The B2B SaaS Retention Interview Template: 15 Questions That Surface Churn Signals 90 Days Early

The Bottom Line

This is a complete, B2B SaaS retention interview template you can publish today in Koji as an always-on study and start collecting churn signal in 24 hours. The template has 15 questions across five sections: relationship context, value realization, current usage patterns, friction and risk signals, and renewal/expansion signals. Each question is tagged with a structured type (open_ended, scale, single_choice, multiple_choice, ranking, yes_no) so the resulting report surfaces frequency-based churn risk alongside narrative quotes.

Why this specific structure matters: most B2B SaaS retention research is done too late. Customer success teams kick off a "save call" 30 days before renewal, by which point the actual churn decision was made 90+ days earlier. The template below is designed for mid-lifecycle deployment — months 4–9 of a 12-month contract — where you can still influence the outcome.

This guide covers when to deploy the template, the 15 questions verbatim, how to interpret the structured responses, and the four churn-risk signals to flag for immediate intervention.

When to Run This Study (and When Not To)

The template is designed for three deployment patterns:

1. Always-on mid-lifecycle health check. Publish as a recurring 24/7 AI-moderated interview that activates 120 days after each new account starts. Customer success uses the report as a leading indicator.
2. Quarterly retention research sprint. Run the template against 30–50 active accounts each quarter to surface aggregate themes for the CS leadership team.
3. Pre-renewal deep dive. Run a focused version against the top 20 accounts due for renewal in the next 90 days — this is the rescue mission deployment, not the prevention deployment.

Don't use this template for: exit interviews after churn (use churned customer interviews — different questions); onboarding research in the first 30 days (use customer onboarding B2B survey guide); or new-feature feedback (different intent altogether).

The 15-Question Template (Verbatim)

Copy-paste this into the AI Consultant as a starting brief, then let Koji refine and publish.

Section 1: Relationship Context (2 questions, 2–3 min)

Q1. Tell me a bit about your role and how you're using [Product Name] day-to-day. (open_ended, probe 1) Why: Confirms participant matches your power-user persona; warms up the conversation.

Q2. Roughly when did your team start using [Product Name]? (single_choice)

• < 3 months ago
• 3–6 months ago
• 6–12 months ago
• 1–2 years ago
• 2+ years ago Why: Tenure correlates strongly with retention outcomes; segments responses for analysis.

Section 2: Value Realization (4 questions, 4–6 min)

Q3. What's the single most important thing [Product Name] helps you accomplish? (open_ended, probe 2) Why: Surfaces the core "job-to-be-done" the customer is hiring you for. Different from features.

Q4. On a scale of 1–10, how much value have you gotten from [Product Name] in the last 90 days? (scale 1–10, anchor probe) Why: This is the most predictive churn signal in the template. Scores ≤6 should trigger immediate CS attention. The anchor probe asks "you said X — what would move that to a 9 or 10?"

Q5. Which of these features have actively helped your team in the last 30 days? (multiple_choice — list your 6–10 top features) Why: Maps actual usage to perceived value. The participants who can't name any features used in 30 days are silently churning.

Q6. How would you describe the moment you most recently saw [Product Name] succeed? (open_ended, probe 2) Why: Recency bias works in your favor here — participants who can vividly describe a recent win are more likely to renew. Those who can't are at risk.

Section 3: Current Usage Patterns (3 questions, 3–4 min)

Q7. How often do you personally use [Product Name] in a typical week? (single_choice)

• Multiple times daily
• Once daily
• 2–3 times weekly
• Weekly
• Less than weekly Why: Personal usage frequency is the strongest leading indicator of contract renewal at the seat level.

Q8. How many people on your team are actively using [Product Name]? (open_ended, probe 1) Why: Seat expansion vs. contraction is a hard renewal signal. Compare answers against your actual seat count.

Q9. Which of these other tools do you use alongside [Product Name]? (multiple_choice — list 8–12 known integrations and competitors) Why: Co-use of a direct competitor is the loudest pre-churn signal. Co-use of integrations indicates expansion potential.

Section 4: Friction and Risk Signals (3 questions, 3–4 min)

Q10. What's the most frustrating part of using [Product Name]? (open_ended, probe 3) Why: The most actionable question in the template. Probing depth 3 means the AI will follow up three times to extract specifics.

Q11. Have you considered switching to another tool in the last 90 days? (yes_no with conditional follow-up)

• If yes: "What triggered that consideration?" (open_ended, probe 2) Why: Direct churn-consideration signal. Even a "yes" without a follow-through is high-risk.

Q12. Rank these aspects of [Product Name] by how important they are to your team. (ranking — 6 items) Example items: speed, accuracy, integrations, ease of use, pricing, support quality. Why: Ranking forces trade-offs; reveals which features the customer values most when forced to choose. This often differs from what they'd say in an open-ended question.

