiteman, BVAR as a category management tool: an illustration and comparison with alternative techniques, Journal of Forecasting 14 Ž1995. 181–199. w7x D. Curry, G. Mathew, Optimal demand-side retail pricing for EDLP conditions, Working paper, the Center of Integrated Research Systems, The University of Cincinnati, April, 1997. w8x D. Curry, G. Mathew, N.T. Bruvold, Store level scanner data and category management: modelling and estimation techniques, Working paper, The Center of Integrated Research Systems, The University of Cincinnati, July, 1996. w9x T. Doan, R.S. Litterman, C.A. Sims, Forecasting and conditional projection using realistic prior distribution, Econometric Review 3 Ž1984. 1–100. w10x G. Eskin, Single Source Data: The U.S. Experience, presented to The Special Joint ARFrMRS Research Leaders Seminar, Boston, MA, July 24, 1989. w11x E.W. Foekens, P.S. Leeflang, P.R. Wittink, A comparison and an exploration of the forecasting accuracy of nonlinear models at different levels of aggregation, Working Paper, Department of Economics, University of Groningen, Groningen, Netherlands, July, 1993. w12x P.M. Guadagni, J.D. Little, A logit model of brand choice calibrated on scanner data, Marketing Science 3 Ž1983. 203– 238. w13x R.S. Litterman, A Bayesian procedure for forecasting with vector autoregressions, Working paper, Massachusetts Institute of Technology, Department of Economics, Boston, MA, 1980. w14x J.D.C. Little, Decision support systems for marketing managers, Journal of Marketing 43 Ž1979. 9–26. w15x J.D.C. Little, New Opportunities in a Changing World—The 1990 Philip McCord Morse Lecture, ORSArTIMS Joint National Meeting, Philadelphia, PA, Oct 29, 1990. w16x J. McCann, Overview of marketing system past, present, and future, Marketing Science Institute Conference, Cambridge, MA, Sep 21–22, 1989. w17x J. McCann, J.P. Gallagher, Expert Systems for Scanner Data Environments: The Marketing Workbench Laboratory Experience, Kluwer Academic Publishers, 1989. w18x J.H. Pedrick, F.S. Zufryden, Evaluating the impact of advertising media plans: a model of consumer purchase dynamics
w20x w21x w22x w23x w24x
w25x
w26x
using single-source data, Marketing Science 10 Ž1991. 111– 130. B. Peters, ‘Brave new world’ of single source information, Marketing Research 2 Ž1990. 13–21. J.B. Rochester, D.P. Douglass, Building marketing information systems, IrS Analyzer 28 Ž1990. . J. Schmitz, G. Armstrong, J. Little, Coverstory—automated news finding in marketing, Interfaces 20 Ž1990. 29–38. C.A. Sims, Macroeconomics and reality, Econometrica 48 Ž1980. 1–48. H. Theil, Principles of Econometrics, Wiley, New York, 1971. C.H. Whiteman, C. Curry, S. Divakar, S. Mathur, Dynamically optimal pricing strategies for consumer packaged goods, Working paper, the Center of Integrated Research Systems, The University of Cincinnati, May, 1996. D.R. Wittink, J.C. Poter, S. Gupta, Dangers in using marketing-level data for determining promotion effects, Marketing Science Institute Report No.93-115, 1–40. M.J. Zenor, The profit benefits of category management, Journal of Marketing Research 31 Ž1994. 202–213.
Dr. James J. Jiang is an Associate Professor of Computer Information Systems at the College of Administration and Business, Louisiana Tech University. His PhD in Information Systems was awarded by the University of Cincinnati in 1992. He earned his MBA in Management Science and MS in Applied Mathematics degrees from Wright State University in 1988 and 1989, respectively. His research interests include system development and implementation and marketing modeling-based systems. He has published more than 50 journal articles in these areas. He is a member of ACM, IEEE, and DSI. Dr. Gary Klein is Couger Chair of Information Systems at the University of Colorado in Colorado Springs. He obtained his PhD in Management Science at Purdue University. Before that time, he served with Arthur Anderson in Kansas City and was director of the Information Systems department for a regional financial institution. He was previously on the faculty at the University of Arizona, Southern Methodist University and Louisiana Tech University. His specialties include information system development and mathematical modeling with 50 publications in these areas. In addition to being an active participant in international conferences, he has made professional presentations on Decision Support Systems in the US and Japan where he once served as a guest professor to Kwansei Gakuin University.
