Spring 2012: Group 2 Huy Dang Oanh Nguyen Jeffrey Gritton Liman Ha

1. The Nature and Measurement of Marketing Productivity in Consumer Durables Industries: A Firm Level Analysis Spring 2012: Group 2 Huy Dang Oanh Nguyen Jeffrey Gritton Liman Ha

2. Introduction Marketing Productivity emerged in the early 80s as a major concern of American business The purpose of this article is to develop a managerially relevant concept of marketing productivity, its operational measurement, and a procedure for establishing environment specific benchmarks to compare with various business

3. The Nature of marketing productivity • Productivity is perceived as a ratio of output from some activity to the input required by that activity. Thus, marketing productivity is: marketing output divided by marketing input. • Bucklin's definition of marketing productivity adjusted Sevin's formula for inflation over time became more adequate to the macro study but not to the firm level.

4. Marketing output • What does top management expect the marketing function to deliver? Answer: The combination of relative market share and price position. • Marketing output for an individual firm as: Marketing Output=(Relative market share) x (Relative Price)

5. Marketing input • Using absolute dollars allows one to measure the marketing output purchased with one dollar of marketing input • Using marketing cost as a percent of sales allows one to measure the marketing output purchased with one percent of sales. • Minimize the impact of inflation over time Marketing input= Marketing expenditures/sales

6. Marketing productivity Relative Share x Relative Price Marketing = Productivity Marketing expenditure/Sales

7. Marketing Productivity Score(MPS) • Steps • Marketing variables are defined • Marketing variables measured • Marketing productivity ratio measured • Marketing productivity score calculated • Comparison • Last year’s MPS • Competitor’s MPS • Standard

8. MPS (2) • Words of caution • PMS comparisons may not be “apples to apples” • PMS may give credit to marketing when credit belongs to other departments • Marketing Productivity Index (MPI) • Regression • “Predicts an average firm’s MPS given a specified operating environment.”

9. Study Data • PIMS Database • Scientifically-managed • Sensitive sales data was hidden • Some weaknesses • “The only data base in existence that allows us to develop a solution to the indexing problem.”

10. Variables Influencing Marketing Productivity • Relative Product Breadth (RPB) • Require more marketing effort • Meet more customer needs than competitors • Produce high market share • Secure high price • Allow shared costs • Number of competitors (NC) • Requires low markeitng productivity • Large NC produces unlikely market share

11. Variables Influencing Marketing Productivity (2) • Relative Customer Size Range (RCSR) • High RCSR requires more marketing effort • Allows firm to obtain relatively high market share • Allows firm to change differential price • Served Market Growth (SMG) • High SMG requires more marketing effort • Concerned more with building capacity than stimulating demand

12. Variables Influencing Marketing Productivity (3) • Number of Immediate Customers (NIC) • High NIC requires low levels of marketing productivity • Higher costs to serve large numbers of customers • Serving large NIC does not necessarily correlate to large market share • Purchase Amount Immediate Customers • Requires relatively high level of marketing productivity • Suggests few customers, less frequent purchases and indirect distribution

13. Variables Influencing Marketing Productivity (4) • Purchase amount Immediate Customers (PAIC) • Requires high level of marketing productivity • Suggests few customers, less frequent purchases and indirect distribution • Frequency of Product Changes (FPC) • Associated with low level of marketing productivity • Costly when introduced to the market • Customization • Associated with high marketing productivity • Limited number of customers

14. Model Construction • Focused on businesses that dealt in consumer durables • Limited number of firms due to capacity limitation • 135 firms total • Variables within 0.10 were retained in the model

15. Model Evaluation • 1)R²=0.43 • 2) R² for each split half remain above 0.40 level; Auxiliary services did not meet 0.10; NC,RCSR, and FPC missed the 0.10 criteria in one of the split halves but were included in the other. • 3)All of the hypothesized relationships are reflected in the overall durables market except two. Custom production in durable goods does not lead to a high level of MP; • 4) Durable goods industries characterized by frequent product changes tend to have a higher level of marketing productivity than do industries with less frequent change.

16. Model Evaluation (2) • 5)Replication of the process with 206 consumer nondurables firms in PIMS data base that were not constrained by capacity. The split half analysis produced results similar to those obtained for the durable industries model. • 6) In total, this replication in a different industry strongly supports the overall structure of the model developed for the durables industries.

17. Limitations • Expenditure data only exist only as a percent of sales in the PMS database • Absolute expenditures would have been useful in allowing more confidence in the comparison of firms with large share differentials.

18. Implications • Management should explicitly define marketing performance objectives in terms of relative price and relative share. • If all the major firms in the market are similar on the firm specific variables (RPB,RPQ, CSR and NIC), no adjustments need to be made. • If the firm is substantially above or below the level of key competitors, an analysis should be conducted .

19. Implications continued • Causes of variations can be: • 1) 1 or more uncontrollable variables not discovered in the study. • 2) Short term competitive strategies such as buying market share. • 3) Particularly effective or ineffective uses of marketing expenditures.

20. Discussion Questions • 1. What is marketing productivity? • What problems do we face in measuring productivity? • How can we avoid them? • 2. What are the drawbacks/limitations of the Hawkins et al.’s (1987) marketing productivity index? • 3. Can we apply the index to measure productivity in other industries?