The compatibility of service customers

The mere presence of customers in churches, restaurants, bars and spectacular sports is important. If no one else shows up, customers will not get to socialize with others, one of the primary expectations in these types of services. However if number of customers becomes so dense that crowding occurs, customers may also be dissatisfied.

Customers can be incompatible for many reasons –

  • Difference in beliefs
  • Values
  • Experience
  • Abilities to pay
  • Appearance
  • Age, health etc.

The service marketer must anticipate, acknowledge and deal with heterogeneous customers who have the potential to be incompatible.

Customer Expectations from Service:

Customer expectations are beliefs about service delivery that function as standards or reference point against which performance is judged. Knowing what customer expects is the first and possibly most critical step in delivering quality service.

Expected Service: – two levels of expectations

  1. Desired service: – the service customer hopes to receive – the “wished for” level of performance.
  2. Adequate service: – the level of service the customer will accept.

Customer Perceptions of Service:

Perceptions are always considered relative to expectations. Customers perceive services in terms of the quality of the service and how satisfied they are overall with their experiences.

Satisfaction is generally viewed as a broader concept while

Service quality assessment focuses on dimensions of service.

Internal and External Customer Perceptions: e.g.      A telephone repair person depends on services provided by the dispatchers vehicle maintenance crew, the repair person is the Internal Customer for the dispatchers and the vehicle maintenance crew. Any customer who calls up for the repair of his equipment is the External Customer for the service repair person.

Priya Chetty

Partner at Project Guru
Priya is a master in business administration with majors in marketing and finance. She is fluent with data modelling, time series analysis, various regression models, forecasting and interpretation of the data. She has assisted data scientists, corporates, scholars in the field of finance, banking, economics and marketing.