J.J. Jiang et al.r Decision Support Systems 23 (1998) 259–271 Roger Alan Pick is an Associate Professor of Management Information Systems at the Henry W. Bloch School of Business and Public Administration at the University of Missouri-Kansas City. He joined the faculty at the Bloch School in 1993. Besides UMKC, Dr. Pick has taught at Purdue University, the University of Wisconsin-Madison, the University of Cincinnati, and Louisiana Tech Unversity. His PhD in Management Science Žmajor in Systems Analysis and
271
Computer Science, minor in Applied Economics. was awarded by Purdue University in 1984. His MS Žfrom Purdue. and his BS Žfrom Oklahoma University. degrees are in mathematics. Pick is a member of the ACM, DSI, IEEE and INFORMS. Dr. Pick’s research interests are in model management, decision support systems, marketing information systems, and in the economics of information technology. His research on these topics has resulted in scholarly articles appearing in Management Science, Communication of the ACM, Journal of Management Information Systems, Expert Systems, and other outlets. For more information, see his homepage at http:rrcctr.umkc.eduruserrrpickr
A marketing category management system: a decision support system using scanner data James J. Jiang
a,)
, Gary Klein b, Roger Alan Pick
c
a College of Administration and Business, Louisiana Tech UniÕersity, P.O. Box 10318, Ruston, LA 71272-0046, USA School of Business, The UniÕersity of Texas at Permian Basin, 4901 E. UniÕersity BlÕd., Odessa, TX 79762-0001, USA Bloch School of Business and Public Administration, UniÕersity of Missouri-Kansas City, 5110 Cherry Street, Kansas City, MO 64110-2499, USA b
c
Accepted 10 August 1998
Abstract Point-of-sale scanner data provides a unique opportunity for analyzing consumer package goods ŽCPG. trends and patterns. Decisions of ever-increasing complexity are made possible by the amount of data available. Unfortunately, the analysis of such voluminous data requires complex techniques and processing requirements not available to many marketing decision makers. In this paper, we describe a prototype system which allows users to manage the complex models and scanner data to make forecasts in an interactive fashion. A limited test of the prototype allowed users not familiar with the underlying models to develop product forecasts. q 1998 Elsevier Science B.V. All rights reserved. Keywords: Decision support systems; Marketing management; Scanner data; BVAR forecasts
1. Introduction Marketing decision-making in the consumer packaged goods ŽCPG. arena benefits from advances in the collection and compilation of electronic scanner data w2,4,15x. The key problem facing today’s CPG brand managers is not lack of data but a lack of systems that transform voluminous scanner data into decisions of strategic advantage w2,5,15,16x. For example, without new system assistance, the five staff-days spent analyzing bimonthly store audit data would increase to 5000 staff-days to analyze weekly-level scanner data w17x. )
Corresponding author. Tel: q1-318-257-3445; fax: q1-318257-4253; e-mail: [email protected]
A review of the literature reveals that marketing models with scanner data appear unfamiliar to decision support systems ŽDSS. researchers w1,3, 12,18,25,26x. Consequently, problems in scannerbased system design receive little attention in the DSS literature. In addition, full scale applications require large capital investments in hardware and personnel. For this reason, current commercial systems solve isolated marketing problems, for example those focusing on pricing, feature and display effects w5,26x. To help overcome these deficiencies, we propose a marketing category management system that permits CPG brand managers to exert frequent control on in-store pricing and promotion variables as well as on out of store variables such as advertising. This
0167-9236r98r$ - see front matter q 1998 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 - 9 2 3 6 Ž 9 8 . 0 0 0 5 3 - 0
260
J.J. Jiang et al.r Decision Support Systems 23 (1998) 259–271
is termed a ‘level-two’ approach to solve today’s marketing scanner-data dilemma—too much data with too few marketing analysts. At level two, the system controls the marketing-mix variables in a given product category. This expands the more common ‘level-one’ approach of summary reports and exception reporting w2,19x. We will begin with an introduction to singlesource systems, those systems containing multiple measurements of a single source. A later section introduces the prototype implementation in detail. Bayesian vector autoregression ŽBVAR. is the modeling approach on which the prototype system is based. An evaluation of the system’s ability to support user requests follows the system description. We will conclude by summarizing the study and describing future research directions. 2. The data: single-source systems A single-source system is a database containing multiple measurements on single units of analysis, such as stores or households. The multiple measurements may include television viewing, advertising, in-store promotions, direct mail coupons, and scanned grocery purchases. The increasing popularity of single-source data is underscored by the fact that CPG firms spend about 1.3 billion dollars every year to purchase data from the two major data suppliers, Information Resources ŽIRI. and A.C. Nielsen. Single-source data is collected at various stages in the product flow. Shipment data is collected on items from the factory to the warehouse, withdrawal data between the warehouse and retailer, consumer data at take-away time, and data about promotional activity. Five primary databases on which single-source data are accumulated: Ž1. a household database with consumer data; Ž2. a store database with sales and local
promotions; Ž3. a retail factors database with pricing, display and features; Ž4. a promotion factors database with coupons and sweepstakes; Ž5. and an advertising database with active TV, print, and radio data. The power of a single-source system comes from a complex set of interactions among these five. Put simply, the system tracks what products are sold Žthe trade environment.; who bought these products Žthe consumer environment.; and why these products were bought Žthe promotion environment. w5x. Table 1 presents an example framework which could represent a single-source dataset as a four dimensional Cartesian product. That is, a singlesource dataset ŽC. s Geographic level ŽG. = Product ŽP. = Time ŽT. = Measurements ŽM.. For example, one Cartesian product could be C1 s Cincinnati metro-market area: at store level for Smith Groceries4 = All packaged beer products4 = Weekly data from 1995–1996 . = Õolume, price, promotion, and display4 . One can see how all the information is captured and how extensive the data quickly becomes. IRI and Neilsen invest tremendous amounts of time and money to develop their single-source data management systems. Although an abundance of data sounds wonderful, the amount is usually too large for effective use. For example, for every single numeric value stored in a research database in 1979, at least 1420 were stored in 1989 w10,15x. Currently, about two gigabytes of numeric values enter into IRI’s single-source systems every week w21x. The challenge facing the CPG industry is to efficiently and effectively analyze scanner data. In other words, in today’s competitive environment, CPG managers need automated systems to help analyze these data in support of decisions w15,20x. Little w14,15x argues that brand managers should make their decisions through model-based DSSs to
Table 1 Single-source dataset Ž1. Geographic Ž2. Product Ž3. Time Ž4. Measurements
ŽG.: store, cluster of stores, metro-market, and national, etc. Žindividual Conditional forecast aggregate at geographic level. ŽP.: sku, brand, etc. Žindividual vs. aggregate at product category level. ŽT.: week, 2 weeks, month, and quarter, etc. Žindividual vs. aggregate at time horizon. ŽM.: price, volume, feature, and display, etc.
