A 200-Person Firm Found a 23% Win Rate on One Client Type and 4% on Another

Types of Architecture — Styles and Movements

Architecture falls into dozens of recognized styles— from Classical antiquity to contemporary Deconstructivism— each defined by its structural logic, aesthetic principles, and historical period.

The main historical types include:

• Classical — Greek and Roman origins: colonnades, symmetry, proportion. The Parthenon. The Roman Pantheon. Rules that held for centuries.
• Gothic — Pointed arches, ribbed vaulting, flying buttresses pushing height and light into cathedrals. Notre-Dame. Chartres Cathedral.
• Renaissance — A humanist revival of classical forms. Brunelleschi's dome over Florence. Palazzo Farnese in Rome. Proportion as philosophy.
• Modernism — "Form follows function." Steel and glass. Ornament rejected outright. Mies van der Rohe. Le Corbusier. The past treated as constraint, not guide.
• Contemporary/Postmodern — Pluralism, digital fabrication, sustainable design. Zaha Hadid's fluid forms. Bjarke Ingels's climate-responsive buildings.

Notable offshoots worth naming: Brutalism (raw concrete as civic statement), Art Deco (geometric decoration as glamour), Deconstructivism (questioning structure itself as a design move).

These are the types of architecture that define the built environment. But they're not the types that define your firm's revenue.

When architects and firm leaders talk about "type of architecture," they usually mean something more operational: the market sectors a practice serves.

Types of Architecture Practice — What Firms Actually Do

The eight primary types of architectural practice are residential, commercial, healthcare, institutional, industrial, landscape, government/civic, and interior architecture.² Each shapes how a firm recruits, prices, and wins work.

That 55% institutional figure is worth pausing on. Per the 2024 AIA Firm Survey³, healthcare, education, and civic projects together represent the majority of U.S. architecture billing— whether firms planned it that way or not. Institutional work has been the industry's gravitational center for decades. Most practices concentrate here by inertia as much as strategy.

Specialization compounds the advantage. According to Monograph⁴, "a clear niche improves SEO, messaging, referrals, proposals, and lead quality"— effectively multiplying the return on every BD dollar spent in that sector.

Most firms have a primary sector. Fewer know their win rate within that sector— or how it varies by client type.

Knowing your practice type is table stakes. But knowing your win rate within that type is where the real leverage lives.

The Question Most Firms Stop Asking After Year Two

Most architecture firms know their win rate as a single number. Very few segment it by client type— and that's where the strategic gap lives.

The industry data is clear on this. Only 40% of AEC firms use a formal go/no-go decision process⁵— meaning 60% are pursuing work without a systematic filter. Pre-scoring go/no-go with AI extends that filter, but only if the criteria exist first. Most have no infrastructure to know which client types they're actually competitive for. Nearly half of firms struggle with BD platform adoption altogether.⁵

"Firms should segment tracking by project type and client relationship before tracking individual performance. Learning that you win 80% of repeat client work but only 20% of new commercial projects reveals more valuable insights than knowing which business development person has the best numbers." — Monograph⁶

The underlying problem isn't effort. It's data infrastructure. Most CRMs are either unused, unsegmented, or disconnected from proposal outcomes. The information to answer the win-rate-by-client-type question exists somewhere in a firm's history. It just hasn't been organized.

Return to the opening scenario. A 200-person firm with a 23% win rate on institutional work and a 4% win rate on speculative commercial pitches— this mirrors what win/loss analytics often reveals when firms finally look. Both client types appear in the portfolio. The data says they are not the same pursuit. For firms building an AI strategy for AEC, win-rate data by client type is the foundation the analytics layer needs. But even without AI tools, manual segmentation is where this starts.

The industry data makes a clear case for why this analysis matters— and the math is harder to ignore than most BD teams realize.

What the Data Actually Shows

The industry's own data draws a clear picture: architecture firms that pursue selectively win more and spend less per dollar of revenue— and the gap between the best and worst performers is widening.

Here's how win rates break down across the industry:

And the spread within a single firm can be just as dramatic.

The SMPS Foundation— the authoritative body for measuring AEC hit rates, established through a study of 303 U.S.-based firms— set the baseline at 37-44% in 2016.¹ That number hasn't shifted much for firms on legacy processes. What has shifted is the ceiling.

