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Von is built for
Marketing

What can Von do for Marketing?

Win/loss analysis

Analyze all deals won and lost in the last 6 months. Top reasons we win, top reasons we lose. Break down by competitor and segment.

Competitive signal detection

Which active deals have a competitor mentioned in recent calls or emails? For each, which competitor and are we positioned well?

Battle card update

Update our battle card for [Competitor] with real data — pull from every deal where we've competed. What objections came up?

Messaging validation

Go through all sales calls from the last 90 days. What pain points are prospects bringing up most? Does our messaging match how buyers talk?

Weekly competitive roundup

Give me a weekly roundup of all competitive mentions in calls and emails — which competitors, what was said, and any new positioning.

Competitive enablement deck

Build me a competitive enablement deck for [Competitor] — pricing intel, objection handling, win themes, and loss themes. From actual deal data.

Buyer persona research

Analyze all discovery calls from the last quarter. What job titles are we selling to? What are their top 3 pain points? What language do they use?

Product gap analysis

From all closed-lost deals and competitive mentions, what product gaps are prospects citing most? Rank by frequency and deal size impact.

“Von has enabled me to be more data-driven in everything I do. Win/loss analysis, competitive research, and trend identification now take minutes instead of the days or weeks it would have taken me to pull together manually.”
Ellie Wilson
Director of Product Marketing
Why not just use Claude or ChatGPT?

Von is built specifically for the needs of revenue teams

You already pay for general-purpose AI tools, so you could probably build this yourself. But those tools weren’t built for the specific needs of revenue teams.

Explore Build vs. Buy
Claude / ChatGPT
Von logo lockup
System connections
Each user sets up their own connections. Centralized access requires engineering to build and maintain an MCP server.
Admin connects once. Native, one-click integrations available to the entire org immediately.
Business context
Doesn’t know your fields, fiscal year, or churn definition. You write and maintain skill files for every use case.
Von builds a context graph in days. No skill files. Corrections from any user apply to everyone via org-wide memory.
Context limits
Even with a million-token window, 50 deals and hundreds of calls bury the middle. The model samples and gives you plausible guesses, not the full picture.
Pre-processes all unstructured data. Analyzes 10,000+ calls in a single query. Real answers, not sampled guesses.
Search quality
Limited to keyword search. Questions like “Why are we losing to competitor X?” return shallow answers.
True semantic search across all data. Deep answers on competitive losses, rep performance, and discovery patterns.
Enablement
Making it work for each persona is entirely on RevOps to figure out and maintain.
Von knows what works for every persona across 50+ companies. No training required from your side.
Timeline
3-6 months of engineering. You eventually assign 2-3 engineers full-time. Results uncertain.
Ready for the entire GTM team from day one. Productivity boost within two weeks.

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