J.J. Jiang et al.r Decision Support Systems 23 (1998) 259–271
261
effectively use single-source data. His reasons are: Ž1. Brand managers’ information needs have shifted from traditional marketing status reports Ži.e., what are sales, price, and total revenue?. to marketing response reports Ži.e., what is price elasticity, advertising response, promotional effectiveness.. However, marketing response reports require complex models. Ž2. Marketing response output can help brand managers understand competitive interactions in a given product category. However, many existing models are hard to use, therefore, rarely applied in the marketing industry. Ž3. There is a shortage of qualified marketing analysts in industry to help brand managers construct and use ‘good’ marketing models. Curry w5x also argues that a model-based DSS offers bright long term prospects. His reasons include: Ž1. Contrary to the situation 10 years ago, managers are now accustomed to using models for specific problems; e.g., forecasting, product positioning, pricing. Ž2. Dozens of models have been developed for key decision areas. The main challenge today is to use existing models productively rather than to develop new models. Ž3. Marketing planning typically involves multiple parts, each of which makes use of at least one model. Ž4. Models can find more consistent optimal solutions to problems than can expert systems.
critical to brand management. Previous studies have found BVAR to be an effective forecasting model. The use of BVAR in this precise context has been studied by Curry et al. w6,8x, Curry and Mathew w7x, and Whiteman et al. w24x. Their conclusions state
3. The model: Bayesian vector autoregression
Table 2 Theil values for BVAR, VAR and GARCH models
In order to choose a model for this system, we wanted to pick a model which matches the CPG situation. The CPG situation involves a large volume of data. Furthermore, that data involves numerous variables which interact with each other, and the data is a collection of time series. Marketers in the CPG arena, whether manufacturers or retailers, need to be able to make both conditional Ž‘What If?’. and unconditional forecasts that account for competitive interactions among brands. The BVAR class of model meets all of these needs w6–8,24x. In addition, the BVAR has capabilities and accuracy compared with other popular techniques. BVAR is able to provide forecasts of multiple data series where marketing activities of all players can be considered. Such a vector forecasting technique is
Using POS scanner data, we establish that BVAR is a superior forecasting tool compared to Exponential Smoothing, univariate ARIMA, Box–Jenkins transfer function models, and MARMA. Because BVAR uses few degrees of freedom and is easy to identify, it satisfies the practical requirements of category management. Finally, using impulse response functions and conditional forecasts, we illustrate that BVAR provides important insights for the category manager. ww6x, page 197x Other forecasting techniques capable of handling the data include the vector autoregression models ŽVAR. and the generalized autoregressive conditional heteroskedasticity ŽGARCH. models. Table 2 is a comparison of the three methods for the dataset of this study. As can be seen, the BVAR outperforms these two techniques not previously considered. The results suggest that over-parameterization could be a problem for both the VAR and GARCH models. Especially since a large number of lags are the normal situation in the brand management applications.
Lag 2
Lag 3
Lag 4
BVAR Brand 1 Brand 2 Brand 3 Brand 4
6.01 3.71 7.17 3.49
6.67 4.11 7.08 3.09
8.67 6.91 6.36 3.39
VAR Brand 1 Brand 2 Brand 3 Brand 4
11.73 10.94 8.29 8.27
12.14 11.56 8.02 9.67
13.25 13.33 7.87 8.52
GARCH Brand 1 Brand 2 Brand 3 Brand 4
5.66 2.44 4.41 4.74
126.87 234.61 156.39 149.57
1345.87 2652.93 1623.99 1769.45
262
J.J. Jiang et al.r Decision Support Systems 23 (1998) 259–271
BVAR forecasts movements in an n-dimensional state vector. Mathematically, vector autoregression can be written: x Ž w q l . s A x Ž w . q Dm Ž w q l . q e Ž w q l . where; x Ž w . is an N = 1 state vector for time period w, A is an N = N state transition matrix, N s Ž n = l . q 1, n s number of time series variables in the system, l s number of lags, D is an N = q coefficient matrix, mŽ w q 1. is a q = 1 vector of merchandising variables for time period w q 1, e Ž w q 1. is an N = 1 white noise vector for time period w q 1, and w is an index for time period. Conceptually, the matrix A represents the ‘natural laws of motion’ influencing a category’s transition from one state to the next, and the matrix D captures the effects of merchandising variables. The state consists of variables which are out of the modeler’s direct control such as volume sales for each UPC and price and merchandising activities for other brands. The merchandising variables include items that the modeler can control in a deterministic way such as displays, features, coupon drops, and ownbrand prices. The system’s dataset consists of n endogenous variables and q exogenous variables. All the relations in the model are linear. There is a total of N = q free coefficients in the model w9x. Since the N = q free coefficients are estimated using data, forecasts using unrestricted vector autoregression often suffer from over-parameterization of the model and, consequently, large out-of-sample forecast error. The Bayesian approach to estimation specifies ‘fuzzy’ restrictions on the coefficients rather than sharply including or dropping a given regressor w13x. Without reviewing the ‘fuzzy’ restrictions in detail, we outline the assumptions and principles of the Bayesian approach to VAR. ŽFor a detailed review see Refs. w6,13x or Ref. w22x.. Bayesian VAR uses Normal prior distributions with means of zero and small standard deviations for long lags w9x. This allows the system to estimate the coefficients using Theil’s mixed estimation technique w23x. The estimation process chooses a set of ‘hyperparameters’ for the prior distribution in the system. Identifying the ‘best’ BVAR model includes the following difficulties: Ža. determining potential
