'churn' 검색 결과

Design processes to retain customers and grow revenue from existing accounts. Use product activity and customer relationships to define risk signals, upgrade opportunities, onboarding steps, renewal schedules, and follow-up plans for previously lost sales opportunities.

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• Defining early warning signals and responses for customer churn
• Planning upgrade offers based on product usage
• Designing onboarding and renewal workflows

Diagnoses and reduces churn — cancellation flows, save offers, failed-payment recovery, at-risk detection, and the product and service causes underneath. Use this when churn is rising or unexplained, to design a cancellation or win-back flow, to recover involuntary churn, to identify at-risk accounts before they leave, or to decide whether a retention problem is a product problem.

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When the user wants to reduce churn, improve customer retention, or plan lifecycle marketing. Also use when the user mentions "retention," "churn," "customer lifecycle," "churn prevention," "at-risk customers," or "loyalty program." For lifecycle, use growth-funnel.

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Score churn risk per segment by script, with tiered retention playbooks. "which customers are about to churn"

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Post-mortem analysis when a client churns. Takes client history, engagement data, support tickets, usage logs, and exit feedback to produce a comprehensive churn autopsy with root cause classification, timeline of decline, and preventive measures.

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Identify at-risk customer accounts by analyzing usage patterns, engagement signals, and support history to generate churn risk scores and intervention recommendations. Use when the user requests churn analysis or provides relevant inputs for this workflow.

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Create or review churn prevention deliverables with audience, evidence, voice, and conversion-quality controls. Use when the user explicitly asks for churn prevention work.

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Customer retention analysis, churn prediction, cohort analysis, win-back campaigns, and loyalty program design. Use when the user asks about churn, retention, customer lifetime value, cohort analysis, or win-back strategies.

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Write churn prevention and win-back playbooks — risk intervention sequences, save conversation guides, and win-back email campaigns. Use when asked to "prevent churn", "save at-risk customers", "win back churned customers", or "build a save play".

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Analyses sales call transcripts (and churn or renewal calls) to find why deals are won, lost, kept or churned. Reads each call across six dimensions (product, messaging, sales process, pricing, competition, customer context), pulls verbatim quotes with the speaker named, counts how often each pattern appears, and writes findings with recommendations. Works with any recorder export or pasted text. Triggers: "win/loss analysis", "why do we lose", "why do we win", "analyse these sales calls", "lost deals", "churn analysis", "objection patterns". Feeds /positioning and /messaging. Not for general meeting summaries or competitor research (use /competitor-research).

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Build AI churn prediction systems. TRIGGERS - Use when user needs help with ai-churn-predictor related tasks.

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Product-led growth and customer-base growth for B2B SaaS. Use this skill for self-serve funnel work (activation, time-to-value, free trial vs freemium, PQL definition, in-product upgrade moments, sales-assist triggers), for the self-guided interactive product demo that runs in front of the login wall (demo portfolio and flow design, gated vs ungated policy, demo seed data and privacy, keeping a published demo in sync with the shipping product, step-level demo analytics), and for growing revenue from existing customers (adoption, expansion, churn-save, the self-serve cancel flow and failed-payment recovery, renewal marketing, net revenue retention, and champion tracking: watching the people who bought and used you at each customer, confirming a job change from evidence, and running both plays a move fires, the seat they left and the account they joined). Also triggers on: PLG, product-led growth, activation, aha moment, time to value, free trial, freemium, PQL, product qualified lead, onboarding, self-serve, customer marketing, expansion revenue, upsell, cross-sell, churn, retention, NRR, renewal, cancel flow, cancellation flow, save offer, exit survey, pause subscription, dunning, failed payment recovery, involuntary churn, people keep canceling, champion tracking, job change signals, job change alerts, our champion left, our champion changed jobs, customer alumni, former customers who moved to a new company, interactive demo, self-guided demo, product tour, click-through demo, demo automation, demo library, should our demo be gated, ungated demo, demo without a sales call, our demo is out of date, demo shows the old UI, put a demo on our homepage, demo completion rate, demo drop-off, try before you talk to sales.

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Build customer health scores and churn risk models using leading indicators. Define health dimensions (Usage, Engagement, Support, Sentiment, Contract), weight and score customers, segment into Healthy / At-Risk / Red, and generate intervention playbooks. Use during QBR prep, customer success reviews, or when churn spikes.

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Use when designing churn defence — health-score signals, churn-cause split (involuntary / value / relationship / fit), early-warning loop. Triggers on 'why are accounts leaving'.

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When the user wants to reduce churn or build retention systems — 'cancel flow,' 'save offer,' 'dunning,' 'failed payment recovery,' 'win-back,' 'exit survey,' 'pause subscription,' 'involuntary churn,' 'customers are leaving.' For post-cancel win-back sequences, see email-sequence. For in-app upgrade paywalls, see paywall-upgrade-cro.

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Expert en analyse du churn (cohort analysis, survival curves, predictive models, at-risk signals)

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Churn analysis v3 — cohort analysis, survival curves, predictive models, DZ context

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Churn prevention specialist — Early warning signals, retention campaigns, win-back, health scores

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Expert en prévention du churn avancé (early warning, health scores, win-back campaigns, predictive)

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Build AI churn prediction systems. TRIGGERS - Use when user needs help with ai-churn-predictor related tasks.

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Weekly churn risk detection across active client accounts. Scans for signals like decreased engagement, missed meetings, delayed payments, and competitor mentions. Scores risk 1-10 and outputs prioritized alert list.

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Develop machine learning models from local data, covering data preparation, model selection, training, evaluation, and deployment preparation. Use it to build workflows for tasks such as predicting customer churn or classifying images.

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• Building a model to predict customer churn
• Training a neural network for image classification
• Preparing missing values and categorical data for training

Route customer success, sales, revenue operations, and contract requests to one of four specialist skills. Use it when a business request is unclear, so the agent selects the relevant workflow before starting the work.

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• Choose a skill for identifying accounts at risk of churn
• Route RFP participation and proposal requests
• Find the workflow for pipeline and revenue forecasts

Add business knowledge missing from a Wren project's database definitions. Compare source documents with existing context, then add explanations, rules, SQL examples, and proposed metrics through guided questions or an automatic mode with an audit report.

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• Documenting status codes, units, and NULL meanings
• Adding business knowledge from handbooks and glossaries
• Defining aggregation metrics such as ARR or DAU