Buy-and-build: the five levers that make the second deal cheaper than the first

What the return data says

The only public return study of buy-and-build by number of add-ons is old, small and European, and it is still the best there is. BCG and HHL Leipzig, on 121 exited deals of which 48 had add-ons, found buy-and-build averaging a 31.6 percent IRR against 23.1 percent for standalone buyouts; but deals with one or two add-ons averaged 35.5 percent and deals with more than two averaged 19.9 percent, below a plain buyout. Small platforms (under $70 million enterprise value) earned 52.4 percent; large ones (over $290 million) earned 12.5 percent.¹ The pattern is consistent with integration capacity being the binding constraint, and it is the number behind B5.

Two cautions from the current cycle sit alongside it. A home-improvement roll-up backed by a private-equity sponsor and financed by private credit ceased operations in October 2025 and liquidated under Chapter 7 with more than $100 million of debt against under $10 million of assets; the reported causes were borrowing costs, demand and labor costs, not integration.² It is the leverage precedent, not the pacing one. And the one acquisition-led platform that reports organic growth separately showed 3 percent organic against 126 percent total in its most recent quarter, with reorganisation costs every quarter and interest expense tripling.³ A buyer at exit will do that decomposition to this platform too.

B1. Target sourcing and screening

What it is. A pipeline of services firms scored on P1 to P6 before a conversation starts: codifiable share (from the firm's service mix, estimated), pricing structure (from how the sector bills), relationship concentration (from firm size and partner count), regulatory ceiling (from the sector), platform fit (from the platform's existing frameworks), and the seller's motive (succession, most often). The output is a ranked list in which the next deal is the one that converts fastest, not the one that is largest or cheapest.

Line it moves. Price paid (a scored pipeline means fewer auctions) and conversion speed.

Typical range and evidence. The field is crowded, and the crowding is the evidence: more than $4 billion of dedicated capital, vehicles owning 70-plus firms, and a professional-services deal count of 198 in the first half of 2026.⁴ In accounting, roughly a thousand firms are reported to be under private-equity influence, with one direct deal generating seven add-ons on average.⁵ Grade: Medium (market data); the scoring is the firm's own.

How to run it. Score the sector first (archetype), then the firm. Prioritise firms whose work maps to frameworks the platform already runs (B3), because the hundred-day plan (B2) then has nothing to build. Walk away from auctions; the platform's edge is conversion speed, not price.

Kill criteria. Entry price above P7's ceiling on two consecutive targets (the thesis is crowded in that segment; move segments or wait).

Metrics. Pipeline scored; time from first contact to close; entry multiple against the ceiling.

Horizon and stage. Deploy; diligence.

B2. Conversion-ready integration playbook

What it is. The hundred-day plan (P8) as a template: the migration scripts, the gateway configuration, the baseline definitions, the register, the sampling plans, the engagement-letter templates, the compensation formula. Each add-on runs the template with its own data; each add-on's method (D4) is added to the library. The metric is the time from close to the first platform-delivered unit, and it should fall with every deal.

Line it moves. Integration cost; time to margin for each add-on.

Typical range and evidence. No public data on integration cost or time for converted services firms. The structural warning from practitioners is that integration debt compounds at the third add-on if the first two were not finished, which is the BCG/HHL pattern described from the inside.⁶ Grade: Weak (no data); the firm's own time-to-first-unit is the measure.

How to run it. Write the template during the first conversion, as it happens, not afterwards. Name an integration lead whose only job is add-ons, before the second deal. Measure time-to-first-platform-unit and time-to-pricing-conversion per add-on and publish them.

Kill criteria. Time to first platform-delivered unit not falling between the first and second add-on.

Metrics. Per add-on: days to first platform unit; days to first pricing conversion; integration cost as a share of deal value.

Horizon and stage. Convert; years one to three.

B3. Platform standardization

What it is. One data model, one framework library, one client record across all acquired firms. Without it, R9 (cross-sell) is a referral between partners who do not know each other, O8 (shared services) is two integrations at once, and the platform's learning (D1's error data, D4's frameworks) is siloed by firm. With it, the third firm converts onto a platform that already knows most of its work.

Line it moves. Enables R9 and O8; reduces each add-on's D4 cost.

Typical range and evidence. Argued; the mechanism is the platform thesis itself. Grade: Weak (argued); Strong as a precondition for R9 and O8.

How to run it. The first firm's conversion sets the data model. The second firm is migrated into it, not alongside it, even where that is slower. Framework variants are versioned by firm within one library rather than forked.

Kill criteria. A second data model or client record in production after the second add-on's first year.

Metrics. Year two onward: share of group revenue on one client record; framework reuse rate across firms.

Horizon and stage. Convert; years one to two.

B4. Continuous diligence on add-ons

What it is. The platform's own data informing the next deal's price. Once the first firm is converted, the platform knows the conversion ceiling of its kind of work, the error rates by unit type, the pricing clients accept, the retention through conversion. The next target in the same segment is diligenced against that, not against a model. Carried from the software edition's continuous-diligence lever with its Weak grade: argued, not measured.⁷

Line it moves. Price paid on add-ons.

Grade. Weak, carried.

How to run it. P1's unit table for each target is pre-populated from the platform's data on comparable firms. P6's fit assessment is a lookup, not a project. P7's model uses the group's actual outcomes as the base case.

Metrics. Per add-on: diligence cost and days; variance between modelled and actual conversion at year one.

Horizon and stage. Compound; year one to exit.

B5. Pacing and the roll-up kill criteria

What it is. How many conversions the operating team runs at once, and the written conditions under which the sponsor stops acquiring and runs what it holds. The BCG/HHL finding is the number: past two add-ons, average returns fall below a plain buyout.¹ The reason is capacity: the same people who convert the first firm are needed for the second, and a third started before the first is finished is converted by nobody.

Line it moves. Fund-level risk; the return on the whole program.

Typical range and evidence. Strong (BCG/HHL), with its sample caveats. Grade: Strong (the one return study) / the firm's own capacity is the constraint.

How to run it. Set the pacing rule before the second deal: no add-on starts until the previous one has reached its conversion milestone (R2 live, pricing converted on the renewing book, D3 at statistical sampling). Set the kill criteria for the roll-up itself in writing: entry prices above the ceiling, a conversion milestone missed by more than two quarters, group organic growth below the first firm's standalone, named-person retention below 90 percent at any firm. The board reviews them with the same register discipline as every other lever (O9).

Kill criteria. These are the kill criteria. The lever is the discipline of having them.

Metrics. Add-ons in conversion at once; conversion milestones met on schedule; group organic growth versus acquired.

Horizon and stage. Convert; year one to exit.