Order forecasts, retail sales, and the marketing mix for consumer durables

Abstract

The paper examines the problem of forecasting ongoing factory orders and monitoring retail demand, with specific reference to high‐technology consumer durables. We present evidence of the managerial importance of the problem and, using a case study of a computer peripheral manufacturer, we describe how different data sources and models can be used to increase prediction accuracy. First we examine the order placement and retail demand process using extrapolative methods that focus on identifying short‐ versus long‐run movements in orders. We then introduce marketing‐mix data for improved retail demand tracking and forecasting, and we propose the use of conjoint measurement data to simulate a product's utility over time and include that information in the demand model. Lastly, we describe the forecasting and marketing planning use of these models and discuss their implications. © 1998 John Wiley & Sons, Ltd.

Accounting for Word‐of‐Mouth Effects in Preference‐Based Market Forecasts

“…Therefore, we hypothesize that the consideration of consumers' differences in the stability of preferences can improve the accuracy of market forecasts, depending on whether consumers have already purchased the product or not. Consumers usually show a high involvement in the purchase decisions of durables in order to increase the probability of making a satisfactory purchase decision (Hanssens, ; Laurent and Kapferer, ; Zaichkovsky, ). Once they purchase and use the product, uncertainty decreases significantly.…”

Reshaping traditional marketing mix to include social media participation

“…As a consequence, the results provided insight into the successful inclusion of firm’s participation in the consumers’ online social life, by extending past studies that identified a relationships between the use of social media and business-to-consumer (B2C) strategies (Arakji and Lang, 2007; Ickler and Baumol, 2012; Pantano and Di Pietro, 2013), the building of relationships with consumers (Chan et al , 2012), the reinforcement of brand image (McCarthy et al , 2014) and firms’ profitability (Harris and Dennis, 2011; Rishika et al , 2013). Apart from the traditional variables of the marketing mix model describing the marketing strategies (Chan et al , 2012; Danaher et al , 2001; Gordon, 2012; Hanssesn, 1998; McCarthy, 1960; Naik et al , 2005), a new element concerning firms’ participation in online social life through social media might be added. This participation involves building and developing a one-to-one channel which is able to reinforce the effectiveness of advertising, through the enhancement of strong relationships with clients who could feel free to interact directly with firms to express their judgments, opinions and suggestions (either positive or negative ones), which could represent a more efficient use of social network tools.…”

“…In other words, starting from the traditional elements of marketing mix and the attempts to reinforce the marketing mix model with three additional Ps to meet also the characteristics of service industry (Constantinides, 2002; Danaher et al , 2001; Gordon, 2012; Gronroosm, 1997; Gummesson, 1999; Hanssesn, 1998; Helm and Gritsch, 2014; McCarthy, 1960; Naik et al , 2005), our model pushes towards a new extension of the model to embrace also the challenges faced in the new interactive online context.…”

Business cycle research in marketing: a review and research agenda

“…Notable exceptions are Özturan et al (2014), Srinivasan et al (2005Srinivasan et al ( , 2011 and Frösén et al (2016). Even though one could argue that clients in B2B industries may be more rational , and therefore less affected by short-term economicsentiment swings than end consumers, they may suffer from a Bbullwhip^effect, in that small BC-induced changes in demand by the end consumer get amplified as one moves further up the supply chain (Hanssens 1998;Lee et al, 1997Lee et al, , 2004. Moreover, given that the resources controlled by one firm can, directly or indirectly, depend on the resources controlled by other firms in a B2B network (Andersson and Mattsson 2010), the herding effect may also get amplified.…”