IDEAS home Printed from https://ideas.repec.org/a/eme/ejmbep/ejmbe-01-2018-0005.html
   My bibliography  Save this article

Analysis of the behaviour of the clients assisted and sales variables in the different phases of the product life cycle

Author

Listed:
  • Aida Galiano
  • Vicente Rodríguez
  • Manuela Saco

Abstract

Purpose - The Bass model was created to analyse the product life cycle (PLC) in order to help sales and marketing departments in their business decision making. The purpose of this paper is to analyse the diferences between the clients assisted and sales variables, to discover which of the two variables is the more useful for the estimation of the PLC phases through the Bass model, thus aiding the managers of company sales and marketing departments. Design/methodology/approach - In this research, the authors analysed the 223,577 clients assisted by a nationwide network of car dealerships, who acquired 36,819 vehicles, during a 24-month period. In the analysis, the Bass model was applied to define the PLC phases; and nonlinear regression models were used to carry out the estimations. Findings - The results show that more consistent estimates of the PLC phases are obtained from the clients assisted variable. This work has theoretical and practical implications that can help business management. Research limitations/implications - The most remarkable thing about this research is that we have shown that the functionality of the clients assisted variable is greater than the sales variable for the Bass model and, therefore, for PLC estimation. Practical implications - The results of this research are very useful, since they allow marketing decision makers to obtain more consistent estimations of the PLC phases using the Bass model and the clients assisted variable. This is based on the fact that the use of this variable helps to detect if there is any deficiency in the design of the marketing strategy when the client does not make the purchase. Social implications - The data on clients assisted are as easily available to companies as sales data. However, the use of this variable improves PLC analysis and this allows an improvement in company forecasting. Thus, making the clients assisted variable a tool to strategically plan investments in innovation and marketing would reduce uncertainty in business management. Originality/value - The purpose of this paper is to analyse the diferences between the clients assisted and sales variables, to discover which of the two variables is the more useful for the estimation of the PLC phases through the Bass model, thus aiding the managers of company sales and marketing departments.

Suggested Citation

  • Aida Galiano & Vicente Rodríguez & Manuela Saco, 2018. "Analysis of the behaviour of the clients assisted and sales variables in the different phases of the product life cycle," European Journal of Management and Business Economics, Emerald Group Publishing Limited, vol. 27(3), pages 266-284, March.
  • Handle: RePEc:eme:ejmbep:ejmbe-01-2018-0005
    DOI: 10.1108/EJMBE-01-2018-0005
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/EJMBE-01-2018-0005/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: no

    File URL: https://www.emerald.com/insight/content/doi/10.1108/EJMBE-01-2018-0005/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: no

    File URL: https://libkey.io/10.1108/EJMBE-01-2018-0005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    Consumer behaviour; Time series; Automotive; Bass model; Life product cycle; M31; C22; C25;
    All these keywords.

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eme:ejmbep:ejmbe-01-2018-0005. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Emerald Support (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.