variables, Žb. placing prior distributions on coefficients, and Žc. the lack of clear guidance to evaluate the tremendous number of potential coefficients. The system we propose eliminates many of the complex choices inherent in BVAR modeling. The prototype helps a user select variables by efficiently partitioning them into time series and deterministic variables. The system also helps simplify identification of the prior distribution by reducing the required number of hyperparameter specifications. Finally, the system helps a user digest the huge volume of estimation results generated from the existing system to find a prior distribution and set of variables that maximizes the model’s out of sample forecast accuracy. This model-building activity is the first part of our DSS, which we call the model fitting subsystem. Using the model fitting system is a decision-making activity in which the decision-maker’s goal is to create the best possible model. The system assists in this by allowing the decision-maker to create a sequence of alternative models and examine their forecasting performance. We will go into this system in greater detail in Section 4. The second part of the DSS is the model application subsystem. This subsystem uses the model built by the model selection subsystem. The user of this subsystem could be, but need not be, the same as the user of the model selection subsystem. The model application subsystem is the part of the DSS that actually assists in solving a business problem. Without any further modification, the model will answer unconditional questions such as: ‘What are expected baseline volumes, by week, for the next eight weeks? What are expected total revenues and market shares during the same time period?’ The model can also produce conditional forecasts to answer ‘what-if’ questions. For example, ‘If we lower the price of a given SKU by 10% two weeks in a row, what will our sales, market share, and total revenues be for each week for the 6 weeks that follow?’ The BVAR model application subsystem to answers these types of questions by generating forecasts conditioned upon fixed values for certain variables. The objectives of the system, therefore, are to aid marketing analysts in generating high-quality forecasting models and to help brand managers generate and evaluate marketing strategies and policies. The
J.J. Jiang et al.r Decision Support Systems 23 (1998) 259–271
BVAR model-fitting and model-application sub-systems in the integrated DSS facilitate these services to the users.
4. A category management system 4.1. The conceptual model Fig. 1 shows a conceptual model of the category management system. A defining element of a DSS is that the functionality derives from the interaction between the computer and decision maker. This particular system is designed for two different types of users: retailing managers and marketing analysts. A marketing analyst’s responsibility is to use data and analytical models to provide information for retailing managers’ decision needs. The model then provides forecasts for the model-application subsystem. Category managers use the model-application system to set up their marketing tactics, such as price-setting, featuring, and merchandising. The system allows them to input their tactics and make a forecast conditioned upon these specifications. Category managers can try a number of possibilities until they find a marketing plan with satisfactory forecast results.
263
The system supports brand managers as well as category managers. A brand manager for a manufacturer can only affect the marketing mix of the UPCs under hisrher control. Their usage of the system would generally only be conditioned upon the smaller number of variables that they control, leaving the other variables outside their control to be forecast. If they wish to anticipate the impact of some action by the competition, this system will allow them to make forecasts conditioned upon that action. The dataset used in the prototype system was sponsored by a major grocer and was collected by IRI. At the request of the sponsors, data was aggregated and the category disguised. This dataset retains 2 years of sales histories Že.g., price, and volume., consolidated by 3 SKUs across all stores in a major U.S. metropolitan area, aggregated on a weekly basis. It also includes in-store and out-of-store causal data Že.g., display, feature, advertising.. In general, given the large number of items in a typical CPG category, item aggregation is almost always necessary w5,11x. Variables collected by IRI can be aggregated to the managers’ desired level of inquiry. Three logical candidates for aggregation are single store, store type, and metro-market w11x. We use data aggregated to the market level as requested by the sponsoring company. However, it is possible to implement the BVAR model using pooled store and individual store level data. 4.2. Model-fitting subsystem and interfaces
Fig. 1. Conceptual model of the category management system.
Fig. 2 shows the conceptual structure of the model-fitting subsystem. The following activities are controlled by the model-fitting subsystem: 1. Model specification: specifying the BVAR model. 2. Model generation: formatting user input for solution. 3. Model execution: controlling the actual running of the model. 4. Model output generation: generating the user reports. 5. Model storage: saving the calibrated category model into the model base. The latter four functions are relatively straightforward involving common practices in model management. The first function, model specification, involves complex interaction with the marketing ana-
264
J.J. Jiang et al.r Decision Support Systems 23 (1998) 259–271
4.2.1. Stage 1: specify time-series Õariables In this stage, a user must select the forecast variables for a particular product category. The user can also update the existing variables in a category before parameters are estimated by the model-fitting system. The process of selecting or updating variables is interactive, and driven by menus and question-answering. When specifying a structure for time-series variables, the user first selects time-series variables to be forecast by the system. ŽSee Table 3.. Then, for each continuous variable the user enters the number of time lagged values to include or allow a default value. Finally, the user specifies deterministic variables using a structure similar to that of the forecast variables. For each question, the system provides context-sensitive help messages. The fitting system also allows the user to update part of the specification without revising the entire model. Fig. 2. Conceptual structure of the model-fitting system.
lyst. To illustrate the model generation process, Fig. 3 shows the proposed BVAR model creation decision process. Three major stages can be identified to construct a ‘best’ BVAR model for a particular product category.
4.2.2. Stage 2: specifying a prior distribution In this stage, the system requires input for four values: mean, weight, tightness, and decay. For greater support, each question has its own help message to guide users who are not familiar with these terms ŽAppendix A.. Furthermore, the system checks the specification for each parameter and automatically replaces any values that are out-of-range with default values. The user can review the existing specifications at any time and update these values on the screen.
