Modelling the advertising-sales relationship through use of multiple time series techniques
✍ Scribed by Joseph F. Heyse; William W. S. Wei
- Publisher
- John Wiley and Sons
- Year
- 1985
- Tongue
- English
- Weight
- 983 KB
- Volume
- 4
- Category
- Article
- ISSN
- 0277-6693
No coin nor oath required. For personal study only.
✦ Synopsis
When time series data are available for both advertising and sales, it may be worth while to model the two series jointly. Such an analysis may contribute to our understanding of the dynamic relationships among the series and may improve the accuracy of forecasts. Multiple time series techniques are applied to the well-known Lydia Pinkham data to illustrate their use in modelling the advertising-sales relationship. In analysing the Lydia Pinkham data the need for a joint model is established and a bivariate model is identified, estimated and checked. Its forecasting properties are discussed and compared to other time series approaches. KEY WORDS Distributed lag model Feedback systems Multiple time series Transfer functions Vector ARMA process It has been demonstrated that a strong relationship between sales and present and past advertising exists [see Clarke (1976) for a relevant survey]
. Such lagged advertising effects on sales may result from delayed response to the marketing effort, or to a hold-over of new customer demand or to an increase in demand from existing customers. In addition, advertising may have a cumulative effect on demand.
A possible link between present advertising and past sales has also been recognized, and is thoroughly discussed in the literature [for example, see Schmalensee (1972)l. Advertising budgets are often set as a percentage of sales, and the possibility also exists for less direct and therefore less obvious policy decisions to affect advertising budgets.
A relationship between sales and present and past advertising is seen as a n effect in one direction (advertising affects or leads sales). A relationship between advertising and past sales is considered to be an effect in an opposite direction (sales affect or lead advertising). Much of the effort in modelling the advertising-sales relationship considers the effect to exist in only one direction. However, by modelling these effects jointly, one can determine the type of dynamic relationship between advertising and sales. We assume that the data for advertising and sales exist at a series of equal time intervals. They may be contemporaneously related, or one series may lead the other, or
The Gunning Fog Index for this paper is about 16.