How to Identify At-Risk Accounts Before Renewal Using Cross-Source Analysis
Stop relying on gut feel to predict churn. Connect your CRM, product usage, support, and engagement data. Querri surfaces the accounts that need attention before renewal, with the data to explain why.
Open QuerriWhat you'll need
Querri (Free trial) to connect your data sources, run the risk analysis, and score accounts
CRM data — HubSpot or Salesforce with account records, renewal dates, contract values, and health score fields if available
Product usage data — CSV or database with login frequency, feature adoption, session duration, or activity metrics by account
Support ticket history — CSV from Zendesk, Intercom, or Freshdesk with ticket counts, categories, sentiment, and resolution times
Optional: NPS/CSAT survey data — CSV with scores and response dates by account
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If you have any questions, you can request a demo or email our team.
Before we begin
Most CSMs and renewal managers rely on lagging indicators or a single health score to predict churn. The problem: those scores miss compounding risk that lives across systems. A customer might have steady usage but spiking support tickets and a collapsing NPS. Each signal points to a different problem, and none of it shows up in the CRM.
This playbook walks through connecting signals from CRM, usage, support, and engagement data into a composite risk view so you can spot at-risk accounts before renewal, understand what's driving the risk, and build targeted interventions instead of guessing.
How it works:
- • Connect or upload CRM, usage, support, and engagement data to Querri
- • Filter to accounts renewing in the next 60–90 days
- • Flag risk signals — declining logins, rising tickets, stale CSM touchpoints — across sources
- • Rank accounts by composite risk so your team knows where to focus
- • Export the risk list as CSV or build a dashboard for weekly monitoring
Follow the steps
Connect your customer data sources
Start by connecting or uploading your CRM data with renewal dates and contract values, product usage metrics, support ticket history, and optionally NPS/survey data into the Querri Library. Each source gets its own dataset; Querri profiles and prepares the data automatically.
If your systems support a direct data connection, check Querri's integrations page. Connected sources keep risk signals current without manual exports, so your at-risk account monitoring stays up to date as new data flows in.
Minimum viable sources: CRM with renewal dates plus at least one behavioral signal (usage or support). Adding NPS and billing data makes the risk model stronger.
Filter to accounts renewing in the next 60–90 days
Pull your renewal pipeline first. Filter down to the accounts actually approaching renewal so you're not running risk analysis on accounts that aren't up for renewal for another year. This is the working list the rest of the analysis runs on.
"Show me all accounts with renewal dates in the next 90 days. Include account name, renewal date, contract value, CSM owner, and current plan tier."
Why 60–90 days? Starting the risk review a full quarter before renewal gives you enough runway to intervene. Shorter windows leave too little time for save plays to take effect. Adjust the window based on your sales cycle length.
Flag risk signals across sources
Now pull the behavioral signals: declining logins, rising support ticket volume, stale engagement. This is where cross-source analysis earns its keep. No single system has the full picture, and no single metric tells you whether an account is actually in trouble.
"For each renewing account, show me the 90-day trend in logins, support tickets opened, and last CSM touchpoint. Flag any accounts where logins dropped more than 20%, tickets increased more than 30%, or there's been no CSM contact in the last 45 days."
Why cross-source? A usage dip alone might be seasonal. A usage dip plus a spike in support tickets plus no recent CSM touchpoint is a compounding risk pattern you'd never catch from a single system.
Querri handles the joins automatically. You don't need to define schemas or tell it which fields to match — just ask the question in plain English. Querri figures out how to connect your CRM, usage, support, and engagement data on its own.
Rank accounts by composite risk
Individual signals are useful but hard to act on at scale. Sort your renewal list by the accounts showing the most compounding risk so your team knows where to spend their time first.
"Sort by accounts with the steepest usage decline and most open tickets. Show me the top 20 accounts ranked by overall risk, with columns for: usage trend, ticket count, days since last CSM touch, contract value, and renewal date."
