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Objective quality and customer satisfaction. BEST CIG Berlin 2007-10-02. The CIG. Participants: Oslo Sporveier Helsinki City Transport Stockholm Transport SAMOT research center, Karlstad. Questions in the CIG. How is PT satisfaction related to actual PT quality?

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Presentation Transcript
the cig
The CIG
  • Participants:
    • Oslo Sporveier
    • Helsinki City Transport
    • Stockholm Transport
    • SAMOT research center, Karlstad
questions in the cig
Questions in the CIG
  • How is PT satisfaction related to actual PT quality?
    • What do people judge when they judge satisfaction?
      • What else than objective quality?
      • And how do they do it?
    • Implications for how to collect, interpret and use customer data
activities in the cig
Activities in the CIG
  • After the kick-off in Copenhagen
    • Meetings in Helsinki and Oslo
    • Discussion of practices, measurement systems and results (see BEST web page)
    • Experiences, problems and solutions
    • Begun work with a CIG report
a summary complex picture of public transport satisfaction

Objective

+

a

quality, current

+

c

journey

Customer

The customer\'s

perception of journey

+

satisfaction,

b

quality relative to

+

e

expectations

d

+

+/

Customer journey

g

f

quality expectations

Customer

satisfaction,

previous journeys

"External

noise"

A summary complex picture of public transport satisfaction
examples of insights and conclusions so far
Examples of insights and conclusions so far
  • Impact of expectations
    • History/previous experience
    • Media/reputation
  • Methods of data collection and analysis
    • Customer surveys
    • Customer complaints
  • How to interpret the result
    • And how to use them
oslo what is behind the satisfaction data
Oslo – What is behind the satisfaction data
  • Interview follow-up of onboard survey of journey satisfaction
  • The scale
    • In general: 1-2 are used when the respondent is dissatisfied, 3-5 is used when the customer is satisfied.
  • The responses: not only performance
    • Previous experience
    • Expectations
count
Count.
  • Previous experiences influences
    • This influences the answers adversely, not positively.
  • Examples:
    • Punctuality: "The tram was on time, I could have given a top score. Still, in the back of my mind were all the delays lately, so I gave a poorer score."
    • Cleanliness: "In general, the metro is always dirty, people are spitting and putting their feet on the seat. Giving a top score is impossible".
helsinki quality and compalints
Helsinki – Quality and compalints
  • Customer complaints and satisfaction
    • Computerized system for analysing customer complaints
      • Content analysis/conceptual mapping
      • Quantifications and linkage to satisfaction measures
    • Complaint data reveals the causes of (dis-)satisfaction
      • Immediate reaction of service problems
discussion issues
Discussion issues
  • Satisfaction in times of change
    • Internal/external changes
    • Positive/negative changes
  • Why satisfaction measures?
    • Quality control
    • Follow up of operators/incentives
    • Effects of improvements
    • Input to strategic decisions
discussion issues count
Discussion issues (count)
  • Methodological aspects
    • Data sources
    • Instruments
    • Scales
    • Samples
  • Additional forces
    • Affective dimensions
    • Impact of culture
    • Media/reputation
the kano model of quality attributes

Customer satisfaction

”Delights” (attractors)

High

Traditional one-dimensional attributes

Objective quality

High

Low

”Must be” (hygene factors)

Low

The Kano-model of quality attributes
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