Price as an indicator of Service Quality

One of the intriguing aspects of pricing is that buyers are likely to use price as an indicator of both service costs and service quality—price is at once an attraction variable and a repellent. Customers’ use of price as an indicator of quality depends on several factors, one of which is the other information available to them.

When service cues to quality are readily accessible, when brand names provide evidence of a company’s reputation, or when level of advertising communicates the company’s belief in the brand, customers may prefer to use those cues instead of price.

In other situations, however, such as when quality is hard to detect or when quality or price varies a great deal within a class of services, consumers may believe that price is the best indicator of quality.

Many of these conditions typify situations that face consumers when purchasing services. Another factor that increases the dependence on price as a quality indicator is the risk associated with the service purchase. In high-risk situations, many of which involve credence services such as medical treatment or management consulting, the customer will look to price as a surrogate for quality.

Because customers depend on price as a cue to quality and because price sets expectations of quality, service prices must be determined carefully. In addition to chosen to cover costs or match competitors, prices must be chosen to convey appropriate quality signal.

Pricing too low can lead to inaccurate inferences about the quality of the service. Pricing too high can set expectations that may be difficult to match in service delivery.

Because goods are dominated by search properties, price is not used to judge quality as often as it is in services, where experience and credence properties dominate. Any services marketer must be aware of the signals that price conveys about its offerings.

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.

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