Table 3 Time series variable selection Stage ŽI.: Question 1—selecting time series variables S S S S S S S
Fig. 3. The proposed BVA model-fitting decision process.
TSUN1: Brand 1’s ŽScotties. PRCD1: Brand 1’s ŽScotties. TCOM1: Brand 1’s ŽScotties. TSUN2: Brand 2’s ŽKleenex. PRCD2: Brand 2’s ŽKleenex. TCOM2: Brand 2’s ŽKleenex. TSUN3: Brand 3’s ŽPuffs. PRCD3: Brand 3’s ŽPuffs. TCOM3: Brand 3’s ŽPuffs. HELP: Help screen!!!
Total volume sales Store price on deal Total TV commercial time Total volume sales Store price on deal Total TV commercial time Total volume sales Store price on deal Total TV commercial time
Cursor ´ Move UprDn; Enter ´Select. S indicates a selected variable.
J.J. Jiang et al.r Decision Support Systems 23 (1998) 259–271
265
Table 4 In-sample fit report—Report I: Summary report of system equations Equation no.
Dependent variables
Total observations
R Unadjusted
Durbin Watson
Q statistic
Adjusted
Degree of freedom
Significance level
1 2 3 4 5 6 7
TSUN1 PRCD1 PRCN1 PRCD2 PRCN2 PRCD3 PRCN3
112 112 112 112 112 112 112
0.79 0.65 0.77 0.67 0.78 0.62 0.79
0.74 0.61 0.74 0.64 0.75 0.58 0.74
2.13 2.14 2.20 2.05 2.06 1.95 1.93
39.32 49.96 20.16 20.03 25.67 19.92 34.61
104 104 104 104 104 104 104
0.12 0.01 0.91 0.92 0.69 0.92 0.26
The higher the value of R, the better the model fits the data ‘in-sample.’ The closer the Durbin Watson statistics is to 2, the better are forecasts. High significance for the Q-statistic indicates excessive residual autocorrelation.
4.2.3. Stage 3 (IteratiÕe improÕements): find candidate solutions When the user decides that srhe wants a solution, the model generation system generates a BVAR model in Regression Analysis Time Series’ ŽRATS. format. Then the model execution system uses RATS time series modules to compute the results. RATS produces voluminous output; more than 100 pages
for a category with only four products. The output generation system scans the output and produces a concise report for the user. Tables 4–6 show example reports of the in-sample fit, model structure, and out-of-sample forecasting ability of a user’s specified model. The model structure diagnosis report in Table 5 shows the relationships in the current model and allows the user to judge the model against experi-
Table 5 Model structure diagnosis report—Report II: Model structure diagnosis Part ŽI.: T-test a Variableb
Lag
Parameter
Standard error
T statistic
TSUN1 TSUN1 PRCD1 PRCD2 FEAT1 SUMMER FALL
1 4 1 2 0 0 0
0.329 0.160 0.534 y0.161 0.010 0.159 0.237
0.09 0.07 0.08 0.07 0.01 0.03 0.08
3.42 2.13 6.31 y2.28 2.15 2.97 3.21
Independent variable
F-test
Significance level
TSUN1 ŽScotties. Žall previous weeks. PRCD1 ŽScotties. Žall previous weeks. PRCD2 ŽKleenex. Žall previous weeks.
3.25 3.87 3.11
Part ŽII.: F-test c
a
0.02 0.01 0.05
Equation no: 1; dependent variable: TSUN1 ŽScotties.; significant Ž t G 2.00. individual influence on TSUN1. In your model, the dependent variable ŽTSUN1. is highly related to the above variableŽs.. c Dependent variable: TSUN1 ŽScotties. Žcurrent week.; influence total effects are significant Ž F G 3.0.. The F-test summarizes the T-tests shown in the previous section. ŽThe T-tests are ‘week specific’ while an F-test integrates over all weeks included in a model.. b
J.J. Jiang et al.r Decision Support Systems 23 (1998) 259–271
266
Table 6 Output for Theil statistics—Report III: Out of sample forecasts Weeks
Theil
Attention!
1 2 3 4 5 6 7 8 8 weeks Theil totals
1.02 0.87 0.74 0.66 0.32 0.21 0.19 0.15 4.16
X
Theil statistics for dependent variable: TSUN1 ŽScotties.. Theil-1: Your model outperforms the Naive Žbaseline model. for this forecast period. Theil)1: Your model fails to outperform the Naive model for this forecast period.
ence and intuition. Once the user is satisfied with the specified model, the estimated model is stored in the model base for the model-application system. If the user is not satisfied with the model, srhe can revisit the variables andror prior distribution. Model construction is an iterative process until the user is satisfied with the model’s out-of-sample forecast performance. Model generation, model computing, output generation, and model storage are transparent to the user.
