How M-Squared’s Global MMM Framework De-Risked a €150M PE Deal in Fast Fashion

When Apex, a London-based advisory firm that supports private equity investors across Europe, came to us with the Lunas project, the brief was simple:

Help the fund understand what they are actually buying.
Not the topline. Not the pitch deck.The truth of the business.

Lunas — a fast-fashion brand operating across Denmark, the Netherlands, and the UK — had all the signals of a strong acquisition target. More than $150M in revenue, over 30 stores, rapid market expansion, and what appeared to be an enviable repeat-purchase curve.

But beneath the surface lived a real question:

“Is this a brand with durable economics — or a discount-driven machine with expensive habits?”

This is the story of how we answered that question.

A Brand at an Inflection Point

Lunas began in Denmark as a digitally native retailer and quickly built a passionate following. Within a decade, the brand expanded into physical retail, capturing a commanding share of the domestic market and building a retention curve that outperformed nearly every benchmark.

The appeal for investors was obvious:

• Proven success in a core market
• Early traction across borders
• A strong omnichannel presence
• A vertically integrated supply chain allowing superior margins

But the concerns were equally valid:

• Heavy discount dependency
• Skyrocketing media costs in the UK (up to 10× Denmark)
• Attribution noise from overlapping markets
• An unclear role of brand versus paid media

The business was successful — but the drivers of that success needed to be untangled before anyone could price it correctly.

The Challenge

From the fund’s perspective, diligence materials only raised more questions:

• Were new customers truly incremental, or simply bought through spend?
• Was retention organic, or was the business “re-acquiring” its own customers?
• Was Lunas a true brand, or merely a discount engine?
• What role did stores actually play in driving revenue?
• And ultimately — what was this business worth?
  

Traditional reporting could not answer these questions. Platform attribution certainly couldn’t.

This diligence required a multi-market, multi-channel causal view — something only an MMM-based Marketing Accounting approach could deliver.

Our Approach

Together with Apex, we rebuilt Lunas’ economics from the ground up.

1. Reconstructing the Real P&L using marketing accounting frameworks

We separated each market (Denmark, Netherlands, UK) into three independent sales channels,

• New customers
• Returning customers
• Retail

This created nine discrete P&Ls — each modeled separately to capture the true dynamics of demand.

2.  MMM input features for Every major business driver

For each P&L, we built a dedicated MMM to isolate:

• Baseline demand
• Media impact
• Discount elasticity
• Store effects
• Brand-driven demand (via branded search)

This allowed us to measure why growth was happening — not just where it showed up.

Key Findings

1. New Customer Growth Was Largely Purchased

 

 

In Denmark, MMM revealed:

• 43% of new customers were driven by paid media
• 30% were driven by discounting
• Only 20–30% were organic baseline

Across markets, 70–80% of all new customers were effectively purchased.

In the UK and Netherlands, baseline demand was even smaller due to early market penetration.

Implication: Acquisition was working — but not because customers naturally found the brand (yet). Most demand was “manufactured,” and needed to be treated as such in valuation.

What we discovered reframed the investment thesis entirely.

2. Retention Was Real, Durable, and Economically Critical

The raw repeat-purchase curve was exceptional:

• ~50% of Year-1 customers returned in Year-2
• ~50% of those again in Year-3
• Even in Year-5, nearly half the cohort remained active

MMM confirmed:

• Mature markets had a strong organic baseline
• Media drove only ~20% of repeat orders
• ROI on media for returning customers was 5–10× higher than for new customers
  

Implication:
The business only made sense on an LTV basis — but that LTV was extremely strong and highly defensible.

3. The Brand Was Real — Not Manufactured by Discounts

To assess brand equity, we used branded search trend as the high-value action.

In Denmark:

• Brand demand contributed roughly ⅓ of new customers

In emerging markets, brand signals were smaller but rising as footprint grew.

Implication:
Despite heavy promotional intensity, the brand remained strong — a critical moat for long-term viability.

4. Stores Were Driving Far More Than Store Revenue

 

 

In Denmark:

• 70–80% of repeat revenue correlated with store footprint
• Store openings boosted online revenue as well as in-store traffic
• Stores behaved like billboards on the high street
  

For a fund with retail DNA, this became a strategic unlock.

Implication:
Lunas wasn’t actually digital-first — it was omnichannel-first, and stores were the engine powering both sides.

5. Discounting Was Expensive — But Margins Stayed Strong

Discounting intensity reached 20–25% of revenue.

Yet Lunas maintained strong margins because it vertically owned key parts of its supply chain.

However, when isolating incremental revenue:

• Denmark’s $120M total revenue
• Only $32M was incremental to media + promos
• Discount cost was $20M+
• First-purchase margins were thin

Implication:
The economic engine was not the first purchase —it was the return purchase.

Outcome: A Clear, Confident Investment Thesis

By integrating nine MMM models, triangulating unit economics, and modeling cross-market dynamics, we delivered a cohesive truth:

• A strong brand in core markets
• A repeat engine that could be trusted
• A store strategy that amplified online performance
• Attractive margins despite heavy discounting
• An international story with upside — but priced correctly
  

The final outcome:
A valuation at 7–8× EBITDA, aligned to risk, grounded in causal measurement, and supported by a clear plan for expansion.

Looking Ahead: The Future of Diligence

Toward the end of the process, Laps said something that captured the essence of this case:

“I’m surprised how long investors have been willing to accept known-wrong answers from cookie-based attribution.”

This deal demonstrated what modern diligence can — and should — look like:

• Causal models, not platform dashboards
• Baseline vs paid, not blended CAC
• True LTV, not first-order margin
• Retail + digital modeled as one ecosystem
• Measurement that informs valuation — not just media plans

This is the standard investors now require.
And it’s the standard we’re proud to help set.