Capture Intelligence

Know your probability of winning before you invest

P-Win scoring answers the most expensive question in GovCon: should you spend $50K-$200K writing this proposal, or walk away? Start scoring every opportunity with data-driven probability of win analysis.

10-20%

Avg win rate

$50K-$200K

Per proposal

7 in 10

Below 30%

30%

"New normal"

What is P-Win?

The single number that decides whether your next proposal is worth the investment.

The Definition

P-Win (Probability of Win) is a quantitative estimate of how likely your organization is to win a specific government contract.

It drives the highest-stakes decision your BD team makes: invest tens of thousands pursuing an opportunity, or walk away.

The Math

Average competitive win rate: 10-20%
For every 5 proposals, 4 will lose
20 opps/year at 15% win rate = $1M+ wasted annually
Every dollar on a low-probability pursuit is a dollar not spent on a winnable one

P-Win vs P-Go vs PTW

P-GoBD lead

Is this opportunity worth investigating?

P-WinCapture manager

What is our probability of winning?

Price-to-WinPricing analyst

What price point beats competitors?

10 factors that drive your P-Win score

Weights vary by procurement type, but these factors are consistent across Shipley, APMP, and Lohfeld methodologies.

15-30%

Customer Relationship

Depth and recency of engagement with the buying office. Have you shaped the requirement?

10-20%

Competitive Positioning

How you stack up against known competitors on the evaluation criteria that matter most.

15-25%

Technical Solution Fit

Does your proposed solution align with what the customer actually needs?

10-15%

Past Performance

Relevant, recent, and strong CPARS ratings on similar contracts in scope and complexity.

5-10%

Key Personnel

Named personnel with relevant clearances, domain expertise, and availability.

10-15%

Pricing

Price-to-Win analysis shows you can compete on cost without sacrificing margin.

5-10%

Teaming

Subcontractors and partners fill gaps, add past performance, and strengthen position.

5-10%

Compliance Risk

Ability to meet all mandatory requirements, certifications, and clearances.

5-10%

Capture Maturity

How far along is your capture effort? Action plan, win strategy, assigned team.

3-5%

Operational Readiness

Can you perform the work on day one? Staffing, facilities, transition readiness.

P-Win decision thresholds

A P-Win score is only useful if it connects to a decision. These ranges represent industry consensus for bid/no-bid actions.

Below 30%

No-Bid

Walk away unless specific factors can be improved before the RFP drops.

30% - 50%

Conditional Go

Pursue with defined milestones. Re-evaluate P-Win at each gate.

50% - 70%

Full capture and proposal investment. Assign dedicated proposal manager.

Above 70%

Strong Go

Priority pursuit. Commit A-team resources and executive sponsorship.

Common P-Win mistakes

P-Win scoring only works if the inputs are honest and the process is disciplined. Most organizations make the same handful of mistakes.

Optimism bias

Capture managers are incentivized to keep opportunities alive. P-Win scores drift upward through wishful thinking and selective attention to positive signals. Require external validation and track predicted vs actual outcomes.

Confusing capability with past performance

"We could do this" and "we have done this and here is the proof" are scored very differently. Past performance means specific, recent, relevant contracts with CPARS ratings to prove delivery.

Static P-Win scores

A P-Win calculated at opportunity identification and never updated creates false confidence. The competitive landscape changes. Customer priorities shift. Recalculate at every major capture gate.

Same weights for every opportunity

Applying identical factor weights to an LPTA services contract and a best-value R&D procurement produces meaningless scores. Calibrate weights to the specific procurement type and evaluation criteria.

No calibration against outcomes

If you consistently assign 60% P-Win to opportunities you win only 30% of the time, your model is broken. Without calibration, there is no feedback loop, and the model never improves.

The 50% trap

Teams frequently assign "around 50%" because it feels safe. It avoids No-Bid discomfort and Strong Go commitment. If your pipeline clusters at 45-55%, your scoring needs calibration.

How AI changes P-Win

AI does not replace human judgment. It changes the quality and completeness of the inputs that human judgment operates on.

Automated Competitive Intel

Continuously monitor FPDS award data, SAM.gov registrations, and public filings to build competitor profiles that update in real time.

