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The Nature and Measurement of Marketing Productivity in Consumer
Durables Industries: A Firm Level Analysis
HAWKINS, DELL I, ROGER J. BEST AND CHARLES M. LILLIS (1987), “THE NATURE AND
MEASUREMENT OF MARKETING PRODUCTIVITY IN CONSUMER DURABLES INDUSTRIES: A FIRM LEVEL ANALYSIS,” JOURNAL OF THE ACADEMY
OF MARKETING SCIENCE, 15 (4), 1-8.
Authors
Del I. Hawkins Professor of Marketing Various marketing Journals and Textbooks
Roger J. Best Associate Professor of Marketing Journals and Textbooks
Charles M. Lillis Vice President of Strategic Marketing for
US WEST Inc. Various Journals Marketing executive
Develop a managerially relevant concept of marketing productivity
Construct an operational measurement of marketing productivity-a validity issue
Establish environment specific benchmarks with which to compare the marketing productivity of various businesses
Purpose of the Article
Model Development
How the authors develop the model step by step is a good learning exercise (this is one of the reason I have included this article in the reading list) Define the variables first Explain conceptualization and limitation of variables Identify their relevance in the area Operationalize the variables-validity Testing the model with credible data Explain limitations
Model has no meaning in isolation, relative to what is the question
The Nature of Marketing Productivity
Concept of Productivity: Output/InputMarketing productivity = marketing output
divided by marketing inputMarketing Output = (Relative market
share) x (Relative Price)Marketing input= (Marketing
Expenditures) /(Sales)Percentage or ratio measures, not in
absolute dollars
Marketing Productivity Formula:
Relative Market Share x Relative Price Marketing Expenditures/Sales
» Marketing Productivity Score (MPS) – In isolation it has no meaning !
» Marketing Productivity Index (MPI)
Marketing Productivity
Sources of data: firms pay a fee to join PIMS
Self reported data, multiplied by an unknown constant when supplied to PIMS
Questionnaire is provided by PIMSOperational Definitions of Variables
(ses Table 1)
PIMS Database(now Marketing Science Institute)
Variables Influencing Marketing Productivity
Variables InfluenceRelative Product Breadth (RPB) +
Number of Competitors (NC) -
Relative Product Quality (RPQ) +
Relative Customer Size Range (RCSR) +
Served Market Growth (SMG) +
Number of Immediate Customer (NIC) -
Importance of Auxiliary Services to End User (IASE)
-
Frequency of Product Changes (FPC) -
Purchase Amount Immediate Customers (PAIC)
+
Customization (C) +
Construction of the Model
Correlation Analysis
Model Evaluation
R² is significantModel is reasonably stableDurables, nondurables, split-half…..all
supports the modelSupports the overall structure of the
model developed for the durables industries
Variables that were collinear were removed before implementing the model
Critique of the Article
Positive Contribution: First of its kind Reasonable model Credible database Coming from industry
Questionable Issues Limitation in variable definition Variability in the dataset Collinearity
Future direction
Discussion Questions
What is marketing productivity? What problems do we face in measuring productivity? How can we overcome them?
What are the drawbacks/limitations of the Hawkins et al.’s (1987) marketing productivity score/index? Can we apply the index to measure productivity in other industries? How?
Write a critique of the article.