3. Model execution: forecasting the specified marketing conditions. 4. Output generation: Generating the reports to users 5. Scenario history maintenance: store the 16 most recent scenarios for later reference. In the dialogue manager of Fig. 4, a brand manager specifies the marketing scenario that srhe wants to forecast. For example, if our competitors lower the price of their product 10% 2 weeks in a row, what will our sales, market share, and total revenues be for each week in the 8 weeks that follow? The user is allowed to specify any scenario by setting prices, features and displays for one or more brands in a category. The requirements of specifying a marketing scenario are shown in Table 7. The user provides input via a form on screen. Any invalid inputs, such as typing a character instead of numeric key for a number are rejected by the system. In addition, the user can review previously attempted scenarios managed by the scenario history management system. After the user has chosen a marketing scenario, the system generates a conditional forecast model based on the model developed. The conditional forecast model generation is controlled by the model generation system. The model execution system uses
4.3. Model-application subsystem and interface Today, success in the PG business boils down to who can best predict the future w5,17x. Brand managers want to know what is going to happen to their business if they change their prices, promotional strategy or other elements of the marketing mix, either nationwide or market by market. The modelapplication system provides this kind of predictive ability. The model-application system transports the abstract model developed by the model-fitting system to the practical world of retailing or manufacturing. Fig. 4 shows the conceptual structure of the prototype model-application system. The following activities are conducted by the system: 1. Marketing scenario specification: marketing conditions specified by users. 2. Model generation: format BVAR model for execution.
Fig. 4. Conceptual structure of the model-application system.
J.J. Jiang et al.r Decision Support Systems 23 (1998) 259–271 Table 7 Marketing scenario input Conditional marketing scenario Week 1 Week 2
Base period ŽDate: 3r10r97.
Price 1 Price 2 Price 3 Adv 1 Adv 2 Adv 3 M1 M2 M3
0.49 1.09 1.19 155 17 35 ON OFF ON
0.69 1.09 1.29 10 17 100 OFF OFF ON
0.69 1.09 1.39 10 25 150 OFF OFF ON
267
dures are transparent to a user. Table 8 shows the sample 8-week forecast for one brand Žeach brand in a category has a similar report.. Table 9 illustrates how the system summarizes sales volume. A similar report is generated for total revenues.
5. Evaluation
Brand 1: Scotties. Brand 2: Kleenex. Brand 3: Puffs.
RATS to compute the conditional forecast results. An output generation system is used to digest the huge amount of printed output generated by RATS, and isolate certain statistics useful to the managers, such as market share. These algorithms and proce-
Can marketing managers use the prototype system effectively? Twenty-one marketing managers were asked to use the prototype system to construct a BVAR model for a particular product category. All twenty-one successfully constructed the BVAR model. The mean quality of the specified BVAR model was 4.03 with a variance of 0.33. Model quality is measured by the out-of-sample forecasting ability of a user’s model Theil statistics. A Theil less than 8 indicates that a model performs better than a naive model defined as a function of predicted and actual values w23x. Smaller Theil values mean better forecasts.
Table 8 Output for single brand forecast—Brand name: Brand 1 ŽScotties. Week 1
Week 2
Week 3
Week 4
Baseline forecast Volume Price Total revenue Market share
228.57 0.50 114.38 69
237.30 0.50 117.71 68
238.86 0.50 118.51 69
237.96 0.49 117.43 69
Conditional forecast Volume Price Total revenue Market share
168.45 0.69 116.23 63
187.83 0.69 129.61 66
233.87 0.61 142.04 70
240.86 0.56 135.29 71
Week 5
Week 6
Week 7
Week 8
Total
Baseline forecast Volume Price Total revenue Market share
236.56 0.49 115.83 69
255.31 0.47 119.96 70
264.38 0.46 120.98 70
268.35 0.45 121.03 70
1967.5 0.49 945.83 69
Conditional forecast Volume Price Total revenue Market share
233.19 0.54 125.37 71
250.16 0.57 126.73 71
257.83 0.49 125.95 71
260.98 0.48 125.09 70
1833.5 0.58 1026.3 70
J.J. Jiang et al.r Decision Support Systems 23 (1998) 259–271
268
Table 9 Sales volume summary report for the entire category Week
1
2
3
4
5
6
7
8
8 weeks total
Brand 1 2 3
Ž97:5:18. 168.5 73.3 22.0
Ž97:5:25. 187.8 73.4 20.6
Ž97:6:1. 233.9 75.7 20.7
Ž97:6:1. 240.9 75.1 20.5
Ž97:6:15. 233.2 73.4 19.8
Ž97:6:22. 250.2 79.0 22.0
Ž97:6:29. 257.8 82.3 23.4
Ž97:7:5. 261.0 84.3 24.0
1833.2 616.5 172.9
Sales volume conditional forecasts ŽUnit: 1000..
