Win rate
Win rate is the share of opportunities that end in a closed-won deal, calculated as deals won divided by deals resolved in the period. Counting against resolved deals rather than all open deals is what makes it meaningful — including deals still in progress mixes an outcome measure with a timing measure.
How it is calculated
Deals won divided by deals won plus deals lost, over a defined window. Some teams also track a value-weighted version, since winning many small deals and losing a few large ones can produce a flattering count-based rate and a poor revenue outcome.
Why it moves
Win rate is as much a qualification measure as a closing measure. A rising win rate can mean better selling or a narrower, better-qualified funnel; a falling one can mean a new competitor, a changed market, or simply that more marginal deals are being worked.
How to read it usefully
Segment it. Win rate by source, by segment and by deal size tells you where to spend; a single blended figure mostly tells you the mix has changed. And pair it with stage conversion rates to see where deals are actually dying.
How Autocloz handles it
Autocloz reports win rate alongside stage conversion and channel attribution, so a change can be traced to a source or a stage rather than only observed. Deals resolved as lost carry a reason, which is what makes segmented win-rate analysis possible later.
FAQ
How do you calculate win rate?
Deals won divided by deals resolved — won plus lost — in the period. Dividing by all open deals instead conflates outcome with timing and produces a number that falls whenever pipeline grows.
Is a higher win rate always better?
Not necessarily. A very high win rate often means the funnel is too narrow and opportunities are being disqualified that could have been won. It is best read alongside pipeline volume and average deal size rather than maximised on its own.
Related terms
A CRM (Customer Relationship Management) system is software that stores and organizes your contacts, companies, deals and interactions in one place, so a team can manage relationships and a sales pipeline. Modern CRMs also automate follow-up, reporting and, increasingly, AI-assisted outreach.
Lead enrichment is the process of automatically adding missing data to a lead or company record — job title, company size, industry, verified email, phone, LinkedIn, technographics — from third-party data sources, so reps can segment, personalize and prioritize without manual research.
An Ideal Customer Profile (ICP) is a description of the company that gets the most value from your product and is easiest to win and retain — defined by firmographics like industry, company size, revenue, geography and technology stack. It targets accounts (the company), distinct from a buyer persona, which describes the individual within the account.
Lead scoring is the practice of assigning a numeric value to each lead based on how well they fit your ideal customer profile (demographic/firmographic fit) and how engaged they are (behavioral signals like email opens, site visits, demo requests). The score ranks leads so sales works the hottest ones first.