Should You Even Pitch This Deal?

Every proposal your agency writes costs money. A typical proposal requires 20-40 hours of senior time — strategy, scoping, writing, design, review. At a fully loaded cost of €100/hour, that's €2,000-4,000 per pitch.

If your win rate is 30%, you're spending €6,700-13,300 in proposal costs for every deal you close. That's a hidden cost most agencies never calculate — and never optimize.

The Problem with Gut Feel

Most agencies decide which deals to pursue based on intuition. A partner hears about an opportunity, gets excited about the brand name or the project scope, and the team starts writing. There's no systematic evaluation of whether this particular deal is worth pursuing.

The result: agencies spend equal effort on deals they have a 60% chance of winning and deals they have a 10% chance of winning. The math is brutal — the expected value of a 10% deal at €20,000 is just €2,000, which barely covers the proposal cost.

What Is Bayesian Win Probability?

Bayesian probability is a method of updating your beliefs based on evidence. In the context of agency sales, it answers a specific question: Given what we know about this deal, what is the probability that we win it?

The "Bayesian" part means we start with a baseline probability (our overall win rate) and systematically adjust it based on deal-specific factors. Each factor either increases or decreases the probability.

The Key Factors

Based on our analysis of agency deal outcomes, these factors have the strongest predictive power:

Positive signals (increase win probability): - Existing relationship: You've worked with this client before (+15-20%) - Referral source: The client was referred by a trusted contact (+10-15%) - Clear brief: The client has a well-defined scope and budget (+10%) - Decision-maker access: You're presenting directly to the budget holder (+8%) - Methodology fit: Your specific expertise matches the project type (+12%)

Negative signals (decrease win probability): - Competitive pitch: More than 3 agencies are pitching (-15-20%) - No existing relationship: Cold inbound or RFP (-10%) - Unclear budget: The client says "we don't have a fixed budget" (-12%) - Procurement-led: The decision is driven by procurement, not the project team (-15%) - Scope ambiguity: Requirements are vague or still evolving (-8%)

A Worked Example

Your agency's baseline win rate is 35%. A new opportunity comes in:

• —Starting probability: 35%
• —Existing client relationship: +18% → 53%
• —Clear brief with defined scope: +10% → 63%
• —But: competitive pitch with 4 agencies: -18% → 45%
• —And: procurement-led decision process: -15% → 30%

Result: 30% win probability. At a project value of €15,000, the expected value is €4,500. If your proposal costs €3,000 to produce, the expected profit is just €1,500. Is that worth 30 hours of your team's time?

Compare this to another opportunity:

• —Starting probability: 35%
• —Referral from existing client: +12% → 47%
• —Decision-maker in the first meeting: +8% → 55%
• —Only 2 agencies pitching: +5% → 60%
• —Clear budget communicated: +5% → 65%

Result: 65% win probability. At a project value of €12,000, the expected value is €7,800. Even with a lower project value, this deal is far more profitable to pursue.

The Pursuit/No-Pursuit Framework

Based on win probability, divide opportunities into three categories:

Green: Pursue Aggressively (>50% probability)

Invest fully — senior team, custom proposal, strategic presentation. These are your high-probability deals where maximum effort yields maximum return.

Yellow: Pursue Efficiently (30-50% probability)

Pursue, but optimize your investment. Use proposal templates, limit custom work, and timebox the effort. The deal is worth trying, but not worth your best team's full attention.

Red: Decline or Qualify Further (<30% probability)

Either decline the opportunity or invest in qualification before committing to a full proposal. Can you get a direct meeting with the decision-maker? Can you reduce the number of competitors? If not, the expected value doesn't justify the proposal cost.

The Portfolio Effect

The real power of Bayesian win probability isn't in any single deal — it's in portfolio optimization. By systematically evaluating every opportunity, you shift your proposal pipeline toward higher-probability deals.

The impact compounds:

• —Before: 20 proposals/quarter, 30% average win rate = 6 wins
• —After: 15 proposals/quarter (declined 5 low-probability deals), 45% average win rate = 6.75 wins

Same number of wins, but 25% fewer proposals written. That's 100-200 hours of senior time freed up per quarter — time that can be spent on delivery, strategy, or business development.

And the quality of wins improves too. High-probability deals tend to be better clients: they have clearer briefs, existing relationships, and reasonable expectations. Low-probability deals — the competitive RFPs, the unclear briefs, the procurement-driven processes — are exactly the clients that cause scope creep, payment delays, and margin erosion.

Implementing Win Probability in Your Agency

Step 1: Calculate Your Baseline

Look at your last 20 proposals. How many did you win? That's your baseline win rate. Most agencies are between 25-40%.

Step 2: Score Your Last Deals

Go back through those 20 deals and score them on the factors above. Look for patterns: which factors predicted wins? Which predicted losses?

Step 3: Build Your Scorecard

Create a simple scoring sheet that your team fills out for every new opportunity. It doesn't need to be complex — 5-7 factors with simple +/- adjustments.

Step 4: Set Thresholds

Decide your Green/Yellow/Red thresholds. Start with 50%/30% and adjust based on your economics (proposal cost, average deal size, capacity).

Step 5: Track and Calibrate

After 20-30 scored deals, check your calibration. Are deals you scored at 50% actually winning about 50% of the time? Adjust factors and weights as you learn.

ScopeMetrix builds Bayesian win probability models for agencies using your actual deal data. Schedule a free consultation →