A practical, clean-at-source workflow that links IDs, qualitative context, and live reporting so you can act before churn happens.
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01
Define scope, outcomes, and your churn period
Start with a precise outcome question (e.g., “Reduce 90-day voluntary churn by 20%”). Choose a consistent analysis window (monthly or quarterly) and align all inputs to it. Identify leading indicators you can influence—onboarding completion, first value achieved, support friction, or sentiment shifts. Document inclusion rules for who “counts” (e.g., paid users only; trials excluded). This clarity drives which data you collect, how you model change, and what “success” means.
Example setup
Outcome: Lower 90-day churn for SMB plan.
Window: Monthly review; 12-month lookback.
Signals: Setup completion, first-feature use, negative sentiment in tickets, billing confusion keywords.
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02
Create Contacts and unique IDs in Sopact Sense
In Contacts, register each customer (or account) once to generate a unique ID. This ID becomes the backbone for every survey response, document, or telemetry snapshot. Use field validation for emails, names, and account metadata to prevent typos. Unique invite links let customers correct their own records later—clean data and GDPR rectification in one move.
Tip: Mirror your CRM’s primary key as a reference field for painless syncing.
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03
Design surveys with validation and longitudinal pairing
Build short, lifecycle-aligned forms (onboarding pulse, mid-cycle health, pre-renewal check-in). Use required fields and numeric ranges for quant questions, and add open text for context. Keep question wording consistent across waves so each response pairs cleanly to the same ID over time. This pairing raises statistical power even with smaller samples.
Field examples
Quant: “Setup completion %”, “Time-to-first-value (days)”, “CSAT 1–5”.
Qual: “What almost made you cancel this month? Why?”
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04
Establish Relationships to link forms with Contacts
Use Relationship to bind each form to the Contacts object. This eliminates duplicates, enables corrections via the same unique link, and keeps pre/mid/post responses stitched to one profile. It’s the core mechanic that makes churn analysis longitudinal, not snapshot-based.
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05
Import light telemetry and support snapshots
Bring in essential product and support signals keyed by the same ID: last login, feature flags, onboarding steps, number of tickets, and tagged themes (billing, setup, performance). You can start with CSV or API; the goal is a single pane where usage and voice-of-customer sit side by side.
Keep it minimal initially—add fields as patterns emerge to avoid bloat.
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06
Configure Intelligent Cell to structure open text
For each key comment field or uploaded document, add an Intelligent Cell to extract sentiment, risk themes, or rubric scores (e.g., “Billing confusion”, “Unclear value”, “Blocked by SSO”). Store outputs in adjacent columns so audits stay transparent—original text and AI result live together.
Prompt skeleton
Task: Tag themes (billing, setup, performance) & sentiment. Constraints: Use only this response. Output: JSON with tags + confidence.
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07
Use Intelligent Column to test churn drivers
Select your candidate variables—setup completion, first-feature use, ticket themes, sentiment, plan type—and ask Intelligent Column to analyze relationships with churn status. You’ll get a plain-English readout plus comparative tables that reveal which combinations most strongly explain cancellations, by segment.
Analysis example
Ask: “Compare churn vs. setup% and ‘billing’ tag; include uplift estimates and key quotes.”
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08
Build live, shareable reports with Intelligent Grid
Open Intelligent Grid, paste a prompt that defines your narrative (Executive Summary → Drivers → At-Risk Segments → Actions), and generate a designer-quality report. Save and share the live link—reports auto-refresh as new data arrives, so you never rebuild slides for the same story.
Include “mobile responsive” and “use callouts & chips” in the prompt for clean visual output.
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09
Operationalize playbooks with measurable triggers
Convert patterns into interventions: define a trigger, owner, SLA, and target metric. Example—“If setup% < 60% and sentiment negative, schedule a 30-minute concierge call within 48h.” Track lift via simple holdouts or time-boxed A/B and visualize impact in the same Grid link.
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10
Measure ROI credibly and iterate
Tie retention wins to revenue-at-risk saved and margin. Use matched cohorts or lightweight randomization when feasible. Refresh your driver set quarterly—drop weak signals, add emerging ones. Because data is clean and linked by ID, iteration is fast and cumulative instead of starting over.
ROI snapshot
Metric: Δ churn rate by segment × average MRR × margin.
