Revolutionizing Private Equity with Investment Performance Analytics 

August 12th, 2020
Vector graphic of data-driven private equity deal making

Private equity investors pursue healthy returns on their investment by strategic as well as tactical decisions during all the three phases of an investment - first in selecting the company to invest in, then when enabling the business transformation of this asset, and lastly the exit. The pressures on private equity investment firms are increasing continuously due to the high stakes involved,  shorter horizons for value realization, and new business models that can disrupt every industry segment. 

Private equity investors are assumed to be number-savvy and have the mythical ‘Midas touch’ but it's surprising that the number of firms still managing their portfolios based on Excel spreadsheets. These spreadsheets need to be manually created and updated and data is usually viewed in a tabular form or some limited types of charts. For these reasons, Excel spreadsheets are not suitable for private equity firms to analyze data about their investments. However, there is now a new and growing trend towards data-driven investing, led by a breed of private equity firms that are drawing on all the capabilities of advanced analytics to maximize revenue opportunities. Data analytics and machine learning have also become buzzwords in the private equity industry. 

Let us examine the role of data analytics at each of the three critical junctures in private equity investment. 

1. Data analytics for investment decisions 

The business model for private equity firms depends on their ability to identify the revenue generation capability of a company that’s seeking investment, and project future performance. When it comes to evaluating a company’s investment proposition, effective data analytics can handle large volumes of data about its business and indicate whether it is likely to earn money or not. 

This increases the number of companies the PE firm can thoroughly evaluate before deciding to invest.  If the firm also provide feedback based on data analytics to the founders who pitch, it will attract founders who are not merely seeking funds but appreciate the value addition of the process.

Investment portfolio analytics often reveals insights that challenge conventional wisdom and assumptions. Anecdotally, a team may say that they expect the fastest growth from a particular region,  and data may reveal that actually another region is going to give them far faster business growth.

For the private equity investor,  the attitude of a company's leadership towards data is itself a valuable indicator. Those that resist discussions around data may be lacking confidence in their company’s performance. So it's no wonder that many private equity firms today prefer to invest in companies where leadership teams embrace data analytics, have actionable data, clear KPIs, and transparency.

Data analytics helps private equity firms to increase their funnel, shorten the decision-making process,  and increase the probability of success. 

2. Driving value creation with analytics

Once the company is part of the PE firm’s portfolio, then the focus is on driving value creation after investment by providing strategic direction and operational improvement.  The data-powered investing private equity firms influence their portfolio companies to adopt analytics technologies and processes. 

Investment managers often reposition the company or redefine the category in order to be linked to high-growth market segments. This requires insights about market trends and forecasts - a key aspect of investment portfolio analytics. 

Initiatives that will create value in the short to mid-term need to be balanced with strategic and bold changes that will transform the valuation in the longer term. Making the right choices requires insights garnered from data that is aggregated from a variety of external sources as well as various internal systems. What-if scenario analysis can help to guide decisions about pricing and other business strategies.

Private equity is a key enabler for new business models in many different industry segments.  These tend to be digitally driven businesses that are disrupting traditional product and service offerings.  Analytics is vital for these digital models to succeed. Real-time analysis,  accurate forecasting, and the ability to tweak the business model in an agile manner play a key role in the success of these new ventures. 

When the investment team and operational teams are referring to a common data platform there is a far firmer grip on operational metrics and less friction and frustration on both sides. This facilitates the achievement of targets related to growth, sales, margins, productivity, cash flow etc.

A data analytics platform that can aggregate data from various systems and sources and provide visibility and insights to the investment team, as well as operational management of respective companies, helps to overcome the problem of unsynchronized data silos. 

Key stakeholders now belong to two different organizations - the private equity firm and the portfolio company. The data analytics platform should be able to deliver insights and meet the information needs of stakeholders across this extended enterprise. 

3. Analytics for successful exits

Investment teams need to plan for exits throughout the ownership period, articulating why the asset is an exciting investment opportunity and data provides the evidence to back this position. The effective use of data creates alignment between the investment and operational teams and helps to build the business case for the deal. The investment team creates an outline of the story and its different components and puts those together in conjunction with the management team. This process typically takes several months. 

Data enables the private equity firm to demonstrate why the company is a great asset, its future potential, and strategic importance. On the other hand, if a data-driven culture has not been created before,  then spreadsheets put together just before approaching the exit will never be equally effective. For prospective buyers, data about the company’s past performance, forecast of the future, insights about markets, geographies, economies, and consumers play a huge role in the purchase decision. 

It's important that the story being told by the investment managers should be corroborated by the latest data about the company's performance and markets. Potential buyers may doubt the investment proposal if it is based on certain market trends and forecasts but the current situation is very different.  For this reason, the data analytics tools and platforms used must provide results practically in real-time. 

The investment opportunity presentation needs to be tailored to the profile of the potential buyer. When approaching another private equity firm,  it will be quite different from the pitch to an institutional investor on the stock exchange. The right data analytics technology platform provides private equity managers to extract, explore, present, and analyze the data in many different ways. Specific information that is requested by potential buyers can be provided easily as the right processes and tools are in place for data aggregation and analysis.

Getting the timing of the exit right is critically important. The exit multiples that the private equity firm can earn vary hugely between market peaks and lows. The use of predictive analytics to analyze economic and market trends before finalizing the exit window can ensure the best valuation at exit. Exit readiness itself can be a KPI and all the factors that affect valuation can be tracked,  as well as analyzed for correlations and causality. 

Portfolio Management

Vector graphic of team generating idea from data

We looked at the transformational power of data analytics for maximizing the potential return on investment by a private equity firm in a company. Data also plays a very important role in the analysis of the entire portfolio -  to compare the exposure, risks, returns, and growth across different assets. As the process of data collection, analysis, and distribution can be automated, there are tremendous savings in the time and effort required to gather data from all portfolio companies. 

An individual manager In a private equity firm can sometimes be working with dozens or even hundreds of portfolio companies. Data analytics helps to benchmark performance as well as uncover strategic advantages across the portfolio, and also enables efficiencies in the operations of the private equity firm itself.

While each company in the portfolio will be using different tools and systems to track and report performance metrics,  the private equity managers need data from all these disparate sources in a consistent and comparable manner. A next-gen data analytics platform can aggregate data from all the different sources and display the performance metrics of all portfolio companies. Further, it also provides insights into the factors that affected performance. Stakeholders can drill down and discover more details from the platform itself.

Predictive models that build on historical data and consider a variety of factors such as currencies, geographies,  economics, and market trends help private equity firms to maximize returns and minimize risks. 

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