Section 5: Renewal & Expansion Signals (3 questions, 2–3 min)

Q13. On a scale of 1–10, how likely are you to recommend [Product Name] to a peer at another company? (scale 1–10 — Net Promoter Score, anchor probe) Why: NPS is a coarse instrument, but at the individual level with the anchor probe ("you said X — what would change that?") it's diagnostic. See NPS follow-up interviews for the deeper framework.

Q14. If your team had to renew [Product Name] today, how would the conversation go? (open_ended, probe 3) Why: This is a near-future projection question — but it's phrased as a hypothetical conversation, not a hypothetical decision, which makes it answerable without forcing a false commitment. The most revealing question in the template.

Q15. What would have to be true for your team to expand its use of [Product Name] in the next 6 months? (open_ended, probe 2) Why: Expansion signal. The participants who can articulate concrete expansion conditions are more likely to expand; the ones who can't are at best static.

How to Interpret the Results

The report Koji generates from these 15 questions automatically surfaces five high-signal indicators:

Indicator 1: Value Score Distribution (Q4)

Look at the histogram. Healthy accounts cluster at 8+. Risk accounts cluster at 5–7. Critical accounts cluster at 1–4. A bi-modal distribution (some 9s, some 3s) is a sign of inconsistent value delivery — usually a CS coverage gap, not a product problem.

Indicator 2: Feature Usage vs. Stated Value (Q3 vs. Q5)

When Q3 (stated most-important value) doesn't map to any feature checked in Q5 (active usage in last 30 days), the customer is hiring you for something they're no longer actually using. That's a phantom-value problem and one of the highest-churn-risk signals in B2B SaaS.

Indicator 3: Switching Consideration (Q11)

Any yes here is a 90-day-risk flag. The follow-up text tells you whether it's price, feature gap, or relationship — each of which has a different CS playbook.

Indicator 4: Renewal Conversation Tone (Q14)

Koji's analysis runs sentiment + theme extraction on Q14. Negative sentiment plus specific objections ("we'd push back on the price") = high risk. Positive sentiment plus vague endorsement ("yeah, we'd probably renew") = medium risk — the vagueness is the tell.

Indicator 5: Power-User Concentration (Q7 + Q8)

If one participant says they use it multiple times daily but only 2 of 12 team members are active (per Q8), you have a hero-user dependency. Hero users leave; their replacements don't inherit the habit. Track these as expansion-and-stickiness candidates.

Setting Up the Always-On Version in Koji

1. Open Koji and start a new study.
2. Paste the 15-question template into the AI Consultant (or load it from the discussion guide generator).
3. Set the study mode to always-on — see always-on user interviews 24/7. The interview is available continuously.
4. Configure the trigger in your customer data platform: send each new account a personalized invite 120 days after activation, then again at 240 days.
5. Set up Slack alerts (see Slack research insights integration) so the CS team gets pinged when an interview returns with a Q4 score of 6 or lower or a Q11 yes.
6. Connect to your CRM so participant metadata (account size, ARR, tenure) flows into the insights chat for segmentation.

The whole setup is ~45 minutes once and the study runs perpetually.

Why This Template Outperforms a Standard Customer Health Score

Customer health scores are based on product telemetry — logins, feature usage, ticket volume. They tell you what happened, not why. The retention interview template gives you the narrative alongside the numbers — the specific frustration that's building, the alternative they're evaluating, the expansion conditions they'd need.

In practice, the two together are stronger than either alone. The telemetry tells you which accounts to interview; the interviews tell you what to do about it. Customer success teams using both can intervene 60–90 days earlier than teams using telemetry alone — and the intervention is more targeted because the customer has already articulated what they need.

Cost and Cadence

• Per-interview cost: 1 credit text / 3 credits voice (€1–€3 each on the Interviews plan)
• Quality refund: Sessions scoring 1 or 2 out of 5 are auto-refunded — see quality gate
• Recommended cadence: Trigger at days 120 and 240 for every account on annual contracts; quarterly for month-to-month accounts
• Sample size for aggregate insights: Aim for 30+ completed interviews per quarter to generate trend reporting

For a 200-account portfolio running on an annual contract, expect ~400 interviews/year for a cost of €400–1,200 — typically a tiny fraction of the ARR you preserve through earlier interventions.

Related Resources

• Structured Questions Guide — How the 6 question types in this template render in the report
• Customer Retention Research — The methodology behind retention-focused interviews
• Customer Health Score SaaS Guide — Combining telemetry with retention interviews
• Churned Customer Interviews — The complementary template for accounts that have already churned
• NPS Follow-Up Interviews — The deeper framework behind Q13
• Always-On User Interviews 24/7 — How to publish this template as a continuous study
• Slack Research Insights Integration — Get CS team alerts on high-risk responses