Churn & Retention Research

Reduce churn 15-30% with root-cause intelligence

Teams that run AI-moderated churn interviews achieve 15-30% higher retention because they surface the real reasons customers leave — emotional narratives, trust breaks, value erosion — that NPS scores and exit surveys systematically miss. 72 hours. Intelligence that compounds.

15-30% retention lift
72-hour insights
Studies from $200
Intelligence Report Live
0% Retention
Onboarding
76%
Support
58%
Features
42%
AI Insight

Early-stage churn driven primarily by onboarding friction, not feature gaps...

User Intuition
Benchmark
85%
Live
98% customer satisfactionSub-72-hour turnaround guaranteed4M+ global panel (B2C + B2B)ISO 27001 / GDPR / HIPAA compliantCompounding Intelligence Hub
TL;DR

Churn intelligence that compounds means deeply understanding why customers leave through 30+ minute AI-moderated conversations that uncover emotional drivers, trust breaks, and value perception shifts — not just surface reasons like 'price' or 'features.' You identify patterns across cohorts, track drivers quarterly, and build institutional retention knowledge that prevents the same churn reasons from recurring. The 10th study is dramatically more valuable than the first because the Intelligence Hub surfaces cross-study patterns automatically.

The Problem

Exit surveys capture symptoms,
not the emotional narrative

You have NPS, exit surveys, and churn prediction tools. Yet you still don't know why customers are actually leaving — and you keep losing them for the same reasons.

01

Exit Surveys Miss the Real Story

5–15% response rates. Customers pick 'price' or 'features' but those are symptoms. The real reason: they felt unheard 6 months ago, their champion left, or your product stopped fitting their evolving workflow.

02

NPS Doesn't Prevent Churn

Your NPS might be 45 (healthy) while you lose 8% annually. Satisfaction ≠ retention. Medallia and Qualtrics measure sentiment but can't probe into the 'why behind the why.'

03

Prediction Tells You WHO, Not WHY

ChurnZero and Gainsight flag at-risk customers but can't explain why engagement is declining. You're guessing at what intervention will work.

04

Internal CS Teams Lack Scale and Objectivity

Your CSMs might call 10 churned customers. But they're biased (defending their work), inconsistent (different questions), and lack time to interview 50+ customers per quarter.

05

Traditional Research Firms Take 6-8 Weeks

By the time you get insights, 50 more customers have churned for the same reason. You need answers in 72 hours, not 2 months.

06

Insights Don't Compound

Even one good churn study becomes a static PDF. You can't see if 'onboarding friction' is trending up, whether your fix worked, or which segments are most affected.

Use Cases

Real-world applications
for churn & retention research

Post-Cancellation Deep Dive

Interview 30 churned customers to understand the trajectory from satisfied to departure. Identify tipping points, trust breaks, and what intervention could have saved them.

Fix root causes, not symptoms

Proactive At-Risk Cohort Research

Your prediction tool flags 200 at-risk accounts. Interview 50 to understand what's driving hesitation. Test retention offers to see what would make them stay.

Save customers before they churn

Annual Contract Renewal Intelligence

90 days before renewals, interview customers considering alternatives. Understand what would make them renew vs. switch. Build targeted retention campaigns.

Increase renewal rates 20-30%

Competitive Defection Analysis

If customers are switching to competitors, understand why. Identify which competitors are winning, what they offer, and what gaps you need to close.

Win back lost customers

Longitudinal Churn Driver Tracking

Run quarterly 30-customer studies. Track if 'onboarding friction' is decreasing post-fix. Prove ROI of retention investments. Spot emerging drivers before they scale.

Compounding retention intelligence

Segment-Specific Churn Diagnosis

Enterprise customers churn for different reasons than SMB. Product-led signups differ from sales-led. Segment studies reveal which interventions work where.

Targeted retention playbooks
Compare

User Intuition vs.
traditional churn & retention research

Dimension User Intuition NPS Tools / Exit Surveys / Manual Interviews
Response Rate 90%+ scientifically recruited 5–15% (exit surveys) or 10-20 (manual)
Interview Depth 30+ min · 5–7 layers of why 5-min survey or variable (interviewer-dependent)
Emotional Drivers Naturally surface through laddering Rarely captured (NPS) or inconsistent (manual)
Turnaround 72 hours Instant (NPS) or 6-8 weeks (research firms)
Cost per Interview From $13 per deep conversation $50–200 (manual) or free but shallow (surveys)
Actionability Specific interventions with customer quotes Generic categories (Price, Features, Support)
Cross-Study Patterns Intelligence Hub surfaces automatically Manual synthesis or none
Scalability Interview 200+ customers in 72 hours CS teams cap at 10-20/quarter; surveys lack depth
Bias Low · consistent AI methodology High (CS defending work) or variable (interviewers)
Key Output WHY customers leave + what would keep them Satisfaction scores (NPS) or surface reasons (surveys)
How It Works

From question to brand intelligence

1
15 min

Scope Study

Define cohort (churned, at-risk, retained) and key questions

2
24–48 hrs

Recruit

Fast recruitment from 4M+ B2C/B2B panel or your customer list

3
30+ min

AI Interviews

Laddering questions surface emotional narratives and root causes

4
Auto

Synthesize

Theme identification, segment comparison, driver ranking

5
72 hrs

Report

Themed findings, customer quotes, retention playbook

6
Quarterly

Compound

Track driver trends, validate intervention impact, build knowledge base

"We thought price was our churn problem. User Intuition found it was actually onboarding — customers felt abandoned after signing. We rebuilt onboarding, and churn dropped 18% in 6 months. That's $720K in retained ARR from a $2,000 study. Now we run quarterly research to track progress."