Prioritize by impact, not just severity. A high-risk account at $5K matters less than a moderate-risk account at $200K. Weight contract value into the ranking so your team focuses on the renewals that actually count.
Export the risk list or build a monitoring dashboard
Take the ranked risk list out of Querri and into your team's workflow. Export as a CSV, or build a Querri dashboard the CS team can pull up every week. Either way, the goal is the same: get at-risk accounts visible before renewal conversations start.
The best renewal protection isn't a last-minute discount. It's an early, data-backed conversation with the customer about what's not working and a concrete plan to fix it.
Automate the cadence. Set this workflow to run weekly or bi-weekly so new risk signals surface automatically. Accounts don't become at-risk overnight. The warning signs compound gradually, and catching them early is the entire point of cross-source monitoring.
Make it a team habit. Share the dashboard link with your CS team and review the at-risk list in your weekly standup. When risk monitoring becomes a recurring ritual rather than a one-off analysis, you catch problems weeks earlier.
Going deeper
Add predictive signals and automate monitoring
Going deeper
Add predictive signals and automate monitoring
The five steps above give you a point-in-time risk assessment from structured data. When you want to add predictive depth (support sentiment, churn pattern detection, or automated alerts), the Researcher and Categorize tools open up three additional layers.
Classify support tickets by sentiment and escalation risk
Add a sentiment layer to support data so the risk model captures not just ticket volume but customer frustration level.
"Add a 'Sentiment' and 'Escalation Risk' column to each support ticket for accounts renewing in the next 90 days, classifying based on the ticket text and resolution history."
Detect early churn patterns from usage data
Look for usage patterns that historically precede churn: declining login frequency, shrinking active user counts, feature abandonment, or reduced session depth.
"Analyze the usage data for accounts renewing in Q3. Identify accounts showing churn-correlated patterns: 3+ consecutive months of declining MAU, feature adoption dropping below 40%, or no admin login in the last 30 days."
Auto-tag at-risk accounts by risk driver
Instead of just a composite score, tag each at-risk account with its primary risk driver so the team knows what kind of save play to run.
"For each account in the top-20 risk list, assign a primary risk category: Usage Decline, Support Overload, Engagement Dropout, NPS Collapse, or Multi-Signal Risk. Show the distribution across categories."
Tips for better renewal risk management
Start monitoring 90 days before renewal, not 30
By 30 days out, the customer has usually made their decision. A 90-day window gives you enough time to find the problem, build a save plan, and show progress before anyone mentions the contract. Thirty days is enough time to schedule a call. It's not enough time to fix anything.
Weight your risk model to match your actual churn patterns
Generic health scores treat all signals equally. Your business doesn't churn generically. If the accounts you lose tend to show usage decline first, weight usage highest. If NPS collapse is the stronger signal in your segment, lead with that. The model is only as useful as it reflects how your accounts actually behave before churning.
Don't just flag risk — explain why
A risk score without context isn't something a CSM can act on. The drill-down step matters more than the score. When you share risk findings with the team, include the primary driver and the supporting data, not just a red dot on a dashboard.
Automate the monitoring cadence
Manual risk reviews happen when someone remembers to run them. Set the workflow on a weekly or bi-weekly schedule so new signals surface automatically and nothing slips through between QBRs.
Separate signal from noise with cross-source validation
One metric moving in the wrong direction might be seasonal. When usage drops while tickets spike and the last CSM touchpoint was six weeks ago, that's not noise. Cross-source agreement is what separates a real risk signal from a blip.
Close the loop: track save plan outcomes
The model gets better when you feed outcomes back in. Track which interventions worked and which didn't. Over time, that data tells you whether product training actually lifts adoption, whether an exec check-in shifts engagement, and which account profiles are worth fighting for vs. which are already gone.
Frequently asked questions
How early before renewal should you start monitoring accounts for at-risk signals?
What data sources are most important for detecting at-risk accounts?
How does cross-source analysis differ from a single health score?
Can this workflow be automated to run weekly or bi-weekly?
What should you do once you've identified an at-risk account?
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