You manage a book of business with dozens or hundreds of accounts. You know the renewals calendar. You know which customers are happy because they tell you, and you know which ones are quiet in a way that feels wrong. But the distance between "I have a bad feeling about this account" and "here is a data-backed risk assessment with a save plan" is enormous. It requires pulling data from your CRM, cross-referencing support tickets, reviewing call recordings, checking product usage, and organizing it all into something actionable. For one account, that takes an hour. For your whole book, it's not realistic. And if you also manage or work alongside a support team, the signal-to-noise ratio gets even harder, because the most important churn indicators are often buried in ticket volumes and escalation patterns that never make it into a renewal conversation.
Von connects to your CRM, call recordings, emails, support platforms like Zendesk, and data warehouse, then spends a few days building a deep understanding of your business. It learns your churn definitions, your health score methodology, your renewal process, and your account segmentation. For customer success teams, and the support teams that work alongside them, Von operates as headcount that handles the analytical and operational work sitting between your customer conversations. It runs churn analysis across structured and unstructured data in a single query, tracks sentiment across every customer call, automates renewal prep, and delivers executive reporting on a set cadence. Your definitions and processes live in Von's organizational memory, so the outputs are consistent across the team.
Here are the use cases where customer success teams are getting the most value today.
1. Multi-Source Churn Risk Analysis
Most churn prediction relies on one or two signals. It could be product usage, or a health score buried in your CRM. The problem is that churn rarely comes from a single source. A customer who isn’t logging in as frequently might be ok. A customer who isn’t logging in as frequently, had declining sentiment on their last three calls, and opened two escalation tickets last month is a different story entirely.
Von runs churn analysis across your full data stack in a single query. It pulls in CRM activity, product usage trends if it’s stored in your data warehouse, call sentiment from your call recording tool, email engagement patterns, and open support tickets. Then it scores each account based on the combination of those signals. The methodology is fully auditable, so when your VP asks why a specific account is flagged, you can show exactly which data points contributed to the score.
Try: "Run a churn risk analysis for my full book of business. Pull from Salesforce activity, product usage stored in [your data warehouse], call sentiment from Gong, and open support tickets. Score each account on a 1-10 risk scale and show me the contributing factors for any account scoring above 7."
2. Customer Health Scoring That Shows Its Work
You probably have a health score in your CRM. You probably also don't fully trust it. Most health scores are either too simplistic (a single metric like login frequency) or too opaque (a number that nobody can explain). When a CSM sees a score of 65, they don't know what to do with it unless they also know why it's 65.
Von populates a health score and the reasoning behind it. One CS team built two new Salesforce fields: a numeric health score and a long-text explanation field that Von fills automatically. Von analyzes call sentiment, engagement patterns, support ticket history, and account activity, then writes a plain-language summary of what's driving the score. They also tuned Von to recognize that certain types of problem-solving discussions (like a customer asking about a specific workflow issue) are actually positive engagement signals, not negative ones. That kind of nuance is something generic sentiment tools miss entirely.
Try: "For each account in my book, generate a health score from 0 to 100 based on call sentiment, email engagement, support ticket volume, and product usage. Write a 2-3 sentence explanation of the score and populate it in the Health Score Reason field in Salesforce."
3. Renewal Prep With Full Account Context
Renewal prep today usually means opening the CRM, scanning for recent activity, maybe listening to the last call recording, and checking whether any support tickets are open. If you're thorough, you also pull product usage data and review email threads. Each source has a piece of the picture, and none of them are connected. So you spend 30 to 45 minutes per account assembling a briefing that should already exist.
Von builds the renewal briefing for you. It pulls from every touchpoint, including CRM data, call recordings, emails, support tickets, and product usage, and delivers a consolidated view of the account. You get a timeline of key interactions, the customer's stated priorities from recent calls, any open issues, usage trends, and a renewal risk assessment. For large renewal books, Von can prepare briefings for every account renewing in the next 90 days and deliver them on a set schedule.
Try: "Pull all accounts renewing in the next 90 days. For each one, build a renewal briefing that includes: ARR, last contact date, call sentiment trend over the last two quarters, open support tickets, product usage trend, and a risk assessment. Rank by ARR and flag anything with declining engagement."
4. Automated Renewal Alerts and Priority Classification
Your renewal calendar lives in Salesforce. The problem is that a renewal date doesn't tell you how much attention an account needs. A customer renewing in 60 days with strong engagement and no open issues is very different from one renewing in 60 days with a support escalation and radio silence from their executive sponsor.
