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

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issues in customer satisfaction research

Issues in Customer Satisfaction Research

Ed Blair

University of Houston

outline
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
satisfaction with what why do we care
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
so satisfaction with what
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.?
sampling issues
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?
measurement issues
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.
issues in using satisfaction research to guide product improvement or product development
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."
issues in using satisfaction research to evalute or improve operations
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.
issues in using satisfaction research to evalute or improve operations1
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: …
psychological factors that influence perceived quality and satisfaction
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.
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