Prioritizing Marketing Image Goals under Resource Constraints

Reading Time: 16 min 
Already a member?
Not a member?
Sign up today

5 Free Articles per month, $6.95/article thereafter. Free newsletter.


Unlimited digital content, quaterly magazine, free newsletter, entire archive.

Sign me up

Image assessment and enhancement are essential strategic management tools. Managers must be able to assess their company’s current image or reputation in the marketplace and improve it. Although it would be ideal to achieve top ratings on every attribute, such as product quality, after-sales service, and reliability, it is not realistic. Given limited resources, managers must decide which of the attributes are more important to the company’s target market and consider how difficult it would be to enhance the image on those attributes. In this paper, we propose an approach for prioritizing marketing image goals by taking into account market preferences and resource requirements. The approach is potentially applicable to small, medium, and large companies, including not-for-profit organizations. In the first half of the paper, we explain the method; in the second half, we describe how a company applied the method.

Marketing Image

The impressions, beliefs, and feelings that people have about a company constitute the firm’s image.1 These impressions may be true or false, real or imagined. If people hold incorrect negative perceptions about the company or its products, then management needs to communicate with them in order to change the incorrect perceptions. On the other hand, if the negative perceptions are accurate, then the company needs to address the actual problems.

A company has different types of images: corporate images, product images, and brand images. We focus here on marketing image, the way customers view the company’s overall marketing offer and marketing mix.2 The marketing image is broader than brand and product line images but narrower than corporate image, which also considers the company as an employer and as a citizen in terms of community involvement, concern for the environment, patronage of the arts, and the like. In the case of a large diversified corporation, marketing images will typically be different for different business units. Consequently, when we use the word “company” in this paper, we mean a corporation’s business unit.

Image is an integral part of any company. The image of a company often determines its success with various constituencies.3 Marketing image plays an important role in the attractiveness of the firm and its products and services to customers and thereby has an impact on the bottom line.

Read the Full Article


1. J.C. Bevis, “Corporate Image Studies,” in Handbook of Marketing Research, ed. R. Ferber (New York: McGraw-Hill, 1974), pp. 206–218.

2. H. Barich and P. Kotler, “A Framework for Marketing Image Management,” Sloan Management Review, Winter 1991, pp. 94–104.

3. E.R. Gray and L.R. Smeltzer, “Corporate Image — An Integral Part of Strategy,” Sloan Management Review, Summer 1985, pp. 73–78.

4. P.E. Green and V. Srinivasan, “Conjoint Analysis in Consumer Research: Issues and Outlook,” Journal of Consumer Research 5 (1978): 103–123; and

P.E. Green and V. Srinivasan, “Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice,” Journal of Marketing 54 (1990): 3–19.

5. Ibid.

6. Ibid.

7. D.R. Wittink and P. Cattin, “Commercial Use of Conjoint Analysis: An Update,” Journal of Marketing 53 (1989): 91–96.

8. Green and Srinivasan (1978).

9. The ideal-point model suggested in the conjoint analysis literature, which uses linear and quadratic terms, sometimes runs into the technical difficulty that, even though the observed part-worth values may be increasing over the levels, the fitted ideal-point model reaches a maximum value within the relevant range so that there is a decrease in utility for values beyond the maximum. This is illogical because, in our context, the attributes are such that higher values of the attribute imply greater preference. See:

Green and Srinivasan (1978) and

D. Pekelman and S.K. Sen, “Improving Prediction in Conjoint Measurement,” Journal of Marketing Research 16 (1979): 211–220.

10. In Figure 1, the shape parameter S is given by I (0.5)S = Q, so that S = [log(Q/I)]/[log 0.5].

11. For discussion of the relative merits of the conjoint and self-explicated methods, see:

Green and Srinivasan (1990).

12. Suppose the unit used in Step 5 above to obtain the resource requirement parameter R is 0.25. Then by the proportionality assumption made in Step 5, the resource required for a 0.05 improvement on attribute j is (0.05/0.25) Rj = 0.2 Rj. The constant term 0.2 does not affect the comparison across attributes and hence is not considered.

13. If the part-worth functions are concave (i.e., exhibit diminishing returns) as displayed in Figure 1, this step-by-step marginal analysis has the desirable mathematical property of producing undominated solutions. That is, at every step of the procedure, the customer value of the image is maximized subject to not exceeding the corresponding resource level. See:

B. Fox, “Discrete Optimization via Marginal Analysis,” Management Science 13 (1966): 210–216.

If the part-worth functions are not concave, the step-by-step marginal analysis needs to be replaced by dynamic programming. See:

R.E. Bellman and S.E. Dreyfus, Applied Dynamic Programming (Princeton, New Jersey: Princeton University Press, 1962).

14. P.E. Green, “On the Design of Choice Experiments Involving Multifactor Alternatives,” Journal of Consumer Research 1 (1974): 61–68.

15. V. Srinivasan and A.D. Shocker, “Estimating the Weights for Multiple Attributes in a Composite Criterion Using Pairwise Judgments,” Psychometrika 38 (1973): 473–493.

16. The resource requirement part-worth function would be scaled such that there are no incremental resources required to maintain the image at the current level on that attribute.

17. The step-by-step marginal analysis would produce undominated solutions as long as the customer attribute utility functions are concave and the resource requirement functions are convex. If the concavity-convexity assumptions are violated, then the step-by-step approach needs to be replaced by dynamic programming so as to maximize benefit for different resource levels. See:

Bellman and Dreyfus (1962).

Reprint #:


More Like This

Add a comment

You must to post a comment.

First time here? Sign up for a free account: Comment on articles and get access to many more articles.