The managers were able to use the system after less than an hour of training. None of them were statisticians; they could not have constructed, used, or interpreted BVAR models using RATS alone without this DSS to assist them. Managers were asked to measure the ease of use of the prototype system and their confidence in the constructed model. The mean responses were 8.24 out of 10 Ž10— extremely easy to use. and 8.14 out of 10 Žextremely confident., respectively. Subjects with the aid of the proposed system not only successfully constructed a BVAR model but also felt that the system is quite easy to use and are confident that they built a good model. It is fair to say that system usage leads to successful model construction; each subject was successful. Stronger evidence of the system’s role in the process would be provided by comparing these results to cases where no support system were available or where some alternative systems were used. However, it is an extremely difficult task to find subjects who are familiar with BVAR modeling and are qualified to be subjects in such an experiment. Subjects in the present experiment were asked, ‘Can you write a BVAR model and run it in RATS environment?’ The answer was always ‘no.’ 6. Conclusions and future research Checkout scanners generate a tremendous volume of marketing data for firms in the consumer packaged goods industry. These data create a new opportunity for brand managers to better understand their customers, to make better forecasts, and to better plan their tactics. However, the overwhelming amount of scanner data in conjunction with the complexity of marketing environments turns the CPG-brand manager’s dream into a nightmare. To
overcome this nightmare, researchers argue the use of model-based DSS to automate data analysis. We report on a prototype Category Management DSS that introduces the use of BVAR model to practitioners. The contributions of this system include the following. Ž1. A system that is designed for category management. Current scanner-based DSSs can only either solve isolated marketing problems Ži.e., pricing. or one brand in a given category at a time w5,26x. Ž2. The system simplifies the usage of an econometric BVAR for scanner datasets. The model can digest the volume of scanner data and is able to effectively capture all marketing-mix variables in a product category. The prototype system simplifies the procedure of constructing a BVAR model and provides a user-friendly interface. Ž3. Instead of adopting a traditional approach, the prototype system separated model management into two subsystems, modeling fitting and model application. This allows the DSS to be interactive to marketing analysts and marketing brand managers, separately. This allows brand managers, instead of asking them to construct analytical models, to focus on their business problems. Marketing analysts can use the model-fitting subsystem to automate model creation. Ž4. The prototype system demonstrates an effective way to integrate an advanced RATS package into the marketing DSS. By implementing an output generating subsystem in model-fitting and model-applications subsystems, the prototype DSS reports only significant and relevant information to the users. The traditional volume of output generated by advanced time-series statistical systems are not presented to the users. In order to completely automate the BVAR model fitting stage, future research on BVAR model fitting
J.J. Jiang et al.r Decision Support Systems 23 (1998) 259–271
heuristics is encouraged. Instead of playing ‘what if’ marketing scenarios, a brand manager may seek an ‘optimal control’ solution that generates ‘next week’s’ marketing mix as a closed-loop feedback function of ‘this week’s’ market results. Optimal marketing algorithms based on BVAR modeling represent a promising direction for future research. The automation of other applications using BVAR, such as pricing and advertising strategies, provides challenges for DSS researchers w7,18,24x. Another promising direction for future research would be to include more complex interactions among brands. For example, we could assume that other brand managers would have this tool or a similar one available to them. Then they would presumably run this same system to make forecasts conditioned upon their own actions and choose the best action that they are able to find. A better system would take this kind of activity into account and searching for a game-theoretic minimax solution. IRI has developed a very complex single-source database management system for the marketing industry. However, an easy-to-query database interface should be designed in a DSS to provide the managers a way to specify a subset for their needs Že.g., a particular set of stores, products, or time periods.. The proposed single-source dataset framework of Table 1 could be used as the basis for designing such an interface to retrieve the data. Acknowledgements The authors would like to thank the companies of Information Resources ŽIRI. for their support of this research through the Integrated Research Center at the University of Cincinnati. Dr. Curry’s ŽUniversity of Cincinnati. comments on this project is highly appreciated. Dr. Doan’s ŽEstima. Regression Analysis of Time Series software grant is also appreciated. Appendix A. Help screen for prior specification phase Ž1. MEAN: w0.1 to 1.0x governs a variable’s own first-week lag in each equation. Setting MEAN at 1.0 centers the prior distribution on a model that assumes this week’s value will be identical to last week’s value except for random movements.
269
The MEAN index controls how much weight a variable’s own one-week lag has on its current level. ŽFor example, how much do last week’s sales influence this week’s sales.. A MEAN value of 0.0 indicates no CARRYOVER effect from last week while a value of 1.0 means that Žon average. last week’s level DETERMINES this weeks level. Remember that no matter what value you specify, the data can override your judgement. However, by setting MEAN at a low value Ž0.1 to 0.4. you are effectively demanding that carry-over effects—revealed by the data—be very large before they can impact results. Normally, MEAN is set to 1.0 because carry-over effects are typically important for most time series. Ž2. OTHER’S WEIGHT: w0.1 to 1.0x controls how much weight one variable Žsay price. may have when affecting another variable Žsay sales.. Note that the MEAN index controls a variable’s own first lag. Its value is typically higher than the values for WEIGHT which controls the impact of all other variables’ first lags. If WEIGHTs 0.0 your model is effectively a set of univariate autoregressions. That is, each variable is being predicted only its own past values. Setting WEIGHTs 0.0, therefore, removes competitive interactions and marketing mix effects from the model. Ž3. TIGHTNESS: w0.1 to 2.0x directly controls the standard deviation of your prior, that is, how much weight is given to each variable’s own lags other than week one. Setting TIGHTNESS at a very small value, say 0.01 virtually eliminates the effects of 2nd, 3rd, and higher order lags even if you have specified such lags in your model. Normal values for this parameters are in the range 0.1 to 0.4. Generally values in the range 0.8 to 1.2 are considered ‘loose’ and permit the data to override your judgement. Ž4. DECAY: w0.1 to 1.0x summarizes how quickly carry-over effects Žfrom one week to the next. dissipate. The higher the value the more quickly effects dissipate; e.g., the less successive weight put on each lag as you move back in time: last week, 2 weeks ago, 3 weeks ago, etc. Normal values for this index are in the range w0.1 to 0.2x. The system default is 0.2.
