Borrowed answers and first principles: rethinking value creation in private equity

Private equity returns now depend more on what happens inside the portfolio company. For buyouts entered in 2010 or later and exited by 2021, McKinsey attributes roughly two-thirds of the total return to market multiple expansion and leverage. Since then, multiples have fallen and financing costs have risen, so revenue growth and margin expansion have taken centre stage (McKinsey, 2024). In "The equity story is now load-bearing", we looked at what that shift means for the exit narrative, with deals now needing 10 to 12% annual EBITDA growth to return 2.5 times their money (Bain, 2026). This article looks at how the plan that delivers that growth is built.

Value creation plans are often assembled by analogy, and by analogy I mean from a previous deal's industry-specific playbook, a set of comparable companies and a standard reporting pack, each carrying assumptions formed in a different business. Whilst this can work, there is the possibility of leaving money on the table at exit.

First principles thinking starts instead from what has to be true for the investment case to work (think drivers in a model, for instance), and then builds the plan, the KPIs, and the reporting up from there. The approach suits the lower to mid-market in particular, where finance teams are typically smaller and have little capacity for reporting the deal does not necessarily need.

What is first principles thinking?

Going back in time, Aristotle wrote that we only know a thing once we have traced it back to its first principles and carried the analysis down to its simplest elements (Aristotle, Physics). Reasoning by analogy works the other way, by finding a similar case and borrowing its answer.

More recently, the idea has drawn wide attention through Elon Musk, who has called reasoning by analogy "the normal way we conduct our lives", in that we often find ourselves defaulting to what we know works.

He argued for breaking a problem down to "the most fundamental truths". In his most-quoted example, he priced the materials a rocket is made of and found they accounted for about two percent of its typical price, so most of the cost came from how rockets had always been designed, built, and bought (Parrish, 2018).

Analogy versus first principles

By analogy

1. Find a similar case
2. Borrow its answer
3. Adjust at the edges

Rocket example: price a new rocket like the last one

First principles

1. Break it down
2. Keep what is true
3. Rebuild the answer

Rocket example: list the materials, price them, rebuild the cost

Both routes start from the same problem; however, the second tests each part of the answer before it is used.

Analogy is a sensible option, but first principles thinking checks whether a borrowed answer still holds for the business.

Where private equity reasons by analogy

Private equity, for the most part, relies on analogy for good reasons. Deal teams work to tight timelines and arrive with a playbook from earlier deals, which lets them move quickly, but a playbook only fits when the new processes and data somewhat resemble those it was written for.

I clearly remember the following example from a past engagement: The operating partner, a newly appointed chief executive, and the existing management team each brought their own list of KPIs, drawn from deal knowledge (PE operating partner), domain knowledge (CEO), and institutional knowledge (management). The combined list was long (close to 50 KPIs) and misaligned, and it quickly became obvious that the larger problem was that nobody could say with confidence which KPIs the company's data and processes could support. This is a clear example of how KPI packs tend to grow by addition.

Three lists of KPIs combine into one long, misaligned wishlist

• PE operating partnerDeal knowledge
• Newly appointed CEODomain knowledge
• Existing managementInstitutional knowledge

KPI wishlist

Long and misaligned

Weak execution

Slow, manual reporting

Reliance on a few data owners

EY's 2026 exit readiness study found that 60% of GPs named building a reliable set of data and KPIs as their hardest finance-function issue, clearly evidencing the problem in the real-life case I noted above. The study observes that "There is often a disconnect between internal performance tracking and the level of detail buyers expect to be able to underwrite the equity story" (EY, 2026).

An equity story is defensible at exit when the KPIs behind it have been tracked reliably through the hold.

Breaking down the investment case

To put it simply, equity value at exit equals adjusted/normalised EBITDA multiplied by an exit multiple (this gives us an enterprise value), less net debt, plus or minus a working capital adjustment. With little help from the multiple and leverage nowadays, most of the value that can be created sits in EBITDA and optimising working capital.

The deal model turns the equity story into numbers; each line of the model should trace back to drivers, and each driver needs a KPI with:

• a written definition;
• a data source;
• a reliable process behind it; and
• an owner.

Visually, it can be mapped out as follows (an example):

Equity value at exit traces back to the KPIs behind EBITDA

1. Normalised EBITDA
2. ×Exit multiple
3. =Enterprise value
4. −Net debt
5. ±Working capital adjustment
6. =Equity value

Deal model

• Retention
• Price
• New customers
• Gross margin

Value drivers in the model

KPI

e.g. gross revenue retention

Each KPI needs

• Definition
• Data source
• Process
• Owner

Worked example: £8m to £14m of EBITDA

Consider a sponsor that buys a UK business services company with recurring, contracted revenue and £8m of EBITDA and plans to reach £14m by year five. The bridge below splits the £6m uplift into four drivers and marks how ready each driver's KPI is.