Narrative: “Concierge setup reduced SMB 90-day churn from 6.2% → 4.7% (+1.5 pts).”
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11
Governance, privacy, and auditability
Keep IDs pseudonymized in analysis tables and store direct identifiers separately. Use role-based access and retention windows. Since Intelligent Cell outputs sit next to the originals, re-runs and audits are straightforward if criteria evolve. Compliance and analytical rigor reinforce each other here.
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12
Scale to new segments and seasons
Clone the flow for adjacent products, geographies, or seasonal cycles. Because the architecture is ID-first and prompts are plain English, extending the model is a configuration task—not a new BI project. Your churn intelligence becomes a living system across the business.
Sopact Sense — Step-by-Step Guide for Customer Churn Analysis
A practical, clean-at-source workflow that links IDs, qualitative context, and live reporting so you can act before churn happens.
Start with a precise outcome question (e.g., “Reduce 90-day voluntary churn by 20%”). Choose a consistent analysis window (monthly or quarterly) and align all inputs to it. Identify leading indicators you can influence—onboarding completion, first value achieved, support friction, or sentiment shifts. Document inclusion rules for who “counts” (e.g., paid users only; trials excluded). This clarity drives which data you collect, how you model change, and what “success” means.
In Contacts, register each customer (or account) once to generate a unique ID. This ID becomes the backbone for every survey response, document, or telemetry snapshot. Use field validation for emails, names, and account metadata to prevent typos. Unique invite links let customers correct their own records later—clean data and GDPR rectification in one move.
Tip: Mirror your CRM’s primary key as a reference field for painless syncing.
Build short, lifecycle-aligned forms (onboarding pulse, mid-cycle health, pre-renewal check-in). Use required fields and numeric ranges for quant questions, and add open text for context. Keep question wording consistent across waves so each response pairs cleanly to the same ID over time. This pairing raises statistical power even with smaller samples.
Use Relationship to bind each form to the Contacts object. This eliminates duplicates, enables corrections via the same unique link, and keeps pre/mid/post responses stitched to one profile. It’s the core mechanic that makes churn analysis longitudinal, not snapshot-based.
Bring in essential product and support signals keyed by the same ID: last login, feature flags, onboarding steps, number of tickets, and tagged themes (billing, setup, performance). You can start with CSV or API; the goal is a single pane where usage and voice-of-customer sit side by side.
Keep it minimal initially—add fields as patterns emerge to avoid bloat.
For each key comment field or uploaded document, add an Intelligent Cell to extract sentiment, risk themes, or rubric scores (e.g., “Billing confusion”, “Unclear value”, “Blocked by SSO”). Store outputs in adjacent columns so audits stay transparent—original text and AI result live together.
Task: Tag themes (billing, setup, performance) & sentiment. Constraints: Use only this response. Output: JSON with tags + confidence.Select your candidate variables—setup completion, first-feature use, ticket themes, sentiment, plan type—and ask Intelligent Column to analyze relationships with churn status. You’ll get a plain-English readout plus comparative tables that reveal which combinations most strongly explain cancellations, by segment.
Open Intelligent Grid, paste a prompt that defines your narrative (Executive Summary → Drivers → At-Risk Segments → Actions), and generate a designer-quality report. Save and share the live link—reports auto-refresh as new data arrives, so you never rebuild slides for the same story.
Include “mobile responsive” and “use callouts & chips” in the prompt for clean visual output.
Convert patterns into interventions: define a trigger, owner, SLA, and target metric. Example—“If setup% < 60% and sentiment negative, schedule a 30-minute concierge call within 48h.” Track lift via simple holdouts or time-boxed A/B and visualize impact in the same Grid link.
Tie retention wins to revenue-at-risk saved and margin. Use matched cohorts or lightweight randomization when feasible. Refresh your driver set quarterly—drop weak signals, add emerging ones. Because data is clean and linked by ID, iteration is fast and cumulative instead of starting over.
Keep IDs pseudonymized in analysis tables and store direct identifiers separately. Use role-based access and retention windows. Since Intelligent Cell outputs sit next to the originals, re-runs and audits are straightforward if criteria evolve. Compliance and analytical rigor reinforce each other here.
Clone the flow for adjacent products, geographies, or seasonal cycles. Because the architecture is ID-first and prompts are plain English, extending the model is a configuration task—not a new BI project. Your churn intelligence becomes a living system across the business.