The Customer Support Analysis experiment uses customer support data to identify patterns of unmet jobs, pains, and gains among users. By systematically reviewing interactions like calls, emails, and feature requests, businesses can refine their value propositions and identify actionable improvements. This experiment is particularly useful for companies with established customer bases and robust support channels.
Cost
The primary expense is time spent reviewing and analyzing existing data. No expensive tools or consultants are typically required.
Evidence
The evidence gathered is relatively weak as it represents what customers say they experience rather than what they do. It serves as a foundation for designing further, stronger experiments.
Metrics
- Frequency of recurring pain points or unmet jobs in support queries.
- Patterns in the top requested features.
Success Criteria
- Identification of at least three recurring customer pains or unmet jobs.
- Agreement within the team on actionable insights derived from the data.
Setup Time
Define questions and gather support team data within a few days.
Run Time
Once prepared, the analysis can be completed in a matter of days.
Risk Categories
Ideal for testing the...
- Ideal For: Businesses with significant existing customer bases and comprehensive support data.
- Business Model Canvas Area: Customer Segments, Value Propositions.
Setup
- Identify specific questions to address (e.g., “Are we solving for top customer jobs?”).
- Schedule review sessions with the customer support team.
- Collect relevant customer interaction data (e.g., emails, feature requests).
Run
- Discuss findings with the support team to surface recurring themes.
- Document key patterns, including job, pain, and gain insights.
- Gather direct quotes and supporting examples from support data.
Analyze
- Update the Value Proposition Canvas based on the findings.
- Prioritize actionable areas for improvement and design follow-up experiments.
- Align insights with broader business goals.
Additional Information
This method is most effective when paired with follow-up experiments to validate the observations, such as customer interviews or surveys. It works best as an initial discovery step to identify areas requiring deeper exploration.