How Data Analytics and Machine Learning are Transforming Private Equity (2024)

All global known companies including Apple, Cisco, Google, Adobe, and more have been part of private equity deals at one point in time. If it weren’t for investors, US private equity firms, and their limited partners, these companies wouldn’t be here today as we know them.

Let’s face it the relationship among investors, founders, and private equity firms have been progressive for everyone involved. However, emerging technologies such as machine learning are disrupting this relationship.

The traditional way to source deals

Traditionally, the way to source investment deals is dependent on building connections and meeting potential clients face-to-face. Overall, sourcing deals depends on the firm’s ability to network effectively.

• After raising funds, firms use proprietary
  deal flow to connect with lawyers, accountants, and executives in the
  industry to find investment or buy out opportunities before other
  firms.
• Joint deals (or
  syndicates) are considered if the partner firm doesn’t have the funding to
  enter a lucrative deal by themselves. Bigger the company, the more
  connections it can find another company that is looking for buyout or
  funding.
• Further, the company may analyze financial and performance data of the company to understand if an investment would be profitable or not. These fundamentals form the basis of private equity and venture capital deal structures.

Global private equity firms including Accel, Sequoia Capital, and Goldman Sachs have been using these traditional methods to scour deals. Other than these basic financials of a company, what can PE firms do to make their investment decisions?

Data-driven approach to seek investment opportunities

The number of investments is increasing in the industry and tech start-ups are increasing by leaps and bounds. All start-ups look the same from the surface. In this scenario, it is increasingly difficult for private equity firms to find the right companies to invest.

Though the number of investment opportunities has increased, it is increasingly challenging for firms to find a few potential profitable companies among failures. The software can help PE firms by:

• Analyzing companies by masses
• Processing large quantities of data to identify trends and create graphs for comparison between start-ups and industry
• Use the results to determine the start-ups with the most promising growth potential

According to a survey conducted by Blue Future Partners and PEVC Tech, out of 137 private equity firms, the majority of them agree to use software to improve their ability to find and execute deals.

While there’s a strong movement towards the use of data analytics in deal sourcing, traditional methods still exist among firms to source deals. Many firms, however, are expanding their investment teams to digitize their deal sourcing.

According to a KPMG survey report, 79% of PE firms are aware that technology exists to facilitate their deal sourcing and investment activities, nearly 9% of firms have implemented the technology, and around 12% of firms are using related software. For anyone looking to get a foothold in the private equity industry or progress in a private equity career, this could be advantageous.

EQT Ventures, a Swedish private equity firm, used Motherbrain, to lead investment decisions in their venture wing. The firm made 20 investments based on software suggestions and has closed more than 30% deals and raised $50 billion across 27 funds. What’s more, the firms’ portfolio were raised upon suggestions made by the machine learning technology.

Clearly, both traditional networking methods to source deals and digital ways to find promising deals are working. Like all industries, the movement of private equity toward software is progressive. At the end of the day, the question remains ‘which will bring higher returns?