Interpret the responses not just by average scores, but also in conjunction with customer behavior and business results.
Survey analytics allows for the evaluation of response distribution, change over time, customer segments, and open-ended feedback.
The reliability of the results should be interpreted taking into account the sample structure, response rate, question versioning, agent effect, and non-responding customers.
Fundamental analysis
Response and completion rate
Question-based distribution and averages
Customer, product, channel, and region comparisons.
Results at the representative and team level
Trends over time
Open-ended subject and emotion classes
Reasons for rejection and discontinuation
Related to commercial results
We would happily repurchase.
Needs response and proposal transformation
Product or price issues due to loss
Post-complaint resolution and customer follow-up.
Survey participation and campaign behavior.
Risk of bias and interpretation.
Responses from easily accessible or satisfied customers may not be representative of the general population. Results should be used as a management indicator, not as a definitive truth, but rather as a tool that needs to be corroborated with other operational and business data.
Closed-loop improvement
The identified issues should be assigned to the responsible process owners, and the completion of actions should be monitored to see if the impact has changed in subsequent surveys.
Use survey analytics not to generate reports, but to document problems in customer experience and measure the impact of improvements.