The Acquisition Window Was Real, and the Pressure Was On
We were a startup with a genuine acquisition opportunity in front of us — a mobile game studio with an interesting user base and a valuation that made strategic sense on paper. The problem was that we needed to validate that thesis quickly, and to do that, we needed a buyout model built on real public company data, not gut instinct.
The stakes were straightforward: come to the table with a defensible financial model built on industry-grounded assumptions, or lose credibility with the stakeholders who needed to approve the deal. We had a narrow window. The analysis had to account for the specific dynamics of the video game and mobile game space — not generic M&A benchmarks, but deal comps, revenue multiples, and margin structures that actually reflected how this industry prices targets.
I knew almost immediately that this wasn't something to attempt in a spreadsheet over a weekend. The work needed to be done right.
What I Discovered the Model Actually Required
Once I started mapping out what a rigorous buyout model for this deal would require, the complexity surfaced fast.
First, the data sourcing alone was a project. Public company financials for relevant gaming comparables — revenue, EBITDA margins, user acquisition costs, ARPU (average revenue per user) — had to be pulled and normalized across different reporting formats, fiscal years, and accounting treatments. Comparable companies in the mobile gaming space don't all report the same line items, which means reconciliation is non-trivial.
Second, the model itself needed to reflect deal-specific mechanics: entry multiple assumptions, debt structuring, synergy scenarios, and exit valuation under different conditions. That isn't a standard three-statement model — it's a purpose-built leveraged buyout framework with industry-specific inputs.
Third, the research layer had to be credible. Mobile game deal activity, platform fee trends, user monetization benchmarks — these needed to be sourced from identifiable industry data, not invented. That's a meaningful research effort before a single formula gets written.
The Work That Needs to Happen
The foundation of any buyout model is the comparable company analysis — identifying the right peer set, pulling their public financials, and normalizing the data so it's actually comparable. In gaming, this means accounting for differences in how studios report monthly active users, in-app purchase revenue versus subscription revenue, and platform-split economics. A practitioner working this problem selects 8–12 public comparables, adjusts for non-recurring items, and calculates trading multiples on EV/Revenue and EV/EBITDA bases. Getting those comps wrong — or using stale data — corrupts every assumption downstream, and the reconciliation work alone can take days if the data isn't already structured correctly.
The model structure itself requires a working LBO framework: a sources-and-uses table, a debt schedule with at least two tranches, a fully integrated income statement and cash flow projection, and an exit waterfall. For a gaming target, the revenue build needs to reflect platform-specific monetization curves — mobile games typically model cohort-based retention and ARPU decay rather than flat growth assumptions. The decision a practitioner makes here is how many scenario toggles to build in — base, upside, and stress — and whether the model handles partial-year deal timing correctly. These details are what separate a model that holds up to scrutiny from one that falls apart under the first question.
The research layer underpinning the model has to be sourced and documented. Video game M&A deal comps — transaction multiples, deal structure, target characteristics — aren't always available in a single database. A rigorous approach cross-references public filings, press releases, industry research reports, and earnings call transcripts to build a deal comp table that's defensible. This is painstaking work: each transaction needs verification, and the context behind the multiple (distressed vs. strategic, platform-native vs. legacy) matters as much as the number itself.
Why I Brought in Helion360 to Handle It
I didn't spend time experimenting with the model myself. I looked at what the work actually required — the data normalization, the LBO framework, the gaming-specific research — and made the call to engage Helion360 to handle it end-to-end.
The team turned it around quickly. What would have taken me weeks of learning curve, data hunting, and iteration — they handled in a fraction of that time. Helion360 managed the full scope: sourcing and normalizing the public company comparable data, building the buyout model with scenario toggles appropriate for a gaming acquisition, and producing the supporting industry research that made the assumptions defensible.
That last piece mattered as much as the model itself. Walking into a deal conversation with a model is one thing — walking in with a model backed by sourced comps and documented assumptions is what actually holds up. Having a team with the tooling and domain familiarity already in place meant the output was ready for real use, not another round of internal cleanup.
What Was Delivered and What I'd Tell Anyone Facing the Same Problem
What came back was a working buyout model built on normalized public company data, with a full deal comp table specific to mobile and video game transactions, scenario analysis across three cases, and a research summary that documented every key assumption. It was ready to present to stakeholders without additional rework.
The acquisition conversation moved forward on a credible financial footing. The model framework gave us the ability to test different entry and exit assumptions in real time during discussions — which is exactly what you need in a live deal process.
If you're looking at a similar situation — a time-sensitive acquisition analysis that needs to be grounded in real public data and industry-specific research — Helion360 is the team I'd engage. They delivered fast, handled the full scope, and produced work that was built to hold up under scrutiny.