How to Build a Churn Risk Score from Customer Support Data

Your inbox already signals churn risk

Your customer support history contains early warnings of potential churn. Customers often communicate more in support tickets than they ever would in feedback surveys. You can harness those valuable clues and transform them into a churn risk score, steering your team toward preventive action. The best part? You’re able to do this with data you’re already gathering every day.

Tickets are a running diary of friction. By systematically analyzing this diary at scale, you can intervene before accounts decide to leave.

What a churn risk score means in practice

A churn risk score estimates the likelihood of a customer cancelling their subscription within a specific time period that you determine, typically 30, 60, or 90 days. This score is then associated with the customer’s account or user profile, updating dynamically as new support interactions and signals are recorded.

You can use the score for triaging support, operational playbooks, and retention forecasting. It’s a helpful metric to share with customer success, sales, and product teams. Begin simply, and optimize your approach as you learn.

Map the data you need

Begin by cataloging your data sources and ensuring you have consistent account and user identifiers across all platforms.

• Support tickets from systems like Zendesk or Intercom
• Live chat conversations and email correspondence
• Transcripts from calls and notes from meetings
• CSAT, CES, and NPS scores linked to support tickets
• Billing incidents, such as downgrades or missed payments
• Product telemetry that tracks user behavior trends

It’s important to respect privacy when creating your churn risk model. Avoid using excessive personal data in your model features. Typewise aids in this process by allowing you to keep customer data within your own systems while using AI to analyze text, offering a balance of advanced analytics and strict privacy standards.

Extract churn signals from support conversations

Operational signals

• First response time by priority level
• Time to resolution and total number of replies
• Ticket reopen rate and escalation count
• SLA breaches, categorized by severity

Linguistic and intent signals

• Sentiment trends over the last 30 and 90 days
• Indicators of frustration such as cancel, refund, or switch
• Mentions of competitors and recurring feature requests
• Policy disputes and legal escalations

Behavioral and financial signals

• Spikes in ticket volume per active user
• Outstanding invoices and account downgrades
• Declining logins or drops in usage of key features

The first response time to a customer query or complaint plays a key role in determining churn risk, as delayed responses may lead to growing customer dissatisfaction. Leveraging AI can streamline your response process. For instance, it can help classify customer queries, prioritize them according to risk score, and auto-generate first drafts of responses. For actionable tips, check out these ways AI can improve response times.

Engineer features your model can trust

• Create rolling time windows: 7, 30, and 90 days
• Aggregate data by account: mean, max, trend, and latest value
• Normalize data distributions with log transformations
• Clip extreme values to control outliers
• Avoid data leakage by only using signals available before the prediction window

Leverage large language models to label text with intent and sentiment. Typewise, for example, can classify both sentiment and intent directly within your existing workflows, helping generate responses that align with your company’s tone, and storing only clean, actionable features for scoring.

Choose a scoring approach that fits your stage

1. Points-based rules. Fast to deploy. Assign points to major red flags, such as multiple SLA breaches or refund requests. Ideal for pilot projects.
2. Logistic regression. Provides interpretable weights and is easy to calibrate. Excellent for smaller datasets.
3. Gradient boosted trees. Handles non-linear relationships and variable interactions. Use SHAP for model transparency.
4. Hybrid. Combine rules for rare, high-impact events with a machine learning model for broader cases.

Begin with a straightforward first version of the churn risk score. This allows you to validate whether your team responds efficiently to the risk indicators it presents. Once this effectiveness is proven, start enhancing the score by adding more nuanced parameters and complexity.

Define churn and create solid labels

Select a clear, quantifiable churn event. For subscription services, this might mean a non-renewal or formal cancellation. For usage-based models, consider prolonged inactivity combined with missed payments.

• Set your prediction horizon, for example, 60 days
• For each day, calculate features from the previous 30 to 90 days
• Label churn if an account cancels within the prediction window

Make sure to split accounts, rather than individual tickets, when dividing training and testing data. Use time-based splits to better simulate real-world deployment.

Calibrate, threshold, and monitor

• Calibrate predicted probabilities with Platt scaling or isotonic regression
• Determine risk-action thresholds based on team capacity, start with “high” and “medium” risk tiers
• Track precision, recall, AUC-PR, and lift, prioritize early saves over vanity metrics
• Review key features weekly to check for drift and unintentional bias

Wire the score into daily operations

A churn score only creates value if it’s integrated into workflows people actually use.

• Display the risk score in your CRM’s account overview panel
• Tag tickets by risk level and main intent
• Send automated Slack alerts with account context and a suggested response
• Automatically schedule outreach tasks for high-risk accounts

Enhance these actions with targeted playbooks:

• High risk: escalate to a senior agent, proactively offer a call, confirm next steps
• Medium risk: deliver a tailored help guide, invite the customer to a brief account review
• Recovering: follow up, gather customer feedback, and ensure issues are fully resolved

Looking to further boost retention? Learn about AI-driven tactics for retaining customers, a powerful complement to your churn score.

Measure impact and iterate

• Compare churn rates between customers contacted through these workflows versus those who weren’t
• Measure save rates by playbook and customer segment
• Monitor net revenue retention among flagged accounts
• Track both response times and resolution times after rollout
• Run A/B tests on outreach messaging and engagement cadence

Share concrete success stories weekly. Real examples help teams believe in the churn score and keep investing in its use.

Build vs buy: pragmatic tooling

You can assemble a churn scoring workflow using your current tools, or add specialized platforms to simplify the process.

• Gainsight: Customer success workflows with health scores and automated playbooks
• Typewise: AI-driven content, sentiment, and intent analysis within CRM, email, and chat tools, offering advanced analytics with in-system data privacy
• ChurnZero: Automation for retention workflows and account health monitoring
• Intercom: Integrated support, messaging, and simple automations
• Zendesk: Robust ticket management with workflow macros, SLA tracking, and comprehensive reporting
• Freshdesk: Scalable ticketing and a knowledge base ideal for leaner teams

If you’re considering a help desk switch, carefully review how your data is modeled and mapped. This comparison of Zendesk alternatives and support platforms can help inform your decision.

A tiny scoring example you can adapt

score = 0

if sla_breaches_30d >= 2:
    score += 25

if negative_sentiment_ratio_30d > 0.4:
    score += 20

if frt_p95_minutes_30d > 120:
    score += 15

if refund_requests_90d >= 1:
    score += 25

if usage_drop_30d >= 30:
    score += 10

if competitor_mentions_90d >= 1:
    score += 10

risk = 'high' if score >= 60 else 'medium' if score >= 35 else 'low'

Though simple, this approach offers immediate value. As you collect more labeled data, replace the fixed rules with a statistical or machine learning model, ensuring your risk thresholds remain visible and actionable for frontline teams.

Common pitfalls to avoid

• Lack of consistent IDs across platforms
• Accidental data leakage from future events
• Overemphasizing accuracy at the expense of recall, for rare churn cases
• Ignoring seasonality and the effects of billing cycles
• Letting a few highly vocal accounts skew your model
• Failing to link alerts with clear operational playbooks
• Not updating risk thresholds after growth or changes to the team

Start putting your churn score to work

You already possess the essential signals needed to build a churn risk score. Turn these insights into higher retention and calmer support interactions. If you’d like help infusing intent analysis and smart responses into your daily workflow, we’re here to assist. Typewise is committed to privacy and delivering measurable results.

Start a conversation with Typewise to build a churn score that your team will actually use.

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