conducting-customer-reference-checks
Structures customer diligence with interview guides, NPS analysis, switching cost assessment, and usage pattern evaluation. Use when conducting customer references, validating product-market fit, or assessing customer satisfaction.
Best use case
conducting-customer-reference-checks is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Structures customer diligence with interview guides, NPS analysis, switching cost assessment, and usage pattern evaluation. Use when conducting customer references, validating product-market fit, or assessing customer satisfaction.
Teams using conducting-customer-reference-checks should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/conducting-customer-reference-checks/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How conducting-customer-reference-checks Compares
| Feature / Agent | conducting-customer-reference-checks | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Structures customer diligence with interview guides, NPS analysis, switching cost assessment, and usage pattern evaluation. Use when conducting customer references, validating product-market fit, or assessing customer satisfaction.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Conducting Customer Reference Checks Structures customer diligence with interview guides, NPS analysis, switching cost assessment, and usage pattern evaluation. ## When To Use - During Series A+ diligence to validate founder claims about product-market fit, retention, and expansion - When evaluating seed-stage companies where customer count is small but reference signal is high-leverage - To pressure-test net revenue retention (NRR) narratives before committing to a term sheet - When a deal's thesis hinges on customer love, switching costs, or category creation ## Inputs To Gather - **Customer list from founder**: Full roster segmented by cohort, ACV tier, and use case — note any references the founder specifically suggested vs. those you sourced independently - **Product usage data**: Monthly/weekly active usage metrics, seat utilization rates, feature adoption curves if available from the data room - **Contract details**: ACV, contract term, renewal dates, expansion history, and any concessions (discounts, custom SLAs, free pilots) - **Competitive landscape**: Which alternatives customers evaluated or used previously — needed to frame switching cost questions - **Churn log**: Lost customers with win/loss notes; request backdoor references from at least 1-2 churned accounts ## Workflow 1. **Segment the reference pool** - Categorize customers by: ACV band, tenure (< 6 mo / 6-12 mo / 12+ mo), use case, and geography - Flag founder-provided references separately from independently sourced ones (LinkedIn, G2 reviews, conference attendee lists) - Aim for 6-10 references total; at least 30% should be independently sourced and at least 1 churned or downsell account 2. **Build the interview guide** - **Discovery block**: How they found the product, what they replaced, buying process and timeline - **Value block**: Top 3 problems solved, quantified ROI if possible, time-to-value from contract signing - **Stickiness block**: What would break if they turned it off tomorrow? Who else internally depends on it? Have they expanded seats/modules? - **NPS/satisfaction block**: Unprompted willingness to recommend (0-10), biggest complaint, feature they'd add first - **Competitive block**: Alternatives considered, why they chose this product, what would make them switch - Tailor question depth to the customer's ACV tier — enterprise references warrant 30-45 min calls; SMB references can be 15-20 min 3. **Conduct reference calls** - Open with context on your role (investor conducting diligence) and confidentiality assurance - Let the customer talk — use open-ended prompts, not leading questions - Probe for specifics: "You mentioned ROI — can you quantify that?" or "What was onboarding actually like in the first 30 days?" - Document verbatim quotes for key claims; paraphrase is insufficient for investment committee memos 4. **Assess switching costs and lock-in** - Identify integration depth (API connections, data pipelines, workflow dependencies) - Evaluate organizational embedding — how many teams/departments use it, is it in critical workflows? - Gauge contractual lock-in vs. genuine preference — multi-year contracts mask churn risk if product satisfaction is low - Score switching cost as Low / Medium / High with supporting rationale 5. **Analyze patterns across references** - Map NPS scores and note distribution (bimodal scores signal segment-specific product-market fit, not broad PMF) - Compare founder-provided vs. independent references — significant divergence is a yellow flag - Identify common objections or feature gaps cited by multiple references - Assess whether expansion revenue is pull (customer-initiated) or push (sales-driven upsell) 6. **Synthesize findings into diligence memo** ## Output The deliverable is a **Customer Reference Diligence Memo** containing: - **Executive summary**: 3-5 sentence verdict on customer quality, satisfaction depth, and switching cost profile - **Reference matrix**: Table of all references with segment, tenure, ACV, NPS score, and one-line takeaway - **Key themes**: Grouped findings on value drivers, competitive positioning, and common complaints - **NPS analysis**: Score distribution, comparison to SaaS benchmarks by stage [VERIFY against current benchmark sources], and qualitative color behind the numbers - **Switching cost assessment**: Low / Medium / High rating per customer with rationale; aggregate assessment for the portfolio - **Risk flags**: Concentration risk (top customer % of ARR), founder-reference bias, feature gaps cited by 2+ references, any signs of churn risk - **Verbatim quotes**: Curated quotes supporting key positive and negative findings — labeled by reference ID, not customer name, for confidentiality ## Quality Checks - At least 30% of references were independently sourced (not founder-provided) - Churned or downsell references were attempted — if none available, note this as a gap - NPS scores are accompanied by qualitative context, not presented as standalone metrics - Switching cost ratings are supported by specific integration or workflow evidence, not subjective impressions - Verbatim quotes are attributed by reference ID and not editorialized - Founder claims about retention, NRR, or customer love are explicitly confirmed, partially confirmed, or contradicted by reference findings - [VERIFY] Any SaaS NPS or NRR benchmarks cited are sourced and current (benchmarks shift year to year) - Memo flags where reference pool is too small or too homogeneous to draw reliable conclusions
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