Renewal managers are running a real pipeline. Real stages, real close dates, a number to hit. You're tracking auto-renew vs. non-auto-renew, co-term logic, expansion attach, GRR vs. NRR. You're responsible for hundreds of accounts, many of them unowned, with signals scattered across Salesforce, Gong, support tickets, and email threads that nobody has time to stitch together. And you're doing it with a fraction of the headcount the new business team gets.
Von is an AI revenue platform that connects to your GTM tech stack and learns how your renewals business actually works. It understands your pipeline structure, your churn definitions, your expansion categories, and your forecast methodology. It cross-references structured data like CRM and product usage with unstructured data like call recordings and emails. Once it's trained on your business, it handles tasks the same way an analyst would. Except it runs on every account at once.
Here are the use cases where renewals teams are getting the most value today.
1. Renewal pipeline inspection without waiting on ops
If you want a clean view of your renewal book by segment, risk tier, and forecast category, you're probably filing a RevOps request and waiting. By the time the report lands, you're already in your pipeline review call working from memory.
Von pulls directly from Salesforce and returns a structured view in seconds. Slice by ARR, segment, close date, auto-renew status, and stage without touching a dashboard. One renewals team runs a full pipeline review every Monday via a single scheduled command. The report shows up in Slack before the call starts, pre-sorted by risk and ARR.
Try: "Pull all open renewals closing in the next two quarters. Break out by segment, ARR, auto-renew status, and current stage. Flag any account where the close date has been pushed or the renewal stage hasn't moved in 30 days."
2. Automated priority classification across the book
When you have hundreds of renewals and limited headcount, the hardest part isn't working accounts. It's knowing which ones to work. Sorting by ARR or close date is a start, but it misses the accounts quietly going sideways.
Von monitors risk signals across multiple data sources and classifies renewals into priority tiers. At 15Five, the renewals team built this system for 1,800 accounts under $30K ARR with no dedicated account manager. Von tracks five signal categories: renewal factors, a strategic risk score, sentiment (NPS, renewal surveys), CX engagement (office hours, tickets), and product usage. It classifies each account into P1 through P4 tiers, stamps a Risk Signal field in Salesforce, and triggers a Slack alert to a renewal specialist with a 24-hour SLA.
Sydney Showalter, Head of Renewals and Expansions at 15Five: "Von has completely changed how we approach renewal intelligence at 15Five. Instead of relying solely on manually documented risk factors in Salesforce, Von lets me analyze actual conversation signals from recorded calls and emails across our entire renewal book to identify the real reasons customers churn. This is analysis I previously couldn't justify dedicating headcount to."
Try: "Classify all open renewals closing in the next 90 days into priority tiers based on ARR, engagement activity, support ticket volume, and sentiment from the last three calls. Flag any P1 accounts and draft a Slack alert for each one with a summary of why it was escalated."
3. Managing unowned accounts at scale
The long tail of your renewal book doesn't disappear because there's no one assigned to it. Those accounts churn quietly, and you find out after the fact.
Von monitors these accounts continuously and surfaces only the ones that need a human. You define what counts as a risk signal and what triggers an alert. Von handles the monitoring and brings you the accounts that matter, with a summary of what it found and what it recommends. You spend your time on intervention, not surveillance.
Try: "Monitor all accounts under $15K ARR with no assigned renewal manager. Alert me in Slack if any account shows a drop in product usage, an increase in support tickets, a negative NPS response, or no customer-facing activity in the last 45 days. Include a one-paragraph summary of the risk signals for each account."
4. Renewal prep with full account context
Walking into a renewal conversation with a strong briefing changes the dynamic. Building it manually means pulling from Gong, checking the support log, reviewing usage data, skimming email threads, and looking up competitive mentions. Even then, you're probably missing something.
Von builds the briefing for you. It pulls from every connected source and surfaces what matters: usage trends, sentiment signals, open issues, past expansion conversations, and competitive mentions. Ceres Imaging takes it a step further. Von surfaces external market data relevant to each customer's business, so renewal managers walk in with something substantive to discuss beyond the contract.
Try: "Build a renewal prep brief for Acme Corp. Include: ARR, renewal date, auto-renew status, product usage trend for the last 60 days, summary of the last three calls, open support tickets, competitive mentions across all call recordings, and recommended talking points."
5. Renewal forecast accuracy
Renewal forecasts are often built the same way sales forecasts are: reps tell you what they think will close. The problem is that rep confidence and actual account health don't always line up.
Von cross-references what's in Salesforce against what's actually happening in the account. It looks at engagement cadence, call sentiment, support volume, and product usage, then flags accounts where the forecast category doesn't match the signals. An account marked "likely to renew" where usage has dropped 40% and the last two calls were negative gets surfaced before it slips, not after.
At Cresta, the team walked Von through their full renewal pipeline structure: target renewal ARR, forecast renewal ARR, final renewal ARR, GRR tracking, and all expansion types including usage, cross-sell, co-term, and co-term logic. Von learned the entire structure. Now every user querying the renewal forecast gets answers grounded in the same methodology.
Try: "Pull all renewals forecast as Likely or Committed next quarter. For each one, review product usage trends, call sentiment from the last two touches, and support ticket volume. Flag any account where the forecast category and signal profile don't match, with a one-line reason for each."
6. Churn theme analysis from closed-lost renewals
Most renewal teams do a solid job working individual accounts. Fewer have the bandwidth to analyze what they're learning across the book. What were the actual reasons customers churned last quarter? Without that analysis, you address the symptom in each account and never fix the underlying problem.
Von analyzes closed-lost renewals at scale, pulling from CRM data, call recordings, and close reason fields to identify patterns. It groups churn by category, surfaces the most common pre-churn signals, and flags whether issues were addressable or structural. One renewals team built a weekly "Executive Revenue Pulse" from this: a report that summarizes churn themes and expansion signals from the last 30 days of closed renewals, so leadership sees what's driving retention results without anyone pulling the data manually.
Try: "Analyze all renewals lost in the last two quarters. Group losses by primary churn reason from close reason fields and call recordings. For each category: how many accounts, total ARR lost, and the most common pre-churn signals. Flag any patterns that appeared at least 60 days before the renewal date."
Renewals is a revenue motion, and it deserves the same analytical infrastructure that new business gets. The teams seeing the most value from Von are the ones that stopped choosing between covering the whole book or covering it well. Von handles the analysis, the monitoring, and the synthesis. The renewal manager decides what to do with it.
