Issues in Customer Satisfaction Research

1. Issues in Customer Satisfaction Research Ed Blair University of Houston

2. Outline: • Satisfaction with what? Why do we care? • Sampling issues • Measurement issues • Issues in using the results • To improve or develop products • To evaluate or improve operations • Psychological factors in perceived quality and satisfaction

3. Satisfaction with what? Why do we care? Why do we care about customer satisfaction? • Satisfaction relates to buying: • Satisfaction and customer loyalty/defections. • Satisfaction and referrals/word of mouth. • Satisfaction relates to “market driven quality:” • At the front end, market research should be used to set the performance specifications against which quality is measured. • At the back end, customer satisfaction is the ultimate quality test, so research is needed to measure satisfaction. • Satisfaction relates to product improvement/development opportunities • Satisfaction gaps • Benchmarking

4. So… satisfaction with what? • Measuring satisfaction with the relationship vs. satisfaction with a transaction • Loyalty/defection and referrals relate more to the relationship • “I can afford to mess up, I just can’t afford to mess up the first time I do business with a customer” • Quality management relates more to the transaction • Product improvement/development opportunities relate more to the transaction • If we care about loyalty/defection or referrals, should we measure satisfaction or the direct phenomena of interest? • If we care about the transaction, what specific aspects should be measured? • Choose transactional elements based on relation to loyalty, etc.?

5. Sampling issues: • Problems can be hidden by non-response if alienated customers don't respond. • Do you want customers or dollars? "The good news is that I only have one dissatisfied customer. The bad news is that it's my largest customer." • In industrial contexts, oversample key customers and report them separately. • How often can we measure them?

6. Measurement issues: • Use measures that give room for improvement and support a call to action. • Effective satisfaction measurement is programmatic, not one-off. If the measures are near a ceiling, it is difficult to see improvements or separate them from random error. Examples: A well known hospital, a well known utility, etc. • A possible scale: Completely satisfied, mostly satisfied, somewhat satisfied, dissatisfied. • When should measures be taken? • Satisfaction can vary with time. Examples: hospitals, B & R. • The best time to measure one dimension can be different from the best time to measure another. Example: order processing, product reliability.

7. Issues in using satisfaction research to guide product improvement or product development • The people who do buy your product can't tell you why people don't buy. Example: nursing homes. • Data are always relative to the current market context. High satisfaction with current products doesn't mean that they can't be improved. Example: GTO. • Low dimensions may not be the best priorities for improvement. Example: UH. • Consider different interpretations for "satisfiers" and "dissatisfiers."

8. Issues in using satisfaction research to evalute or improve operations • Satisfaction data can be frustrating for line managers, for various reasons: • Satisfaction data can show fluctuations over time that are unexplainable from an operational perspective. • Haloing can cause confusion. Example: instructor on time. • Knowing that customers are less than completely satisfied doesn't tell you how to improve. • Different results may be typical for different business units or performance dimensions. Examples: hospital wards, hospital cities, medical care vs. billing.

9. Issues in using satisfaction research to evalute or improve operations • To keep line managers from rebelling: • Use a group approach. “Our” results, not “your” results. Example: Minnegasco. • Focus on opportunity rather than evaluation to minimize defensiveness. • Recognize that satisfaction data are soft because: • Operational improvements may not produce improved satisfaction because they become the norm. Example: pay at pump. • Satisfaction more generally is influenced by psychological factors • Satisfaction may be impacted by exogenous factors such as price. • Translate satisfaction data into an operational action plan with hard measures, and tie any incentives to that plan (rather than satisfaction). Good example: Continental. Bad examples: …

10. Psychological factors that influence perceived quality and satisfaction • Perceived quality and prior beliefs. Examples: Bud, UH. • Perceived quality and inference. Examples: chips, appliance repair. • Perceived quality and expectations. Example: pizza. • Perceived quality and situations. Examples: stockbroker, appliance repair arrival time, restaurant service time. • Perceived quality and cognitive frames. Example: equipment service. • Perceived quality and attention. Examples: elevators, airplanes, restaurants.

11. Q & A