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Customer satisfaction

As explained in Effective Data Analysis

Customer satisfaction is a customer's evaluation of how well an organization's product or service meets expectations.

Customer satisfaction is a metric capturing how well an organization’s product or service meets customer expectations. It helps teams understand customer experience and inform organizational strategy. section 7.1.1

A common CSAT measure comes from a survey question asking whether an interaction met a customer’s need. Results may be summarized as the percentage of respondents selecting “Yes” over time. section 2.1.2 section 7.1.1

Interpret customer satisfaction metrics with attention to measurement and evidence limits:

  • Check what the metric actually represents; an aggregate score may omit customer identities, visited content, and response rates. section 2.1.2
  • Treat survey results cautiously because question wording and response scales can substantially affect answers. section 2.1.2
  • Separate association from causation: a satisfaction decrease after a promotion does not establish that the promotion caused it. section 2.2.3
  • Document the rationale and scope of interpretations so stakeholders can evaluate conclusions and decisions responsibly. section 2.2.3

Help Center satisfaction survey

A Help Center pop-up asks customers, “Did this article answer your question?” The dashboard reports the percentage answering “Yes” over time, but provides no response rate or article-level detail. Further analysis therefore depends on ingesting the underlying survey data and assessing its limitations. section 2.1.2

How Effective Data Analysis covers it

How deep it goes
Defines CSAT and illustrates survey limitations, but stops short of specifying a complete measure-design procedure.
Examples
Uses a Help Center yes-or-no survey and a gym-promotion analysis involving a slight satisfaction-score decrease.
What it assumes
Assumes basic familiarity with metrics and stakeholder interpretation while teaching caution about survey data from the examples.
What you can do after
Evaluate customer satisfaction results while accounting for missing context, survey-design effects, and unsupported causal claims.
How it connects
Links customer satisfaction with customer metrics, churn rates, lifetime value, survey design, and causal interpretation.

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This explainer was generated by Manning's knowledge graph from the text of Effective Data Analysis. The section links point to the parts of the book it draws on.