One CS team built a priority classification system (P1 through P4) using Von. Von monitors communications for churn signals, including keywords like "cancellation" or "looking at alternatives," tracks support ticket spikes, and stamps a Risk Signal field in Salesforce. That field feeds into an automated priority classification that triggers Slack alerts to renewal specialists. P1 accounts get a 24-hour SLA. The system runs on a scheduled cadence (daily, weekly) so risk classification updates as new signals come in rather than waiting for a CSM to manually review the account.
Try: "Monitor all accounts renewing in the next 120 days. Flag any account where you detect churn signals in call recordings or emails, support ticket volume has spiked in the last 30 days, or the primary contact has gone quiet. Classify each flagged account as P1 through P4 based on ARR and severity of risk signals, and send a Slack alert to the assigned renewal specialist for anything classified P1 or P2."
5. Save Plans for At-Risk Accounts
An account is flagged as at-risk. Now what? Building a save plan means pulling together the engagement timeline, understanding what went wrong, identifying what value the customer has received, and developing a strategy to turn things around. That's a half-day project if you do it well, and when multiple accounts are at risk simultaneously, the work stacks up fast.
Von builds save plans by pulling from the full account history. It generates a branded deck that includes the engagement timeline, value delivered, key pain points from recent calls, competitive exposure if the customer has mentioned alternatives, and a recommended renewal strategy. One team used Von to manage churn risk during a major service transition. Von analyzed every account manager call for sentiment and escalation reasons, then surfaced only the accounts with negative or neutral sentiment. For each flagged account, it generated a summary of why the account was escalated, what action the rep took, and a recommended next step, delivered as a notification to the AE and their manager.
Try: "This account is flagged as high churn risk. Build a save plan that includes: the full engagement timeline for the last 12 months, value delivered (key milestones, usage stats, support resolution), what went wrong based on call sentiment and support history, and a recommended strategy to retain them. Deliver it as a deck."
6. Sentiment Tracking at Scale
You listen to your customer calls. But you listen to the ones you have time for, which is a fraction of the total. The sentiment patterns that matter most, the slow decline in enthusiasm, the shift from collaborative problem-solving to transactional check-ins, often happen across calls you never hear.
Von analyzes every customer call for sentiment, not just a sampling or the ones you flag. It tracks patterns over time and surfaces accounts where sentiment is trending downward. One team built a workflow where Von monitors all customer calls and delivers a notification after each one that includes a sentiment score, a summary of the conversation, and whether any escalation is needed. Managers only see the notifications for accounts flagged as at-risk, which means they spend their time on the accounts that need attention rather than reviewing every call summary.
This is analysis that most CS teams know would be valuable but can't justify dedicating headcount to. Von makes it a recurring output.
Try: "Analyze all customer calls from the last 30 days. Score each one for sentiment on a 1-10 scale. Flag any accounts where sentiment has declined across two or more consecutive calls. For each flagged account, show me the trend, the key topics from recent calls, and a recommended action."
7. Executive Health Reporting on Autopilot
Leadership wants a portfolio health report. That means pulling data from across the org, summarizing it by segment, identifying trends, and packaging it into something presentable. It takes hours, it happens weekly or monthly, and the format barely changes. The only thing that changes is the numbers.
One CS team used Von to build what they call an "Executive Revenue Pulse," a weekly report that Von generates automatically. It summarizes each priority segment's health, highlights thematic trends (like a spike in pricing-related churn signals or a drop in engagement across a specific account tier), and surfaces the accounts that need leadership attention. Von's scheduled commands handle the delivery, so the report lands in the right inbox or Slack channel at the same time every week without anyone having to build it manually.
Try: "Every Monday at 8 AM, generate an executive health report for my CS leadership team. Break it down by account tier. For each tier, show total ARR, number of accounts by health score range, accounts where sentiment declined last week, and any P1 or P2 renewals in the next 60 days. Deliver to Slack."
These seven use cases come back to the same thing. Customer success teams have always known that preventing churn requires catching the signals early, understanding the full picture, and acting before it's too late. The work to do that has never been the mystery. The capacity to do it at scale, across a full book of business, with support data and call sentiment and usage trends all factored in, is what's been missing. Von handles the analytical and operational work that sits between knowing an account might be at risk and having a plan to save it. The time you get back goes straight into the customer conversations that actually move the needle on retention.
