Can cause-and-effect models provide insight about the drivers of satisfaction and usage level?

Case: Performance Lawn Equipment

The worksheet Purchasing Survey in the Performance Lawn Care database provides data related to predicting the level of business (Usage Level) obtained from a third-party survey of purchasing managers of customers Performance Lawn Care.8 The seven PLE attributes rated by each respondent are

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Delivery speed—the amount of time it takes to deliver the product once an order is confirmed

Price level—the perceived level of price charged by

PLE

Price flexibility—the perceived willingness of PLE representatives to negotiate price on all types of purchases Manufacturing image—the overall image of the manufacturer

Overall service—the overall level of service necessary for maintaining a satisfactory relationship between PLE and the purchaser

Sales force image—the overall image of the PLE’s sales force

Product quality—perceived level of quality

Responses to these seven variables were obtained using a graphic rating scale, where a 10-centimeter line was

drawn between endpoints labeled “poor” and “excellent.” Respondents indicated their perceptions using a mark on the line, which was measured from the left endpoint. The result was a scale from 0 to 10 rounded to one decimal place.

Two measures were obtained that reflected the outcomes of the respondent’s purchase relationships with

PLE:

Usage level—how much of the firm’s total product is purchased from PLE, measured on a 100-point scale, ranging from 0% to 100%
Satisfaction level—how satisfied the purchaser is with past purchases from PLE, measured on the same graphic rating scale as perceptions 1 through 7 The data also include four characteristics of the responding firms:

Size of firm—size relative to others in this market

(0 = small; 1 = large)

Purchasing structure—the purchasing method used ina particular company (1 = centralized procurement, 0 = decentralized procurement)

Industry—the industry classification of the purchaser [1 = retail (resale such as Home Depot), 0 = private (nonresale, such as a landscaper)]

Buying type—a variable that has three categories (1 = new purchase, 2 = modified rebuy, 3 = straight

rebuy)

Elizabeth Burke would like to understand what she learned from these data. Apply appropriate data-mining techniques to analyze the data. For example, can PLE segment customers into groups with similar perceptions about the company? Can cause-and-effect models provide insight about the drivers of satisfaction and usage level? Summarize your results in a report to Ms. Burke.

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