Private Equity knowledge.
Guides, metrics and cases.
Private Equity knowledge.
Guides, metrics and cases.
Private Equity knowledge.
Guides, metrics and cases.
PE 101: Concepts that matter.
In buyouts, value creation comes from three levers:
- EBITDA growth
- Multiple change
- Amortization
But with higher rates and compressed exit multiples, the playbook has shifted decisively toward operational earnings growth and margin expansion. Current industry reviews show sponsors leaning into pricing, mix, and cost programs to offset weaker multiple tailwinds, while the liquidity logjam (exits/fundraising) raises the bar for truly underwritten, cash-generative plans.
PE 101: Concepts that matter.
In buyouts, value creation comes from three levers:
- EBITDA growth
- Multiple change
- Amortization
But with higher rates and compressed exit multiples, the playbook has shifted decisively toward operational earnings growth and margin expansion. Current industry reviews show sponsors leaning into pricing, mix, and cost programs to offset weaker multiple tailwinds, while the liquidity logjam (exits/fundraising) raises the bar for truly underwritten, cash-generative plans.
PE 101: Concepts that matter.
In buyouts, value creation comes from three levers:
- EBITDA growth
- Multiple change
- Amortization
But with higher rates and compressed exit multiples, the playbook has shifted decisively toward operational earnings growth and margin expansion. Current industry reviews show sponsors leaning into pricing, mix, and cost programs to offset weaker multiple tailwinds, while the liquidity logjam (exits/fundraising) raises the bar for truly underwritten, cash-generative plans.
BRK-H
S&P500
QQQ
ABVCAP
BRK-H
S&P500
QQQ
ABVCAP
BRK-H
S&P500
QQQ
ABVCAP
ETA/Search: From thesis to first meeting, practically.
The search fund path rewards focused, thesis-driven sourcing: define narrow acquisition criteria (size, industry, moat, cash flow), build an industry map, and run a proprietary outreach engine (owners, brokers, and centers-of-influence) until you surface “live” conversations.
Stanford’s longitudinal studies (2022/2024) document the model’s track record and common features: funding structure, target profiles, and outcomes, useful for calibrating search pacing, diligence depth, and what a “bankable” first meeting looks like.
ETA/Search: From thesis to first meeting, practically.
The search fund path rewards focused, thesis-driven sourcing: define narrow acquisition criteria (size, industry, moat, cash flow), build an industry map, and run a proprietary outreach engine (owners, brokers, and centers-of-influence) until you surface “live” conversations.
Stanford’s longitudinal studies (2022/2024) document the model’s track record and common features: funding structure, target profiles, and outcomes, useful for calibrating search pacing, diligence depth, and what a “bankable” first meeting looks like.
ETA/Search: From thesis to first meeting, practically.
The search fund path rewards focused, thesis-driven sourcing: define narrow acquisition criteria (size, industry, moat, cash flow), build an industry map, and run a proprietary outreach engine (owners, brokers, and centers-of-influence) until you surface “live” conversations.
Stanford’s longitudinal studies (2022/2024) document the model’s track record and common features: funding structure, target profiles, and outcomes, useful for calibrating search pacing, diligence depth, and what a “bankable” first meeting looks like.
BRK-H
S&P500
QQQ
ABVCAP
BRK-H
S&P500
QQQ
ABVCAP
BRK-H
S&P500
QQQ
ABVCAP
Metrics & Modeling: EV/EBITDA, ROIC, cohorts, unit economics.
EV/EBITDA triangulates enterprise value to pre-tax operating cash flow; because EBITDA excludes interest on excess cash, cash is netted from EV to avoid overstating the multiple.
ROIC (NOPAT / invested capital) frames whether growth creates value, sustained ROIC above WACC compounds intrinsic value; below it, growth destroys value.
For go-to-market, unit economics (LTV, CAC, payback) and cohort analysis (retention/expansion by vintage or segment) separate durable models from leaky buckets; a higher LTV:CAC (often ~3x as a rough bar) and improving cohort retention are the tells.
Metrics & Modeling: EV/EBITDA, ROIC, cohorts, unit economics.
EV/EBITDA triangulates enterprise value to pre-tax operating cash flow; because EBITDA excludes interest on excess cash, cash is netted from EV to avoid overstating the multiple.
ROIC (NOPAT / invested capital) frames whether growth creates value, sustained ROIC above WACC compounds intrinsic value; below it, growth destroys value.
For go-to-market, unit economics (LTV, CAC, payback) and cohort analysis (retention/expansion by vintage or segment) separate durable models from leaky buckets; a higher LTV:CAC (often ~3x as a rough bar) and improving cohort retention are the tells.
