Most CROs spend their week trapped in the same cycle. You need an answer, so you request it from RevOps. RevOps queues it behind 15 other requests. By the time you get the report, the window to act on it has already closed. Multiply that by pipeline reviews, forecast prep, board decks, deal risk assessments, competitive analysis, and coaching conversations, and you start to see why so many revenue leaders feel like they're running the business on stale information and gut instinct.
Von changes how that cycle works. It connects to your CRM, call recordings, emails, and data warehouse, spends a few days building a deep context graph of your business, and then delivers analyst-grade work on demand for anyone on your revenue team. It learns your definitions, your pipeline stages, your fiscal calendar, and your terminology. It reasons across structured and unstructured data in a single query. And because it pre-processes billions of tokens weekly, complex multi-source questions come back in seconds instead of days.
For a CRO, that means the questions you stopped asking because they were too expensive or impossible to answer are suddenly back on the table. Here are ten of the highest-value ways CROs are putting Von to work right now.
1. Pipeline inspection without a RevOps ticket
The Monday morning problem is always the same. You need a clean view of pipeline by stage, segment, and rep before your forecast call, and your RevOps team is buried. So you either wait, or you open three dashboards and try to piece it together yourself.
With Von, you type what you need. "Show me all open pipeline for Q3 by stage, segment, and rep. Flag anything that's been in the same stage for more than 21 days." Von pulls directly from Salesforce, cross-references activity data, and returns a structured view in seconds. You skip the ticket entirely, and you stop waiting until Wednesday for a report that's already stale.
One CRO who uses Von runs exactly this kind of analysis every Monday morning from a single prompt. What used to require a pipeline review call just to surface the information now shows up before the call even starts.
Example prompt: "Pull all open opportunities for Q3. Break out by stage, segment, and owner. Flag deals with no activity in the last 14 days and deals that have been in the same stage for 3+ weeks."
2. Forecast prep that doesn't consume your entire Sunday night
Putting together a forecast view means reconciling what reps say they'll close, what the CRM data actually shows, and what the call recordings reveal about deal momentum. That's three different systems and usually a few hours of manual work at minimum.
Von does the reconciliation for you. It pulls opportunity data, layers in call sentiment and email activity, and gives you a consolidated forecast view with risk flags attached. You stop assembling a puzzle and start reviewing an answer.
Example prompt: "Build my Q3 forecast summary. For each deal over $50K, include the rep's committed amount, current stage, days in stage, last customer-facing meeting date, and a sentiment summary from the most recent call. Flag any deal where rep confidence and call sentiment don't align."
3. Board deck and QBR creation from live data
Board decks are a tax on the CRO's time. You pull numbers from Salesforce, copy them into slides, add commentary, send it to RevOps for a sanity check, get corrections back, update the deck, and repeat. The whole cycle takes days.
Von builds the deck from your live data, covering pipeline summary, win/loss trends, rep performance distribution, forecast accuracy, and competitive landscape. It outputs slides, spreadsheets, or both. One CRO who uses Von builds his entire board deck this way. What used to be a multi-day exercise with multiple handoffs is now a series of prompts on a Monday afternoon.
Example prompt: "Create a board-ready pipeline review deck for Q3. Include: total pipeline by stage, quarter-over-quarter bookings trend, win rate by segment, average deal size and cycle time vs. prior year, and top 10 deals with status summaries. Output as slides."
4. Deal risk identification across the full portfolio
At-risk deals usually surface when a rep raises a flag or a forecast number slips. By then, you're in damage control. The deals that quietly die without anyone noticing are the ones that actually kill your quarter.
Von scans the entire portfolio continuously. It cross-references CRM stage and close date against call recordings, email engagement, and activity cadence. A deal where the champion hasn't been on a call in three weeks but the rep still has it committed for this quarter? Von catches that without anyone asking.
This is where analyzing 1,000+ calls in a single query matters. General-purpose AI tools tap out around 50, but Von can scan your entire book of business and surface risk patterns that no human would catch manually.
Example prompt: "Identify all deals forecasted to close this quarter where any of the following are true: (1) no customer-facing meeting in the last 14 days, (2) close date has been pushed more than once, (3) negative or declining sentiment in the last two calls. Rank by deal size."
5. Decision compression: signal to action without five meetings
Eric Choronzy, CRO at DemandScience, describes this better than anyone. The old way a CRO processes a revenue signal looks like this: Signal, discussion, report, analysis, alignment, meeting, decision, rollout, execution. That's nine steps and weeks of elapsed time.
With Von, the loop compresses to signal, decision, execution, iterate.