Only £1.0m of the planned £6.0m EBITDA uplift can be measured on day 30

Illustrative deal model for a UK business services company, £m

• Ready: KPI can be reported now
• Build: data exists, needs work
• Capture: data not yet recorded

1. Entry EBITDA£8.0m
2. Gross margin+£1.0m, Ready
3. Retention+£2.0m, Build
4. New customers+£1.5m, Build
5. Price increases+£1.5m, Capture
6. Year 5 EBITDA£14.0m

Driver	KPI	Readiness	Why
Gross margin	Gross margin by service line	Ready	Already reported in the management accounts on various cuts and dimensions, and reconciles to the ledger
Retention	Gross revenue retention	Build	The CRM and billing systems use different customer IDs
New customers	Revenue from new customers	Build	The sales team uses CRM stages inconsistently
Price increases	Price realisation at renewal	Capture	Billing records what each customer pays but not why the amount changed

• Gross margin

  Ready

  KPI

  Gross margin by service line

  Why

  Already reported in the management accounts on various cuts and dimensions, and reconciles to the ledger

• Retention

  Build

  KPI

  Gross revenue retention

  Why

  The CRM and billing systems use different customer IDs

• New customers

  Build

  KPI

  Revenue from new customers

  Why

  The sales team uses CRM stages inconsistently

• Price increases

  Capture

  KPI

  Price realisation at renewal

  Why

  Billing records what each customer pays but not why the amount changed

Rebuilding from what is ready

The second half of the method is to rebuild from what is known to be true. I'm of the opinion that a KPI blueprinting exercise early in the hold puts the rest of the investment life cycle, and the value creation work within it, in a strong position. For each KPI on the wishlist, the sponsor/operating partner, management and their advisors answer five questions.

1. Which line of the deal model does this KPI evidence, and how much value relies on it?
2. Is it defined in writing, in terms a future buyer would accept?
3. Does the data exist, and in which systems?
4. Which process produces the data, and does it run reliably?
5. Who owns the KPI?

Asking why a KPI is wanted until the answer reaches a line in the deal model, a version of the "five whys" technique (Parrish, 2018), filters the list, and the KPIs that remain are ordered by value and readiness. KPIs that are ready are reported straight away; KPIs with usable data go into a build plan; and KPIs that need new data start with a process change. Lower priorities stay on the list with a recorded reason and return once the underlying process and data remediation work is done.

Blueprinting restacks the wishlist into an order of work

KPI wishlist

Unranked

KPI blueprinting

• 1. Linked to the deal model
• 2. Defined in writing
• 3. Data exists
• 4. Process runs reliably
• 5. Has a named owner

• Report now
• Build next
• Capture data first
• Parked with a reason

Requested byPE operating partnerNew CEOExisting management

We describe this order of work as the path of least resistance, and automation comes last in it. Automating a reporting process before it has been reviewed and fixed produces wrong numbers faster.

Signal Hill's application of first principles to value creation

Signal Hill Value Partners is building Kloof, a value creation management platform, around this approach.

"Kloof" is a South African word for a gorge cut by water following the path of least resistance, and the platform applies the same first principles logic to value creation. It serves multiple parties in a value creation plan:

• Investors (sponsors and deal teams), who need a consistent view of progress against the investment thesis and equity story;
• Operators (management teams), who agree on the KPIs that create value and own the monthly reporting behind them; and
• Advisors (diligence, value creation and deal advisory teams), who produce evidence-based analysis, especially towards exit.

All three work from one shared data foundation, scoped specifically to the deal (ideally, directly to the equity story).

Each step feeds one shared data layer, from pre-acquisition to exit

One shared data layer for investors, operators and advisors

Built up over the hold

1. Pre-acquisition

   • Equity story alignment

2. Day 1 to 100

   • KPI blueprinting
   • Reporting diagnostic

3. Hold

   Reporting automation

   • Priority area 01
   • Priority area 02
   • Priority area 03

Exit

Sell-side evidence

The work starts before acquisition, by aligning the equity story with the KPIs that will defend it. In the first 100 days, KPI blueprinting sets the priorities and a reporting diagnostic finds the gaps in how those KPIs are reported. Reporting automation follows for the KPIs that are ready, and throughout the hold, each priority area adds to a deal-ready data layer that supplies the evidence for sell-side documents at exit.

Conclusion

A first principles approach keeps value creation simple and sequenced. It starts from the deal model (potentially even earlier), reports what is ready, builds what needs work and captures what is missing, and it leaves any technology spend until deemed necessary.

Following the path of least resistance gives the board evidence early in the hold and gives the equity story a solid foundation to stand on at exit.