An award notice that names only the winner answers one question: who got it. A full bidder table answers a dozen more, and they are the questions that decide whether your next bid is worth writing. How many companies fight over this category? How far apart were the prices? Who keeps showing up together? When does this contract come back?
This post walks through four analyses the whole table makes possible. The figures are round numbers chosen for the example; the point is the method.
The anatomy of a bidder table
A bidder table lists every company that bid, each one's commercial registration (CR) number, the bid amount, the awarded value, and often a local-content ratio: the share of the bid's value delivered through the domestic economy. One tender, one table, every price visible.
A facilities-maintenance tender, seven bidders
| Bidder | Bid amount (USD) | Local content | Result |
|---|---|---|---|
| Bidder A | 1,840,000 | 41% | Awarded |
| Bidder B | 1,975,000 | 38% | |
| Bidder C | 2,120,000 | 55% | |
| Bidder D | 2,310,000 | 29% | |
| Bidder E | 2,490,000 | 61% | |
| Bidder F | 2,760,000 | 33% | |
| Bidder G | 3,150,000 | 47% |
Seven prices for the same scope. Read alone, this table settles one tender. Read across hundreds of tenders, tables like it settle strategy.
Competition density: how crowded is your category
The simplest aggregate is a count: bidders per tender, averaged by category. It is also the most strategically loaded, because it is a direct measure of how contested a market is.
Suppose that over two years in one market, office IT supply tenders averaged 9.4 bidders while marine dredging averaged 2.8. The uniform-odds baseline says a capable newcomer wins roughly 1 in 9 IT tenders and better than 1 in 3 dredging tenders. Densely contested categories compress margins and reward cost discipline; thin ones reward showing up qualified. Neither is better in the abstract, but they demand different companies.
Density also moves, and the direction is the signal. A category drifting from 4 bidders to 8 over two years is commoditising: expect price pressure and rising local-content bars. One drifting from 8 to 4 is consolidating or specialising, and the remaining bidders are telling you why.
Spread: what the gaps between bids say
- Money on the table. Bidder A won at 1,840,000 with the runner-up at 1,975,000: a gap of 135,000, about 7 percent of the winning price. That is a tight, well-judged win. A company that wins repeatedly with 20 or 30 percent of daylight below the runner-up is underpricing, and the table is the only place it would ever find out.
- Category maturity. The median bid here, 2,310,000, sits about 26 percent above the winner. A cluster this loose is normal for services with judgment in the scoping. When most of a category's bids land within a few percent of each other, the work has commoditised and the award turns on cost structure or the technical file.
- Specification ambiguity. The top bid, 3,150,000, is 71 percent above the winner. When the whole range stretches that wide, tender after tender, bidders are reading the scope differently. For a bid team, wide historical spreads mean clarification questions are where the margin is.
Aggregated per category, these become spread percentiles: the same discipline benchmarks bring to salaries, applied to public prices.
Co-bidder lists: who shows up together
Every bidder table is also a co-occurrence record: a list of companies that chose the same opportunity at the same time. Stack two years of tables and count, for each company, who else appeared alongside it, and a structure emerges that no company register shows. Firms cluster by niche. Some pairs meet in nearly every table in a category; they are each other's true competition, whatever their marketing says.
The practical use is partner discovery. Say the tables show you placing second, three times, behind bidders with materially higher local-content ratios: the same tables tell you which companies have the local base you lack, which of them bid your categories, and which are absent from them and might team rather than compete. A co-bidder list turns "who could we partner with?" from a coffee-circuit question into a query.
Recompete prediction: when the contract comes back
Public contracts expire on schedules, and the bidder table from the last cycle is most of the briefing for the next one. Take a two-year facilities contract awarded in June 2025. It returns to market around mid 2027, with the recompete typically published some months ahead. From the old table you already know the incumbent, the price that won, the full set of companies likely to bid again, and how much room there was between them.
That converts business development from monitoring to scheduling. Instead of waiting for a notice, you work backwards from expiry: relationship-building and past-performance evidence two quarters out, teaming decisions one quarter out, pricing informed by the last cycle's spread. Incumbents defend recompetes hard, but they lose them predictably when density is rising and their winning margin was thin. The timing patterns get their own treatment in the Academy lesson on recompete timing.
Reading aggregates with the sample in view
An average over six tenders in a thin category is a direction, not a decimal; treat it as "few bidders, wide spreads" and go check the individual tables. Aggregates earn their precision in categories with real volume, which is why benchmarks read best by buyer and by market, where the volume is.
This is the analysis layer tender intelligence is built for: full bidder tables on every tender profile, bid benchmarks with bidder-count histograms and spread percentiles by buyer or market, and a recompete radar that works the expiry math for you. If you want to practise on a single table first, start with the Academy lesson on reading a bid table.