'churn' 검색 결과

Prepare customer interviews to investigate problems, churn, or new product ideas. Clarify research goals, participants, and access constraints, then choose a method and create a tailored plan with questions, follow-ups, bias checks, and criteria for useful findings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing interviews to validate a new product problem
• Investigating why customers leave or fail to activate
• Planning research with limited customer access and time

Evaluate a proposed pricing change by modeling revenue per customer, conversion, cancellations, and customer acquisition cost recovery. Compare scenarios and receive a recommendation to implement, test, modify, or hold the change, with supporting calculations and risks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Evaluating a price increase for new customers
• Estimating revenue from a new premium tier
• Assessing an annual prepayment discount

Run linear or logistic regression on CSV or Excel tables to examine relationships between variables. Get coefficients, measures of model fit, significance tests, and checks for overlapping predictors, with explanations in plain language.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Examining relationships between sales and numeric factors
• Analyzing factors associated with customer churn
• Checking coefficient significance and model fit

When the user wants to reduce churn, improve user engagement, or increase lifetime value. Also use when the user mentions "retention", "churn", "users leaving", "engagement", "DAU/MAU", "user activation", or "why are users uninstalling". For onboarding-specific issues, see app-launch. For monetization, see monetization-strategy.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Scan support tickets, Slack channels, NPS scores, and usage patterns to flag accounts showing early churn indicators. Produces a weekly risk scorecard with severity tiers, root cause hypotheses, and suggested save plays per account. Designed for seed/Series A teams where the founder or a single CSM manages all accounts manually.

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Playbook for designing SaaS and startup products that convert, retain, and monetize — landing pages & CRO, checkout & forms, onboarding/activation, churn reduction, pricing psychology, dashboards, behavioral-science tactics, feature discipline, positioning/ICP, go-to-market, and AI-era differentiation. Use when advising on product design, UX, conversion, landing pages, checkout, onboarding, trials, pricing, retention/churn, growth, or positioning for a startup or SaaS. Distilled from product designer Richard (@richardrx). Not for use on gambling, betting, or casino products.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Designing free-to-paid conversion flows for SaaS products. Trigger moments, paywall design, value demonstration, upsell vs downsell, win-back flows, churn prevention. Honest about paywall-everywhere (gates everything aggressively), free-forever-trap (no upgrade path surfaces), and value-triggered-upgrade (paywall surfaces at moments of demonstrated value) patterns. Triggers on upgrade flow, paywall, free-to-paid, freemium conversion, trial conversion, plan upgrade, subscription upgrade, win-back flow, churn prevention. Also triggers when free-to-paid conversion is low, when paywalls are blocking the wrong moments, or when upgrade flows are being scoped for the first time.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

This skill should be used when the user asks to "calculate MRR", "analyze churn", "compute SaaS metrics", "do cohort retention analysis", "calculate LTV or CAC", "evaluate unit economics", or "track subscription revenue growth".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Customer leadership advisor on CX strategy, retention and expansion, and voice-of-customer programs. Use when defining a CX strategy, scoring CX maturity, planning churn interventions, or designing a VoC program.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Run cohort analysis: retention matrix and LTV by cohort. Churn scoring → churn-risk. "build a retention matrix"

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

When the user needs to identify at-risk accounts, understand why customers are leaving, reduce churn rate, build health scores, design save plays, or create win-back campaigns.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Design and roll out a price increase or packaging change without triggering churn. Segments the base by price sensitivity, models revenue and churn scenarios, decides grandfathering and notice strategy, and produces the full comms kit (customer email, in-app notice, sales talk track, support FAQ, objection handling). Use when raising prices, repackaging tiers, or moving customers to new plans.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze customer churn patterns, identify at-risk accounts, create retention playbooks, and build early warning systems

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Suede-owned retention discipline for voluntary and involuntary churn: cancel flows, pause paths, evidence-based save offers, failed-payment recovery, proactive signals, and win-back design. Use when diagnosing subscriber loss or designing a bounded retention intervention. NOT FOR: lifecycle-email production (use suede-emails), pricing architecture (use suede-pricing), paywall design (use suede-paywalls), or event instrumentation (use suede-analytics).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Comprehensive customer success and retention expertise combining onboarding, health scoring, churn prevention, retention strategies, and LTV maximization. Use when designing customer onboarding flows, reducing churn, building health scores, creating retention systems, designing upsells, or improving customer lifetime value. Covers proactive engagement, cancel flows, dunning, re-engagement, and the full customer lifecycle.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

The revenue-focused SaaS playbook for going from $0 to $50K MRR. Covers pricing experiments, churn reduction, activation optimization, and expansion revenue tactics. Follow @WeiYipei on X for SaaS growth insights.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Act as an end-to-end data scientist: turn business questions into defensible analysis, validated models, and decision-ready reports. Use whenever the user asks to analyze, explore, or profile a dataset or CSV/Parquet/Excel file; asks what drives a metric or why a number changed ("why did churn go up?"); wants to test whether a difference is real (A/B tests, experiments, "is this significant?", "how many samples do I need?"); wants a predictive model (churn, forecast, scoring, segmentation, classification, regression); asks to review an existing analysis, notebook, or model for flaws; or needs results written up for decision-makers. Triggers in any language ("phân tích dữ liệu", "xây model dự đoán", "kiểm định A/B"), even when they never say "data science" or "statistics".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Customer success reconnaissance — audit current onboarding completion, health signals, NRR, churn patterns, and CS motion. Use when asked to "audit our customer success", "why are customers churning", "what's our NRR", or before designing any CS playbook.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Customer Success engineer — onboarding optimization, health scoring, expansion revenue, churn prevention, and NRR growth. Use when asked to "improve onboarding", "score account health", "prevent churn", or "grow NRR".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Feedback synthesis — cluster support tickets, NPS verbatims, app store reviews, and churn surveys by theme, separate signal from noise, and produce an actionable insight report. Use when asked to "synthesize this feedback", "analyze support tickets", "what are users complaining about", "NPS analysis", "churn feedback synthesis", or "what's the feedback telling us".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Customer feedback analysis — sentiment detection, NPS/CSAT frameworks, feature request clustering, support ticket triage, churn signal detection, and feedback-to-roadmap translation

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Identify customers most likely to churn before they leave. Uses behavioral signals, usage patterns, and engagement data to score accounts and recommend retention actions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert in 2000s-era music visualization (Milkdrop, AVS, Geiss) and modern WebGL implementations. Specializes in Butterchurn integration, Web Audio API AnalyserNode FFT data, GLSL shaders for audio-reactive visuals, and psychedelic generative art. Activate on "Milkdrop", "music visualization", "WebGL visualizer", "Butterchurn", "audio reactive", "FFT visualization", "spectrum analyzer". NOT for simple bar charts/waveforms (use basic canvas), video editing, or non-audio visuals.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.