Happy Thursday and welcome.

A year ago I wrote about the first chart I opened when getting serious about a fund. As an LP, I built it in Excel and think-cell. It was slow and fiddly, so we never took the time to go beyond our initial slide.

A few weeks ago, a client asked a question about it that I never thought to ask in all my years as an LP.

So, let’s get to that.

Steffen

PS: As something new, we've made a short video showing how changing the grouping sets up the diligence questions. Watch the walkthrough.

The question: I was checking in with a client working through a diligence in FundFrame. They liked the money multiple chart. Then they asked:

"Do we have to look at it like deals? What if we group by sector, geography and the like?"

I had looked at that chart for years. It had never occurred to me. The reaction was part joy, part "how did I never see this before?"

A quick recap: In the chart I shared last year, each investment gets a bar. Its width shows invested capital. Its height shows the money multiple, split into realised proceeds and remaining value.

A small deal at 5x and a large deal at 2x look very different. I used it to see how much the fund depended on a few winners and where to test for repeatability.

Chart commentary: The chart above I love to see. Largely realized (green), wide value contribution and relatively few losses. Remove the best deal and it’s still a great fund.

What changes: Before, the visualization above was the only one. Now we’ve added a whole lot more, once you allow for grouping deals. Here are some use cases.

The fund-size test: If a manager is raising a larger fund and plans to write larger cheques, I want evidence that those investments can contribute to returns too.

Chart commentary: In this example, investments below 20m show 2.57x. Those at 60m to 80m show 1.19x. The smallest deals have also returned the most cash relative to cost. Most capital sits in larger deals, which have lower multiples so far.

The question I'd ask: How will your sourcing and operating playbook change at that size, and where have you already tested it?

The catch: Larger deals may also be younger. Compare vintages and holding periods before concluding that the manager struggles with size, then examine the companies themselves.

Another cut: Group the same investments by entry EV/EBITDA multiple.

Chart commentary: The lowest entry multiple band has the highest return at 2.11x, but the pattern is uneven. Deals bought at 10x to 12x outperform those bought at 8x to 10x. Those bought above 15x outperform the 12x to 15x group.

What I'd do next: Compare sectors and holding periods before drawing a conclusion about entry price. Growth, leverage and current marks could also affect the result. This view tells me which differences to investigate.

Before your next meeting: Find the biggest changes in the new fund. Bigger deals? A new region? A different partner in charge? Group the track record around those changes.

For years, every new angle meant another afternoon in Excel and think-cell, so most angles never got tried. Now regrouping the chart takes a click. It's built into FundFrame's AI Diligence module.

You arrive at the next GP meeting knowing which part of the track record needs explaining.

This newsletter is written by Steffen Risager, the founder of FundFrame, a platform for LPs to manage their private markets investments.

Before that, Steffen was CIO at Advantage Investment Partners, a Danish Fund-of-Funds.

Steffen has a decade of experience as an LP, and has made commitments totalling approx. $6bn across fund- and co-investments.