AI Compliance Matrices

Parse solicitation documents, map requirements against your capabilities, and flag compliance gaps before a human touches the analysis.

Historical Pattern Analysis

Analyze years of win/loss data to identify which factors actually predicted wins in your specific competitive environment.

Real-Time Recalculation

P-Win updates continuously as new information enters the system instead of waiting for the next formal gate review.

The market has spoken

pWin.ai raised $10M in seed funding in 2025, co-developed with Shipley Associates. The market has validated that AI-powered P-Win scoring is the future of capture management.

Organizations that adopt data-driven capture intelligence now will have a compounding advantage over teams still running spreadsheet-based scoring.

How Projectory's P-Win Predictor works

Built into the capture workflow, not bolted on. Structured scoring combined with AI-driven analysis aligned with Shipley and APMP methodologies.

Dealbreaker Screening

Binary pass/fail check against mandatory requirements: clearances, set-aside eligibility, geographic presence, certifications, and OCI. Fails flag No-Bid before you invest in detailed scoring.

Competitive Scoring

AI analyzes FPDS histories, SAM.gov data, and your win/loss records to build a competitive landscape. Your position is scored relative to the field, not in isolation.

Composite P-Win Score

Ten factors evaluated with weights calibrated to procurement type. AI data combined with capture team inputs. Output: single P-Win percentage with confidence interval and factor breakdown.

Go/No-Go Decision

P-Win feeds into a structured decision workflow. Reviewers see scores, factor breakdowns, competitive landscape, and risks. Decisions are logged with rationale.

See P-Win Predictor in Action

Frequently asked questions

Common questions about P-Win scoring and capture intelligence.

What is a good P-Win score for a government contract?

A P-Win above 50% is generally considered a strong basis for a Go decision. Scores between 30% and 50% warrant conditional pursuit with clear milestones. Below 30%, most capture teams should default to No-Bid unless specific factors can be improved before proposal submission. The absolute number matters less than how honestly it was calculated and whether it is calibrated against your actual win/loss history.

How is P-Win different from Price-to-Win?

P-Win (Probability of Win) estimates your overall likelihood of winning a contract based on factors like customer relationships, past performance, and competitive positioning. Price-to-Win (PTW) is a pricing analysis that determines the optimal price point to beat competitors while maintaining profitability. PTW is one input to your P-Win score, typically weighted at 10-15%, but it is not the score itself. They are complementary but separate analyses.

Can AI accurately predict P-Win scores?

AI improves P-Win accuracy by analyzing larger datasets, identifying patterns in historical win/loss data, and removing subjective bias from competitive analysis. However, AI works best when combined with human judgment on relationship quality and strategic factors that data alone cannot capture. The goal is augmented decision-making, not fully automated prediction. Organizations using AI-assisted P-Win scoring report better calibration between predicted and actual outcomes over time.

How often should we recalculate P-Win during capture?

P-Win should be recalculated at every major capture milestone: initial opportunity identification, after customer engagement, after draft RFP review, at the bid/no-bid gate, and before final proposal submission. Static P-Win scores calculated once at opportunity identification are one of the most common mistakes in capture management. With AI-powered tools, recalculation can happen continuously as new competitive intelligence and capture activity data enters the system.

Does P-Win scoring work for small businesses?

Yes, but the weight of certain factors shifts. Small businesses competing on set-aside contracts may weight teaming arrangements and socioeconomic status higher, while large-contract past performance carries less weight. The scoring framework is the same; the calibration changes based on your competitive position and the acquisition strategy. Small businesses often benefit more from disciplined P-Win scoring because they have fewer resources to waste on low-probability pursuits.

What data does Projectory use to calculate P-Win?

Projectory analyzes solicitation requirements, your past performance records, competitor award histories from FPDS and SAM.gov, teaming arrangements, compliance alignment, and capture activity maturity. The platform combines structured data analysis with AI-driven pattern matching across historical procurement outcomes to generate a composite P-Win score with a confidence interval and factor-level breakdown.

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See how Projectory's P-Win Predictor turns subjective bid/no-bid decisions into data-driven capture strategy.

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