churn-risk

Name: churn-risk
Author: indranilbanerjee

507stars91forks

How to install churn-risk?

indranilbanerjee/digital-marketing-pro/churn-risk

$npx -y skills add indranilbanerjee/digital-marketing-pro --skill churn-risk

Installs into the current project.

Summary

Assess customer churn risk. Use when: churn scoring, at-risk segment identification, intervention playbook generation.

Files · 1

View on GitHub

SKILL.md

1	# /digital-marketing-pro:churn-risk
2
3	## Purpose
4
5	Assess churn risk across customer segments and generate intervention strategies. Score segments using behavioral signals — email engagement decline, purchase frequency drops, login pattern changes, support ticket escalations — to categorize each segment into risk tiers and produce actionable intervention playbooks. This command bridges the gap between knowing customers are churning and knowing what to do about it. Instead of reactive "win-back" campaigns after customers have already left, it identifies at-risk segments early enough to intervene while the relationship is still recoverable. Each intervention playbook includes specific actions, timing windows, channel recommendations, and messaging approaches calibrated to the risk tier and customer value.
6
7	## Input Required
8
9	The user must provide (or will be prompted for):
10
11	- Customer segments to score: The segments to evaluate — can be predefined CRM segments (e.g., "Enterprise accounts," "Monthly subscribers," "First-time buyers") or behavioral cohorts (e.g., "Users who haven't purchased in 60 days," "Users with declining email opens"). Each segment should include available behavioral signals: email engagement trends (open rate, click rate, unsubscribe rate over time), purchase frequency and recency, login or product usage patterns, support ticket volume and sentiment, and any other engagement indicators tracked in the CRM
12	- CRM data source: Which CRM system holds the customer data — Salesforce, HubSpot, or another connected CRM MCP. The command will pull behavioral data directly from the CRM if connected, or the user can provide exported data
13	- Intervention budget (optional): Total budget available for retention interventions — used to prioritize which segments and actions to focus on based on LTV-at-risk versus intervention cost. If not provided, all recommendations are generated without budget filtering
14	- Lookback period (optional): How far back to analyze behavioral trends — defaults to 90 days. Shorter windows catch rapid deterioration, longer windows identify slow-burn churn patterns
15	- Custom churn signals (optional): Brand-specific behavioral indicators beyond the defaults — e.g., "stopped using feature X," "downgraded plan tier," "removed payment method," "decreased order size" — that have historically preceded churn for this brand
16
17	## Process
18
19	1. Load brand context: Read `~/.claude-marketing/brands/_active-brand.json` for the active slug, then load `~/.claude-marketing/brands/{slug}/profile.json`. Apply customer lifecycle data, historical churn rates, known retention patterns, and industry benchmarks. Also check for guidelines at `~/.claude-marketing/brands/{slug}/guidelines/_manifest.json` — if present, load any communication frequency limits or channel restrictions that constrain intervention options. Check for agency SOPs at `~/.claude-marketing/sops/`. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with industry defaults.
20	2. Gather customer behavioral data: Connect to the CRM MCP (Salesforce or HubSpot) and pull behavioral signal data for each segment — email engagement metrics over the lookback period, purchase history with frequency and recency calculations, product usage or login patterns, support interactions with sentiment indicators, and any custom churn signals the user specified. If CRM MCP is not connected, prompt the user to provide exported segment data or configure the integration.
21	3. Score each segment for churn risk: Execute `python "${CLAUDE_PLUGIN_ROOT}/scripts/churn-predictor.py" --brand {slug} --action score-segment --segment-name {name} --signals '{...behavioral signals...}'` with the behavioral signal data. The scoring model applies weighted signals — recent engagement decline is weighted more heavily than historical patterns, and signals are combined using a composite risk score. Each signal contributes based on its predictive strength: purchase recency (highest weight), engagement trend direction and velocity, support sentiment trajectory, and usage pattern breaks. Scores are normalized to 0-100 for comparability across segments.
22	4. Categorize into risk tiers: Map composite scores to four risk tiers — Low (0-25, stable engagement, no intervention needed beyond standard nurture), Medium (26-50, early warning signals present, proactive engagement recommended), High (51-75, multiple deteriorating signals, targeted intervention required within 2 weeks), and Critical (76-100, imminent churn risk, immediate high-touch intervention needed within 48 hours). These cutoffs are fixed heuristic bands, not learned from your data — the script computes the 0-100 score and these bands map score→tier. Recalibrate the band edges against your own