You see a leading indicator that EMEA pipeline is soft. Instead of scheduling a meeting to discuss it, requesting a report, waiting for the analysis, and then deciding what to do, you ask Von directly. "What's driving the EMEA pipeline shortfall vs. plan? Is it fewer deals created, lower conversion, or both? Which reps are below target on pipe gen?" You get your answer in minutes, make a decision, and move.
Example prompt: "Compare EMEA pipeline created this quarter vs. same period last year. Break out by rep, source, and stage. Identify which reps are below 80% of their pipeline creation target and whether the miss is volume or conversion."
6. Cross-referencing forecast calls against what the data actually shows
You've lived this one. A rep says a deal is "solid, closing this month," and two weeks later it slips to next quarter. The distance between what reps say in forecast calls and what the data reveals is where forecast misses are born.
Von can pull a rep's verbal forecast from their last call recording, compare it against CRM data (stage, activity, close date movement), and flag the discrepancies. You stop relying on what people tell you and start verifying it against what actually happened in the account.
Example prompt: "For all deals my team forecasted as Commit for this month, pull the rep's most recent forecast call comments and compare against: days in current stage, number of close date pushes, last customer meeting date, and email response rate from the buyer in the last 30 days. Flag mismatches."
7. Competitive win/loss analysis at scale
Most competitive analysis is anecdotal. A rep mentions a competitor on a call, and someone logs it in Salesforce if you're lucky. The real story of why you win and lose against specific competitors is buried across hundreds of calls, emails, and opportunity records.
Von can analyze your entire call library for competitive mentions, map them to deal outcomes, and surface patterns that no human could synthesize manually. Which competitor shows up most in deals you lose? At what stage do they typically enter? What objections do buyers raise when they're also evaluating a competitor? You get answers drawn from actual conversations rather than whatever a rep remembered to type into a CRM field.
Example prompt: "Analyze all closed-lost deals from the last 12 months where a competitor was mentioned on a recorded call. Group by competitor. For each, show win rate when they're in the deal, average deal size, the stage where they most often appear, and the top 3 objections buyers raised. Include specific call references."
8. Territory and capacity planning grounded in real performance data
Territory planning usually means a spreadsheet, a set of assumptions about rep productivity, and a lot of negotiation. The problem is that the assumptions are often wrong. Average quota attainment hides massive variance. Your top 30% of reps generate over 70% of revenue (Von's own research across 25,000+ closed-won deals confirms this pattern holds every single year). Planning as if every rep will hit the same number is planning to miss.
Von lets you model territories against actual rep performance curves instead of averages. You can segment by rep tenure, ramp status, historical attainment, and deal type to build territory plans that reflect how your team actually performs.
Example prompt: "Pull all closed-won deals from the last 3 fiscal years by rep and territory. Show attainment distribution by territory. Identify which territories are over-indexed on a single top performer and which have consistent production across multiple reps. Flag territories where more than 60% of revenue comes from one rep."
9. Coaching visibility across your management layer
You know your frontline managers are supposed to be coaching. You don't know if they actually are, or if their coaching is working. The typical approach is anecdotes, 1:1 check-ins, and gut feel to evaluate manager effectiveness.
Von can show you which managers' teams are improving on specific metrics over time, which ones are flat, and where the patterns diverge. It can pull from call recordings to identify whether managers are actually joining customer calls, running deal reviews, or just forwarding pipeline reports. Coaching becomes measurable.
Evan Briere at DemandScience runs weekly performance analysis across his team, tracking win rates, average selling prices, cycle times, and pipeline creation against historical benchmarks. Coaching conversations are grounded in data instead of gut feel.
Example prompt: "For each frontline sales manager, show their team's trailing-90-day trend on win rate, average deal size, and pipeline creation. Compare against the org average and against their own team's performance in the prior quarter. Highlight managers whose teams are trending down on 2+ metrics."
10. M&A and expansion diligence on a new book of business
When you acquire a company, merge teams, or take over a new segment, you inherit a book of business you don't understand. Customer health, renewal risk, rep performance, and competitive exposure are all opaque. Getting a real picture typically takes weeks of manual digging.
Von already has the data connected. You can ask it to profile an entire acquired book of business in minutes, covering renewal timelines, churn indicators from call sentiment, concentration risk by account, and rep-level performance. You walk into your first leadership meeting with the new team already knowing where the problems are.
Example prompt: "For the APAC book of business, show me: total ARR by account tier, renewal dates for the next 6 months, accounts with declining call sentiment over the last 2 quarters, and any accounts where the primary contact has gone silent. Rank by revenue at risk."
All ten of these use cases point to the same thing. The CRO stops being the person who requests information and starts being the person who acts on it. The analytical work that used to sit in a queue, or never got done at all, happens in the time it takes to type a question. That's not a productivity hack. It's a different operating model for running a revenue org.