Joel M., CEO — Abacus Wealth Partners

Methodology & Trust

When AI Helps and When a Human Should Lead Churn Research

AI-moderated interviews deliver honest churn feedback at scale — but some retention conversations need human sensitivity.

AI-Moderated Interviews Excel At

  • Structured exit and at-risk customer interviews at scale
  • Consistent methodology for quarterly churn driver tracking
  • Segment-level churn comparison (enterprise vs. SMB, vertical, etc.)
  • Competitive switching trigger analysis across 50+ customers
  • Reducing social desirability bias — customers are more honest with AI
  • 24/7 availability matching churned customer schedules

Consider Human Moderation For

  • High-value enterprise account save conversations requiring personal rapport
  • Emotionally charged service failure debriefs needing empathy
  • Relationship recovery conversations where diplomacy matters
  • Complex multi-stakeholder interviews (CFO + CTO + Ops)
  • Sensitive topics where customers need to vent to a person
  • Strategic executive churn interviews requiring executive presence

Methodology refined through Fortune 500 consulting engagements.

Get Started

Churn intelligence that
compounds quarterly

In 72 hours, understand why customers really leave. Every quarter, track whether your interventions are working. Build retention knowledge that prevents the same churn reasons from recurring.

5-Min Review

See how AI-moderated churn interviews surface root causes exit surveys miss. Real customer quotes, themed findings, retention playbook.

Pilot Scoping

We'll help you design a pilot with your at-risk accounts. Prove value before committing to a continuous program.

No contract · No retainers · Studies from $200

FAQ

Common questions

Churn analysis research is the practice of deeply understanding why customers leave through 30+ minute AI-moderated conversations. Unlike exit surveys (5–15% response, surface reasons) or NPS tools (satisfaction scores without context), churn analysis uncovers emotional drivers, trust breaks, and value perception shifts through 5–7 levels of laddering questions.
Exit surveys get 5–15% response rates with surface-level multiple-choice reasons ('price,' 'features,' 'support'). AI-moderated interviews achieve 90%+ response rates, 30+ minute depth, and 5–7 layers of 'why' — uncovering emotional narratives like 'I felt unheard,' 'my champion left,' or 'the product stopped fitting my evolving workflow.' Exit surveys capture symptoms. AI interviews surface root causes.
Same week. Recruitment takes 24–48 hours from our 4M+ B2C/B2B panel or your customer list. Interviews happen within 48 hours of recruitment. Full report delivered 72 hours from study launch. You can understand churn drivers this week and act immediately.
Yes — this is one of our strongest use cases. Interview at-risk customers flagged by your prediction tool (ChurnZero, Gainsight) to understand what's causing hesitation, test retention offers, and intervene before they actually leave. Proactive retention beats reactive firefighting.
Studies start at $200. A foundational 15-customer study costs ~$200–400. Comprehensive 50-customer segment analysis costs ~$650–1,000. Compare to traditional research firms charging $1,500–$2,000 per interview ($75K for 50 interviews). Most teams see 50–120x ROI from recovered ARR.
Teams typically achieve 15–30% retention improvement by fixing root causes. If you have $20M ARR and 8% churn ($1.6M lost annually), a 15% improvement recovers $240K. Even conservative 5% improvement = $80K recovered. A $2,000 study that recovers $240K = 120x ROI.
Your CSMs lack scale (10-20 calls/quarter max), objectivity (they're defending their work), and consistency (different questions per customer). User Intuition interviews 200+ customers in 72 hours with consistent AI methodology, 90%+ response rates, and zero bias. Your CS team focuses on saving customers; we focus on understanding why they leave.
NPS measures satisfaction scores but can't probe into the 'why behind the why.' You might have NPS 45 (healthy) while losing 8% annually. Medallia and Qualtrics are great for sentiment tracking but terrible for root-cause diagnosis. Use them together: NPS flags declining sentiment, User Intuition explains why.
No. VPs of Customer Success, Directors of Retention, Product Managers, and COOs all run churn research independently. You define the questions, we execute the research. No PhD required.
Run studies quarterly with identical methodology. The Intelligence Hub automatically compares against previous quarters, showing which drivers are trending up/down, whether interventions worked, and emerging patterns. The 10th study is dramatically more valuable than the first because the system surfaces cross-study insights automatically.
Yes. We provide segment exports and API access. Map churn drivers to customer segments, build retention playbooks in your CS platform, and trigger interventions based on identified risk factors. Works with all major CS platforms.
Best practice. Interview churned customers to understand what went wrong. Interview retained customers to understand what's working. The comparison reveals which factors predict retention vs. which are merely correlated. Most teams run mixed cohort studies.