The spread between repeat and new client win rates is the clearest documented proxy for sector-level variation. It's a 2.5 to 3x difference in win probability from a single variable: whether the client knows your work. Institutional clients— who represent 55% of billings and make procurement decisions based heavily on demonstrated track record— behave like repeat-client-style decisions even on open competitions.

In practical terms, the overhead math is unforgiving. Based on typical net multipliers of 2.94-3.54x, every 100 hours spent on a losing proposal requires 300-350 billable hours to recover overhead costs, according to Monograph¹⁰. Firms chasing 4%-win-rate commercial developer pitches while their institutional practice wins at 23% are spending three billable hours to recover every lost hour. The numbers don't work.

The shift is already happening. According to Deltek's 46th Annual Clarity Report⁸, firms submitted 38% fewer proposals in 2025 while winning 52% more in total awarded value. Selective bidding isn't a future strategy. It's what leading firms did last year. But selective bidding without the underlying data is just fewer bets, not smarter ones.

The performance gap is widening too. 27% of tech-forward firms expect win rates of 75-100%, compared to just 13% of tech-static firms— roughly twice the rate.⁶ The question isn't whether win-rate analytics by client type is worth doing— the data settles that. The question is how to start.

How to Build Your Win-Rate Map by Client Type

Building a win-rate map by client type doesn't require a new system— it requires segmenting the data you already have. Most firms can do a first pass in a half-day using existing proposal records.

1. Pull last 3-5 years of proposals — Email archives, Deltek, a spreadsheet. Wherever they live. The goal is a complete list of what you pursued and whether you won.
2. Tag each pursuit by client type — Use the 8-category taxonomy from Section 3. If a project spans types, tag by the primary client relationship.
3. Calculate win rate per category — (Wins ÷ Total pursuits) × 100. Do this separately for each client type. Keep repeat and new client pursuits separate within each category.
4. Rank categories by win rate — This is your firm's actual competitive profile. Not what you believe it to be. What the data shows.
5. Compare categories by pursuit cost — Rough estimate: average hours per submission. Multiply the losing proposals in each category by 2.94-3.54x to see the billable recovery burden per missed win.

And none of this requires buying new software first.

What you find will almost certainly surprise you. Most firms that do this exercise discover their actual competitive profile looks different from the one they'd describe.

What to do with this data: raise the go/no-go bar for categories where you win less than 20%. Shift BD investment toward categories where referrals already exist and your win rate reflects it. For tracking AI success metrics, the win rate by client type becomes the baseline you're trying to move.

Apply an AI decision framework to your go/no-go criteria once you have this data. The framework needs the historical record to be useful.

Tools that support this work: Monograph (purpose-built for architecture practices), Deltek Clarity, and ProjectMark are the primary BD platforms designed for AEC. The first pass doesn't require any of them. A spreadsheet and a half-day are the entry point.

The proof is in the outcomes. One structural engineering firm improved its win rate from 34% to 78% after implementing organized proposal tracking and automation— and delivered designs 52% faster in the process, according to a case study published by Monograph.¹⁰ The win-rate gain came from better pursuit selection, not better writing.

Manual win-rate tracking gets you the data. AI tools increasingly automate what happens next.

AI-Powered Proposal Analytics — What Leading Firms Are Doing Now

More than half of A&E firms now use AI in business development— and the firms doing it well aren't using AI to write proposals faster, they're using it to decide which proposals to write at all.¹¹

The shift is directional. AI in AEC BD used to mean speed. Now leading firms use it for opportunity scoring, lead triage, and win-probability estimation before the go/no-go meeting. Three tool categories are shaping this:

Purpose-built AEC platforms (Monograph, Deltek, ProjectMark) analyze past wins and losses by client type, procurement method, geography, and relationship strength to score new opportunities before resources are committed. Integrated CRM tools (Unanet, Microsoft Dynamics with aec360) connect pursuit history to live pipeline data, giving BD directors a single view of active pursuits and historical hit rates. AI-native lead scoring is emerging at larger firms— still early, but clearly the direction of travel.

The outcome is measurable. Firms integrating AI in BD hit a 50% median win rate in 2025⁸, compared to the 37-44% baseline for firms still operating without data tools.¹ That gap is attributable to better pursuit selection— not better proposal writing.

But here's the honest caveat: AI amplifies good data. If your firm's past win/loss records aren't tagged by client type, the model has nothing to work with. The segmentation in the previous section isn't prep work. It's the foundation.

