Platform Alpha: Can Accountancy Platforms Prove the Value They Create?

Private equity has transformed the UK accountancy market. The more interesting question now is whether it can demonstrate how much value that transformation has actually created.

Take a deliberately simple example. A PE-backed platform acquires ten accountancy firms, each with £10m of revenue. Five years later, the combined business generates £180m.

On the face of it, £100m has become £180m. An impressive growth story.

Except those ten firms would not have stood still. Suppose that, left independent, each could reasonably have been expected to grow from £10m to £15m. Collectively, they would now be generating £150m.

The counterfactual will never be precisely knowable. But without attempting to establish one, we risk attributing to the platform growth that the underlying firms might reasonably have achieved anyway.

So the interesting revenue number isn’t £80m. It is the £30m above the estimated standalone trajectory.

Even that isn’t value creation. It is evidence that something different happened.

Suppose the ten independent firms would collectively have reached £150m of revenue and £30m of EBITDA. If the platform also produces £150m of revenue but £40m of EBITDA, there has been no incremental revenue growth at all, yet something economically significant has clearly happened.

Reverse the example. The platform reaches £180m of revenue but still produces only £30m of EBITDA. The additional revenue looks rather less impressive.

This is where platform alpha becomes useful: the incremental economic value created because businesses belong to a particular platform, relative to the value they could reasonably have generated independently.

The components are familiar. Cross-selling, pricing, technology, AI, advisory growth, talent and cost efficiency feature in most value-creation plans. Sophisticated investors already measure many of them.

The harder question is whether those individual measures are being connected consistently enough, from the point of acquisition, to demonstrate what the platform itself has added.

Consider a firm acquired at £10m of revenue and £2m of EBITDA.

At acquisition, there will have been extensive diligence, forecasts and an investment case. Three or four years later the firm may have been absorbed into a much larger organisation. Its systems have changed, clients have moved between service lines, people have transferred and central costs have been absorbed elsewhere.

The platform knows what the group has become. But can it still establish what this particular firm might reasonably have looked like had it remained independent, and what happened differently because it joined the platform?

If that analysis has to be reconstructed immediately before exit, attribution inevitably becomes harder.

There is therefore an argument for establishing a platform-alpha baseline at acquisition.

Capture the firm’s revenue, profitability, margins, clients, service mix, people economics and other relevant measures. Establish a credible standalone trajectory. Then track what happens after acquisition and, importantly, why.

If cross-selling is part of the investment case, measure the incremental revenue and profit generated from services introduced through the platform. If technology releases capacity, track where that capacity goes and what economic return follows. If pricing improves, separate that effect as far as possible from general market price inflation. If margins rise, understand whether that came from productivity, cost removal, service mix or simply stronger market conditions.

None of this requires pretending that a counterfactual can be measured with scientific precision. It cannot. But a consistently constructed estimate, established contemporaneously and updated against observable evidence, is likely to be considerably more useful than one recreated several years later to support an exit narrative.

It could also change behaviour during ownership.

After 20 or 30 acquisitions, a platform should have accumulated a substantial body of evidence about what happens after firms enter the group. Which types of firm consistently outperform their estimated standalone trajectory? Which interventions actually work, and how quickly? Which integration investments produce the best returns? Where has the platform generated negative alpha through bureaucracy, partner departures, poor integration or damaged client relationships?

That evidence should influence what gets bought next. It should also inform whether the next £10m is better spent acquiring another practice or improving the businesses already owned.

It should make some uncomfortable conversations easier too. AI adoption isn’t value creation simply because AI has been deployed. Cross-selling isn’t successful because examples of collaboration can be found. Integration isn’t complete because everyone operates under the same brand.

The economic consequences are what matter.

The greatest relevance may come at the secondary buyout, where the first owner’s claims about value creation have to be priced by someone else.

The seller wants to be paid for having created a better business. The buyer needs to establish how much better it really is.

For the seller, acquisition-level evidence can provide a bridge from the businesses originally acquired, through their estimated standalone trajectories, to their actual economic performance inside the platform. Rather than asserting that integration, technology and commercialisation have created value, the vendor can demonstrate where that value arose and whether similar effects occurred across successive acquisition cohorts.

For the buyer, the same analysis serves a different purpose.

Some platform alpha will already be captured in current earnings and therefore reflected in the purchase price. Some will have arisen from one-off actions that cannot be repeated. Other elements may reveal capabilities that continue to generate value.

Removing duplicated overhead can create significant value for the first owner, but the second owner cannot remove it again. A cross-selling model that has repeatedly increased profitable wallet share across successive acquisition cohorts is different. So too might be a technology platform that continues improving productivity as more clients and workflows migrate onto it.

The secondary-buyout risk is otherwise obvious: paying the seller for value already created while underwriting the same value-creation levers again as future upside.

This is why platform alpha may be less a new financial metric than a measurement discipline.

Much of the underlying data already exists. The opportunity is to connect the original acquisition case, the standalone counterfactual, subsequent interventions and resulting economic performance consistently from entry through to exit.

For the existing owner, that should lead to better acquisition, integration and capital-allocation decisions — and better evidence of value creation when the time comes to sell.

For the next owner, it answers a rather different question: how much of the value in front of me has already been created and paid for, and how much evidence is there that this platform can create more?