Practice Prize Article-CHAN4CAST: A Multichannel, Multiregion Sales Forecasting Model and Decision Support System for Consumer Packaged Goods

Authors:
Suresh Divakar;Brian T. Ratchford;Venkatesh Shankar
Affiliations:
Citigroup, New York, New York 10022;R. H. Smith School of Business, University of Maryland, College Park, Maryland 20742-1815;Mays Business School, Texas A&M University, College Station, Texas 77843-4117
Venue:
Marketing Science
Year:
2005

Citing 14
Cited 7

Revisiting DSS implementation research: a meta-analysis of the literature and suggestions for researchers

MIS Quarterly
Improving Decision Making by Means of a Marketing Decision Support System

Management Science
Logit Demand Estimation Under Competitive Pricing Behavior: An Equilibrium Framework

Management Science
Commercial Use of Upc Scanner Data: Industry and Academic Perspectives

Marketing Science
The Success of Marketing Management Support Systems

Marketing Science
The Decomposition of Promotional Response: An Empirical Generalization

Marketing Science
An Empirical Analysis of Determinants of Retailer Pricing Strategy

Marketing Science
A Dynamic Changepoint Model for New Product Sales Forecasting

Marketing Science
A Choice Model for Packaged Goods: Dealing with Discrete Quantities and Quantity Discounts

Marketing Science
Multiple Discreteness and Product Differentiation: Demand for Carbonated Soft Drinks

Marketing Science
Planning Marketing-Mix Strategies in the Presence of Interaction Effects

Marketing Science
A Prelaunch Diffusion Model for Evaluating Market Defense Strategies

Marketing Science
Comments on "Models and Managers: The Concept of a Decision Calculus"

Management Science
Market Roll-Out and Retailer Adoption for New Brands

Marketing Science

EditorialAre Consumers Rational? Experimental Evidence?

Marketing Science
A consumer support architecture for enhancing customer relationships

WSEAS Transactions on Information Science and Applications
An architecture for consumer support systems

SEPADS'09 Proceedings of the 8th WSEAS International Conference on Software engineering, parallel and distributed systems
An ontology-based architecture for consumer support systems

WSEAS Transactions on Information Science and Applications
Identifying the underlying structure of demand during promotions: A structural equation modelling approach

Expert Systems with Applications: An International Journal
An EOQ model of homogeneous products while demand is salesmen's initiatives and stock sensitive

Computers & Mathematics with Applications
Effective Marketing Science Applications: Insights from the ISMS-MSI Practice Prize Finalist Papers and Projects

Marketing Science

Quantified Score

Hi-index	0.00

Visualization

Abstract

We discuss the development and implementation of CHAN4CAST, a sales forecasting model, by pack size, category, channel, region, customer account and a Web-based decision support system (DSS) for consumer packaged goods. In addition to capturing the effects of such variables as past sales, trend, own and competitor prices and promotional variables, and seasonality, the model accounts for the effects of temperature, significant holidays, new product introductions, trading day corrections, and adjustments to the wholesale level. In general, the model forecasts sales volume satisfactorily for a leading consumer packaged goods company. The DSS enables top- and mid-level executives in sales, marketing, strategic planning, and finance to develop accurate forecasts of sales volume, plan prices, and promotional activities over a long time horizon; to track sales response to marketing actions over time; and to simulate forecast scenarios based on possible marketing decisions and other variables. CHAN4CAST is being rolled out for more users and more divisions in the company. The key take-aways are that successful development and implementation of a rigorous marketing science model require a strong internal champion, a careful balance between modeling sophistication and practical relevance, good diagnostic features, regular validations, and greater attention to the development of a fast and responsive DSS.