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

Abstract

W e 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.

Impact of customer loyalty and service operations on customer behaviour and firm performance: empirical evidence from UK retail sector

“…The response from the survey has been used to measure the sales volume as the readiness to buy the product. This will represent purchase intention and potential sales in retail stores (Divakar et al 2005 shoppers are more than £2800. The remaining 135 respondents' income was in the range of £1601-£2800.…”

“…Some of these factors namely loyalty (store and brand), display, price sensitivity, customer service, customer behaviour and sales have been adopted from the literature (Cooper et al 1999;Divakar et al 2005;Parasuraman et al 1988). While the importance of other factors namely convenience, competitive loyalty schemes and introduction have been developed from our initial interaction with retail store managers and customers, all these measures are used further for regression analysis.…”

“…Service operations are viewed as compliment to promotional sales and loyalty cards (Seiders et al 2000). While, the direct effect of loyalty attributes are clearly evident from the literature, it is not widely discussed how service operations are indirectly influencing the customer buying behaviour (Divakar et al 2005). To unveil this point, we posit our next research hypothesis that service operations will mediate the relationship between loyalty attributes and customer behaviour.…”

“…Leading retail stores in the UK, such as Asda and Tesco, are highly competitive and trying to survive in this competitive market using different sales techniques, such as sales discounts, promotional sales, coupons, free vouchers and online offers (Cooper et al 1999;Divakar et al 2005).…”

Forecasting and operational research: a review

“…If successfully estimated, such models deliver forecasts, price and cross-price elasticities, and the problem then becomes one of developing optimal pricing and price-promotion campaigns through a 'marketing management support system'. Divakar et al (2005) provided a recent example of the soft drinks market aimed at producing 'accurate forecasts' and 'diagnostics for price and promotion planning' at product level for different distribution channels. In addition, the model-based approach was seen as overcoming the drink manufacturer's problem prior to the modelling exercise, of multiple inconsistent forecasts generated by different users.…”