Metrics & Modeling: EV/EBITDA, ROIC, cohorts, unit economics.
EV/EBITDA triangulates enterprise value to pre-tax operating cash flow; because EBITDA excludes interest on excess cash, cash is netted from EV to avoid overstating the multiple.
ROIC (NOPAT / invested capital) frames whether growth creates value, sustained ROIC above WACC compounds intrinsic value; below it, growth destroys value.
For go-to-market, unit economics (LTV, CAC, payback) and cohort analysis (retention/expansion by vintage or segment) separate durable models from leaky buckets; a higher LTV:CAC (often ~3x as a rough bar) and improving cohort retention are the tells.
BRK-H
S&P500
QQQ
ABVCAP
BRK-H
S&P500
QQQ
ABVCAP
BRK-H
S&P500
QQQ
ABVCAP
Automation/AI — How data pipelines improve process and outcomes.
Robust data pipelines turn raw telemetry into measurable productivity:
UPS’s ORION pipeline/optimizer cut ~100M miles and ~10M gallons of fuel per year by continuously recomputing routes, hard dollar savings from clean ingestion, feature engineering, and decision automation.
Uber’s Michelangelo platform shows the same pattern at software scale and streaming data + standardized ML workflows (e.g., DeepETA) improved global arrival-time prediction and service reliability, while enterprise surveys confirm rapid GenAI adoption as firms wire these pipelines into frontline processes.
Automation/AI — How data pipelines improve process and outcomes.
Robust data pipelines turn raw telemetry into measurable productivity:
UPS’s ORION pipeline/optimizer cut ~100M miles and ~10M gallons of fuel per year by continuously recomputing routes, hard dollar savings from clean ingestion, feature engineering, and decision automation.
Uber’s Michelangelo platform shows the same pattern at software scale and streaming data + standardized ML workflows (e.g., DeepETA) improved global arrival-time prediction and service reliability, while enterprise surveys confirm rapid GenAI adoption as firms wire these pipelines into frontline processes.
Automation/AI — How data pipelines improve process and outcomes.
Robust data pipelines turn raw telemetry into measurable productivity:
UPS’s ORION pipeline/optimizer cut ~100M miles and ~10M gallons of fuel per year by continuously recomputing routes, hard dollar savings from clean ingestion, feature engineering, and decision automation.
Uber’s Michelangelo platform shows the same pattern at software scale and streaming data + standardized ML workflows (e.g., DeepETA) improved global arrival-time prediction and service reliability, while enterprise surveys confirm rapid GenAI adoption as firms wire these pipelines into frontline processes.
BRK-H
S&P500
QQQ
ABVCAP
BRK-H
S&P500
QQQ
ABVCAP
BRK-H
S&P500
QQQ
ABVCAP
Cases: What worked, what didn’t, and why.
Worked : Blackstone × Hilton (2007–2018):
High-leverage entry + crisis-era operational work + post-recession asset-light strategy produced an ~$14B profit on exit, an example of EBITDA growth and deleveraging outmuscling cycle risk.
Didn’t : Kraft Heinz (2015–):
Aggressive zero-based budgeting with underinvestment in brands left a thin growth engine and culminated in a $15.4B impairment and strategy reset, proof that cost alone rarely sustains value without brand vitality and top-line renewal.
Cases: What worked, what didn’t, and why.
Worked : Blackstone × Hilton (2007–2018):
High-leverage entry + crisis-era operational work + post-recession asset-light strategy produced an ~$14B profit on exit, an example of EBITDA growth and deleveraging outmuscling cycle risk.
Didn’t : Kraft Heinz (2015–):
Aggressive zero-based budgeting with underinvestment in brands left a thin growth engine and culminated in a $15.4B impairment and strategy reset, proof that cost alone rarely sustains value without brand vitality and top-line renewal.
Cases: What worked, what didn’t, and why.
Worked : Blackstone × Hilton (2007–2018):
High-leverage entry + crisis-era operational work + post-recession asset-light strategy produced an ~$14B profit on exit, an example of EBITDA growth and deleveraging outmuscling cycle risk.
Didn’t : Kraft Heinz (2015–):
Aggressive zero-based budgeting with underinvestment in brands left a thin growth engine and culminated in a $15.4B impairment and strategy reset, proof that cost alone rarely sustains value without brand vitality and top-line renewal.
Informational content only. Not investment advice.
Data from public and private sources; some figures may be estimates.
Confidentiality and terms apply to all engagements.
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Informational content only. Not investment advice.
Data from public and private sources; some figures may be estimates.
Confidentiality and terms apply to all engagements.
How it works.
Subscribe to Newsletter
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