Win/Loss Intelligence
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Win/loss intelligence is the structured analysis of pursuit outcomes, drawn from debrief notes, evaluator scoring, team retrospectives, and competitive patterns, to improve how a firm selects, positions, and executes future pursuits.
Why Most AEC Firms Collect Data But Build No Intelligence
A debrief call produces raw information; win/loss intelligence requires connecting that call to a pattern across a dozen pursuits. Most firms have scattered debrief notes living in email threads or a shared drive folder nobody opens. The gap is synthesis: which evaluator concerns recur, which fee positions lose shortlists, which project types the firm consistently undersells in Section E of the SF-330. Without a system for aggregating outcomes against pursuit variables, a firm cannot distinguish a one-time miss from a structural weakness in its positioning.
What the Data Actually Needs to Capture
Useful win/loss records go beyond win rate by client type. They track the competitive set named in debriefs, the scoring criteria weights published in the RFP, the project manager proposed, the narrative approach taken, and the fee spread when it becomes available. Federal procurement under the Brooks Act requires qualifications-based selection, so price is off the table in QBS evaluations; that means scoring differentials on SF-330 sections and interview performance are the only signal available. A firm that tracks those differentials over 18 months starts to see which staff assignments, which project analogues, and which client relationships actually move scores.
Turning Outcomes Into a Pursuit Asset
Win/loss intelligence has no operational value sitting in a spreadsheet a BD director updates twice a year. It becomes useful when it surfaces at the moment a pursuit team is deciding whether to pursue, how to position, and who to propose. A firm that lost three consecutive shortlists to the same competitor on a transit agency's criteria sheet should know that before it writes a go/no-go recommendation, not after it submits. Kantiv connects historical pursuit outcomes to active pursuits, so the context from past evaluations reaches the team making decisions now, rather than staying buried in files from the last pursuit cycle.
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