270
J.J. Jiang et al.r Decision Support Systems 23 (1998) 259–271
References w19x w1x M.M. Abraham, L.M. Lodish, An implemented system for improving promotion productivity using store scanner data, Marketing Science 12 Ž1993. 248–269. w2x R. Blattberg, Learning How the Market Works, Marketing Science Institute Conference, Cambridge, MA, Sep. 21–22, 1989. w3x R. Blattberg, R. Briesch, E.J. Fox, How promotions work, Marketing Science 14 Ž1995. 122–132. w4x D. Curry, Single-source systems: retail management present and future, Journal of Retailing 65 Ž1989. 1–20. w5x D. Curry, The New Marketing Research Systems, Wiley, New York, 1993. w6x D. Curry, S. Divakar, S. Mathur, C. Whiteman, BVAR as a category management tool: an illustration and comparison with alternative techniques, Journal of Forecasting 14 Ž1995. 181–199. w7x D. Curry, G. Mathew, Optimal demand-side retail pricing for EDLP conditions, Working paper, the Center of Integrated Research Systems, The University of Cincinnati, April, 1997. w8x D. Curry, G. Mathew, N.T. Bruvold, Store level scanner data and category management: modelling and estimation techniques, Working paper, The Center of Integrated Research Systems, The University of Cincinnati, July, 1996. w9x T. Doan, R.S. Litterman, C.A. Sims, Forecasting and conditional projection using realistic prior distribution, Econometric Review 3 Ž1984. 1–100. w10x G. Eskin, Single Source Data: The U.S. Experience, presented to The Special Joint ARFrMRS Research Leaders Seminar, Boston, MA, July 24, 1989. w11x E.W. Foekens, P.S. Leeflang, P.R. Wittink, A comparison and an exploration of the forecasting accuracy of nonlinear models at different levels of aggregation, Working Paper, Department of Economics, University of Groningen, Groningen, Netherlands, July, 1993. w12x P.M. Guadagni, J.D. Little, A logit model of brand choice calibrated on scanner data, Marketing Science 3 Ž1983. 203– 238. w13x R.S. Litterman, A Bayesian procedure for forecasting with vector autoregressions, Working paper, Massachusetts Institute of Technology, Department of Economics, Boston, MA, 1980. w14x J.D.C. Little, Decision support systems for marketing managers, Journal of Marketing 43 Ž1979. 9–26. w15x J.D.C. Little, New Opportunities in a Changing World—The 1990 Philip McCord Morse Lecture, ORSArTIMS Joint National Meeting, Philadelphia, PA, Oct 29, 1990. w16x J. McCann, Overview of marketing system past, present, and future, Marketing Science Institute Conference, Cambridge, MA, Sep 21–22, 1989. w17x J. McCann, J.P. Gallagher, Expert Systems for Scanner Data Environments: The Marketing Workbench Laboratory Experience, Kluwer Academic Publishers, 1989. w18x J.H. Pedrick, F.S. Zufryden, Evaluating the impact of advertising media plans: a model of consumer purchase dynamics
w20x w21x w22x w23x w24x
w25x
w26x
using single-source data, Marketing Science 10 Ž1991. 111– 130. B. Peters, ‘Brave new world’ of single source information, Marketing Research 2 Ž1990. 13–21. J.B. Rochester, D.P. Douglass, Building marketing information systems, IrS Analyzer 28 Ž1990. . J. Schmitz, G. Armstrong, J. Little, Coverstory—automated news finding in marketing, Interfaces 20 Ž1990. 29–38. C.A. Sims, Macroeconomics and reality, Econometrica 48 Ž1980. 1–48. H. Theil, Principles of Econometrics, Wiley, New York, 1971. C.H. Whiteman, C. Curry, S. Divakar, S. Mathur, Dynamically optimal pricing strategies for consumer packaged goods, Working paper, the Center of Integrated Research Systems, The University of Cincinnati, May, 1996. D.R. Wittink, J.C. Poter, S. Gupta, Dangers in using marketing-level data for determining promotion effects, Marketing Science Institute Report No.93-115, 1–40. M.J. Zenor, The profit benefits of category management, Journal of Marketing Research 31 Ž1994. 202–213.
Dr. James J. Jiang is an Associate Professor of Computer Information Systems at the College of Administration and Business, Louisiana Tech University. His PhD in Information Systems was awarded by the University of Cincinnati in 1992. He earned his MBA in Management Science and MS in Applied Mathematics degrees from Wright State University in 1988 and 1989, respectively. His research interests include system development and implementation and marketing modeling-based systems. He has published more than 50 journal articles in these areas. He is a member of ACM, IEEE, and DSI. Dr. Gary Klein is Couger Chair of Information Systems at the University of Colorado in Colorado Springs. He obtained his PhD in Management Science at Purdue University. Before that time, he served with Arthur Anderson in Kansas City and was director of the Information Systems department for a regional financial institution. He was previously on the faculty at the University of Arizona, Southern Methodist University and Louisiana Tech University. His specialties include information system development and mathematical modeling with 50 publications in these areas. In addition to being an active participant in international conferences, he has made professional presentations on Decision Support Systems in the US and Japan where he once served as a guest professor to Kwansei Gakuin University.
J.J. Jiang et al.r Decision Support Systems 23 (1998) 259–271 Roger Alan Pick is an Associate Professor of Management Information Systems at the Henry W. Bloch School of Business and Public Administration at the University of Missouri-Kansas City. He joined the faculty at the Bloch School in 1993. Besides UMKC, Dr. Pick has taught at Purdue University, the University of Wisconsin-Madison, the University of Cincinnati, and Louisiana Tech Unversity. His PhD in Management Science Žmajor in Systems Analysis and
271
Computer Science, minor in Applied Economics. was awarded by Purdue University in 1984. His MS Žfrom Purdue. and his BS Žfrom Oklahoma University. degrees are in mathematics. Pick is a member of the ACM, DSI, IEEE and INFORMS. Dr. Pick’s research interests are in model management, decision support systems, marketing information systems, and in the economics of information technology. His research on these topics has resulted in scholarly articles appearing in Management Science, Communication of the ACM, Journal of Management Information Systems, Expert Systems, and other outlets. For more information, see his homepage at http:rrcctr.umkc.eduruserrrpickr