FAQ

What is a good win rate for an architecture firm?

The industry average falls between 37% and 50%, depending on firm size, specialization, and use of data tools. Firms using proposal software hit approximately 45%⁷; firms integrating AI into BD reached a 50% median in 2025.⁸ According to Monograph⁶, a win rate below 46.5% signals significant opportunity cost from poor pursuit selection— meaning those firms are spending more resources per won dollar than they need to.

What are the main types of architecture?

Architecture includes both historical styles (Classical, Gothic, Renaissance, Modernist, Contemporary) and practice specializations (residential, commercial, healthcare, institutional, industrial, landscape, government/civic, interior).² Most firms operate primarily in 1-3 specializations. The distinction matters strategically: styles describe how a building looks; practice types describe how a firm gets work— and which client types it's positioned to win.

What is the go/no-go process in architecture?

Go/no-go is a formal decision process to evaluate whether to pursue a project based on fit, relationship strength, win probability, and resource availability. Only 40% of AEC firms use a formal version⁵— meaning most firms pursue work without a systematic filter for which client types they're actually competitive for. The firms using formal go/no-go processes are the same firms trending toward the 50% median win rate.

What percentage of architecture firm work comes from repeat clients?

Architecture firms derive 75-85% of their business from repeat clients and referrals, with only about 20% from competitive new client wins.⁴ Repeat client win rates exceed 75%⁹, compared to 25-30% for new client pursuits— making client retention the highest-return BD investment for most firms. This differential is also why institutional specialization compounds over time: each won project seeds the next.

Conclusion

The type of architecture your firm practices is on your website. The type you actually win is in your data.

The 200-person firm in the opening scenario didn't change what it does. It changed what it pursues. Discovering that its institutional win rate was 23%— and its speculative commercial win rate was 4%— made the BD reallocation obvious. No rebrand required. But it does require looking at the data.

If you haven't segmented your win rate by client type, that analysis is this week's project. The data is almost certainly already in your system.

If navigating the data side is the hard part— knowing which numbers to pull, how to interpret the gaps, and where AI tools can accelerate the process from there— that's exactly where an outside perspective helps. Dan Cumberland Labs works with AEC firms on strategic AI implementation for AEC firms.

References

1. SMPS Foundation / Southern Illinois University Edwardsville, "Measuring for Success: A Look at Hit Rates & Other KPIs in the A/E/C Industries" (2016) — https://www.smps.org/2016-hit-rate-report/
2. ArchLogBook, "Types of Architectural Practices" (2024) — https://docs.archlogbook.co/01-industry-basics/types-of-architectural-practices
3. American Institute of Architects, "AIA Firm Survey Report 2024" (2024) — https://www.aia.org/resource-center/aia-firm-survey-report-2024
4. Monograph, "How Top Architecture Firms Fill Their Project Pipeline" (2024) — https://monograph.com/blog/how-architecture-firms-fill-project-pipeline
5. Unanet, "Half the Battle: Why AEC Firms Are Only Winning 50% of Bids" (2025) — https://unanet.com/blog/half-the-battle-why-aec-firms-are-only-winning-50-of-bids
6. Monograph, "How to Calculate Win-Loss Percentage (With Examples)" (2024) — https://monograph.com/blog/how-to-calculate-win-loss-percentage-with-examples
7. OpenAsset, "100+ Architecture Statistics for 2024: Trends, Technology & More" (2024) — https://openasset.com/resources/architecture-statistics/
8. Deltek, "46th Annual Deltek Clarity Architecture & Engineering Industry Study — 2025 Press Release" (2025) — https://www.deltek.com/en/about/media-center/press-releases/2025/what-the-46th-annual-deltek-clarity-ae-study-reveals-about-the-industry
9. UNCOMMON Architects, "How Architecture Firms Win Clients — From Lead Gen to Retention" (2025) — https://uncommonarchitects.com/blog/how-architecture-firms-win-clients-from-lead-gen-to-retention/
10. Monograph, "Proposal Tracking Systems: A Complete Guide for A&E Firms" (2025) — https://monograph.com/blog/proposal-tracking-systems-complete-guide-ae-firms
11. OpenAsset, "Understanding the AEC Industry + Top 17 AEC Trends for 2025" (2025) — https://openasset.com/resources/aec-industry-trends/