An emerging trend in the private equity space is an enhanced focus on data science.
This focus has historically been more on the operations side (post-acquisition) where data scientists have been leveraged to help companies improve performance in many key areas including marketing, business intelligence, financial analysis, and human resources. The advantage of focusing data scientists on the operations side is rather obvious: after an acquisition has occurred, the data challenges and opportunities of the portfolio company are more accessible.
More recently, some target companies have been opening up their books a bit more at the due diligence stage. This allows private equity firms to dig more deeply into the key revenue and profitability assumptions. Key analytics at this stage can include understanding not only the detailed drivers of profitability but also customer segments, factors influencing operations efficiency and direct competitor comparisons. Exploratory insights from mining databases including public data, social media and data vendors can be linked with company data to see if external trends are similar to internal trends. This may help better define any unique value propositions that company has or market segments they potentially can explore. For businesses with physical locations (retail, restaurant, etc.), data scientists can explore the assumptions regarding future site selection, projected number of sites growth, cannibalization, etc. For customers with direct customer contacts, data scientists can help understand the customer value
Leveraged buyout models (often Excel-based financial calculations) tend to have a rather standard structure. The formulas are well understood by financial analysts so many companies are using the same (or very similar) financial calculations. The variation across companies is usually driven by the key input assumptions including the revenue and income growth assumptions.
A key distinguishing factor for private equity firms is how well they understand the key inputs to those leveraged buyout models. Detailed data analytics at the due diligence stage can give them an advantage in better understanding the assumptions and limitations in the projected revenue and projected earnings…resulting in more accurate projections of the target companies value. #privateequity #datascience #data #analytics
