Objective quality and customer satisfaction BEST CIG Berlin

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? 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 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 quality, current journey Customer journey quality expectations The customer's perception of journey quality relative to expectations + b + c Customer satisfaction, previous journeys "External noise" + a Customer satisfaction, + e + f +/– g – d

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 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. 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 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

Count.

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) Methodological aspects Data sources Instruments Scales Samples Additional forces Affective dimensions Impact of culture Media/reputation

Critical incidents

Critical incidents and satisfaction

Critial incidents, attributes and satisfaction

The Kano-model of quality attributes High Low High ”Must be” (hygene factors) ”Delights” (attractors) Traditional one- dimensional attributes Customer satisfaction Objective quality Low