People who liked this also viewed

6 picks

Type

azure-kustoQuery and analyze data in Azure Data Explorer (Kusto/ADX) using KQL for log analytics, telemetry, and time series analysis. WHEN: KQL queries, Kusto database queries, Azure Data Explorer, ADX clustersSkillsJul 2026470k1.3k
lark-base飞书多维表格（Base）操作：建表、字段、记录、视图、统计、公式/lookup、表单、仪表盘、workflow、角色权限；遇到 Base/多维表格/bitable 或 /base/ 链接时使用。文件导入转 lark-drive，认证/授权转 lark-shared。SkillsJul 2026382k16k
lark-calendar飞书日历：管理日历日程和会议室。查看/搜索日程、创建/更新日程、管理参会人、查询忙闲和推荐时段、预定会议室。当用户需要查看日程安排、创建/修改会议、查询/预定会议室时使用。不负责：查询过去的视频会议记录（走 lark-vc）、待办任务（走 lark-task）。SkillsJul 2026380k16k
shadcnManages shadcn components and projects — adding, searching, fixing, debugging, styling, and composing UI, including chat interfaces. Provides project context, component docs, and usage examples. AppliSkillsJul 2026246k120k
xlsxUse this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.SkillsJul 2026139k163k
firebase-data-connectAgent Skills for FirebaseSkillsJul 2026108k387

churn-risk

507stars91forks

View on GitHub

How to install churn-risk?

indranilbanerjee/digital-marketing-pro/churn-risk

$npx -y skills add indranilbanerjee/digital-marketing-pro --skill churn-risk

Installs into the current project.

Summary

Assess customer churn risk. Use when: churn scoring, at-risk segment identification, intervention playbook generation.

Files · 1

View on GitHub

SKILL.md

1	# /digital-marketing-pro:churn-risk
2
3	## Purpose
4
5	Assess churn risk across customer segments and generate intervention strategies. Score segments using behavioral signals — email engagement decline, purchase frequency drops, login pattern changes, support ticket escalations — to categorize each segment into risk tiers and produce actionable intervention playbooks. This command bridges the gap between knowing customers are churning and knowing what to do about it. Instead of reactive "win-back" campaigns after customers have already left, it identifies at-risk segments early enough to intervene while the relationship is still recoverable. Each intervention playbook includes specific actions, timing windows, channel recommendations, and messaging approaches calibrated to the risk tier and customer value.
6
7	## Input Required
8
9	The user must provide (or will be prompted for):
10
11	- Customer segments to score: The segments to evaluate — can be predefined CRM segments (e.g., "Enterprise accounts," "Monthly subscribers," "First-time buyers") or behavioral cohorts (e.g., "Users who haven't purchased in 60 days," "Users with declining email opens"). Each segment should include available behavioral signals: email engagement trends (open rate, click rate, unsubscribe rate over time), purchase frequency and recency, login or product usage patterns, support ticket volume and sentiment, and any other engagement indicators tracked in the CRM
12	- CRM data source: Which CRM system holds the customer data — Salesforce, HubSpot, or another connected CRM MCP. The command will pull behavioral data directly from the CRM if connected, or the user can provide exported data
13	- Intervention budget (optional): Total budget available for retention interventions — used to prioritize which segments and actions to focus on based on LTV-at-risk versus intervention cost. If not provided, all recommendations are generated without budget filtering
14	- Lookback period (optional): How far back to analyze behavioral trends — defaults to 90 days. Shorter windows catch rapid deterioration, longer windows identify slow-burn churn patterns
15	- Custom churn signals (optional): Brand-specific behavioral indicators beyond the defaults — e.g., "stopped using feature X," "downgraded plan tier," "removed payment method," "decreased order size" — that have historically preceded churn for this brand
16
17	## Process
18
19	1. Load brand context: Read `~/.claude-marketing/brands/_active-brand.json` for the active slug, then load `~/.claude-marketing/brands/{slug}/profile.json`. Apply customer lifecycle data, historical churn rates, known retention patterns, and industry benchmarks. Also check for guidelines at `~/.claude-marketing/brands/{slug}/guidelines/_manifest.json` — if present, load any communication frequency limits or channel restrictions that constrain intervention options. Check for agency SOPs at `~/.claude-marketing/sops/`. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with industry defaults.
20	2. Gather customer behavioral data: Connect to the CRM MCP (Salesforce or HubSpot) and pull behavioral signal data for each segment — email engagement metrics over the lookback period, purchase history with frequency and recency calculations, product usage or login patterns, support interactions with sentiment indicators, and any custom churn signals the user specified. If CRM MCP is not connected, prompt the user to provide exported segment data or configure the integration.
21	3. Score each segment for churn risk: Execute `python "${CLAUDE_PLUGIN_ROOT}/scripts/churn-predictor.py" --brand {slug} --action score-segment --segment-name {name} --signals '{...behavioral signals...}'` with the behavioral signal data. The scoring model applies weighted signals — recent engagement decline is weighted more heavily than historical patterns, and signals are combined using a composite risk score. Each signal contributes based on its predictive strength: purchase recency (highest weight), engagement trend direction and velocity, support sentiment trajectory, and usage pattern breaks. Scores are normalized to 0-100 for comparability across segments.
22	4. Categorize into risk tiers: Map composite scores to four risk tiers — Low (0-25, stable engagement, no intervention needed beyond standard nurture), Medium (26-50, early warning signals present, proactive engagement recommended), High (51-75, multiple deteriorating signals, targeted intervention required within 2 weeks), and Critical (76-100, imminent churn risk, immediate high-touch intervention needed within 48 hours). These cutoffs are fixed heuristic bands, not learned from your data — the script computes the 0-100 score and these bands map score→tier. Recalibrate the band edges against your own

People who liked this also viewed

6 picks

Type