The dataset for this study is retrieved from the Gain.pro platform. Our data is built up as follows: as a starting point we track all (>100m legal entities) in Europe to detect sizable companies. As a result, our platform contains a profile on every business with >50 employees (totalling approximately 400k companies). We subsequently analyze those we consider ‘investable assets’ in even more depth. This includes all companies that exceed either €5m EBITDA, €50m revenue, €10m funding raised or >250 FTEs, which results in ~35k companies. This study heavily draws on 2 unique elements of our dataset for those assets. The first is that we track an approximation of organic growth for every company based on an in-depth outside-in analysis of their financials (i.e. when normalizing for any M&A, switching topcos etc). The second is that our platform clearly distinguishes platform deals from add-on M&A, resulting in a much clearer view on levels of dealmaking including buy-and-build activity. This enables us to generate insights on the private asset pool you simply cannot create with any other database. 

Certain limitations of the analysis should be mentioned. The analyzed companies for which sufficient data is available contains a skew towards institutional ownership. To be precise, for the analyzed companies at a detailed level, 11% were VC-backed, 32% were PE-backed, 10% were minority PE-backed, 2% were longhold and 45% were private companies. Where appropriate we aimed to reduce this sample bias by for instance only analyzing PE-held assets. This is disclosed in the footnotes where applicable. In addition, our focus on ‘investable assets’ inevitably skews towards sizable companies and also leads to a degree of survivor bias. In addition, companies in the dataset have different reporting standards and timings. For instance, some companies have not published annual reports for the years 2021 or 2022 yet. This also leads to the inclusion of different time frames when calculating e.g. 5-years figures averages or CAGRs. Lastly, the inherent low data availability in private markets means that metrics such as organic growth estimates are made that can be incorrect in individual cases. However, it is likely that most of these limitations are normalized at the aggregate level that this study presents our insights at.