Enhancing investment decisions to maximize private equity returns by leveraging data analytics

Data analytics has ushered in a new age of decision-making in the PE industry leading to more levers to drive value and generate overall higher returns

As the global pandemic hit, the Private Equity (PE) industry too saw an abrupt decline in the volume of deals in first half of 2020. But by the third quarter of 2020, it had bounced back and was performing at a 3-year high with total transaction volume reaching $168 billion, as per S&P Market Intelligence’s 2021 Global Private Equity Outlook.

The total amount invested in the second half of 2020 was approximately $320 billion, which is higher than in the same period during the previous couple of years. Looking ahead, the S&P report also suggests that over two-thirds of investors believe that investment activity will increase in 2021.

With about $2 trillion of dry powder available with the PE investors, it is very important to leverage Data Analytics to adjust to the new normal of the investment environment across the deal lifecycle, from sourcing, value creation, to eventually optimizing the exit options.

Early Trends and Themes for Investments: In this age of information abundance and hyper-connectivity, intuition and network-driven investment sourcing is not sufficient or optimal. The traditional sourcing channels can be augmented by robust Data Analytics leveraging advanced Machine Learning algorithms, that can give an edge in terms of early identification and also unearthing potential targets that are not yet mainstream opportunities.

Sourcing and analyzing information from digital platforms is an efficient way to identify potential targets for investment to complement traditional channels. Data Analytics can be leveraged to synthesize and mine large sets of public (e.g., Investment activity, Industry news, Web traffic, Social Media activity, Mobile app metrics, e-commerce sites, etc.) and subscription-based data sources (e.g., syndicated transactions data, massive mobile data, etc.). Trending themes and entities can be identified for any specific industry, companies, and periodic notifications can be set up.

An analytics engine that drives digital data mining for sourcing deals is typically powered by Big Data Management capabilities, Predictive Modeling, Forecasting techniques and NLP (Natural Language Processing) techniques that drive NER (Named Entity Recognition), Topic Extraction and Text Analytics, etc. Investors can thus leverage the bird’s eye view of the industry from a wide range of data sources and gain the first-mover advantage that would have otherwise required hundreds of man-hours.

Due Diligence Process: Nowadays, deal teams have access to large sets of structured and unstructured data from the target company that needs to be synthesized in a very short period. At the same time, they have to conduct competitor analysis, and customer feedback assessment to further enhance their understanding of the product performance and gauge future success.

This capability requires knowledge and access to robust tools and also the Data Engineering skills and analytics capability to provide this arbitrage quickly in the investment decision-making process, which Sometimes could take only a few days. The ability to set up a complex cloud environment to process large amounts of data, leverage Data Engineering capabilities to cleanse, process, aggregate and synthesize to draw quick, but deep insights can be of immense value to validate and determine key assumptions in the investment thesis.

Portfolio-level Feedback Loop: Building on the data-driven approach, private equity firms can develop in house database of historic funded and non-funded deals, and the current portfolio metrics. This data captures the deal size, industry, region, bid process details, competitor PE firms involved, deal team members details, ROI assumptions, and post-investment performance metrics across key financial and operational parameters. It enables firms to make robust funding decisions by drawing critical learnings from the past and benchmarking the growth potential based on deals in similar space.

Driving value through robust Portfolio Analytics: Post-acquisition, robust Data Analytics interventions can help accelerate the performance across the top-line and bottom-line. It can be done via better customer targeting, operational network restructuring, pricing shifts, and/or enhancing the data management infrastructure, ultimately leading to more efficient strategic and tactical decision making.

The way Data Analytics is deployed to generate value by the portfolio operations groups is not particularly different from how traditional management teams do, but an investor mindset warrants an appropriate analytics partner (internal or external) with the same laser-sharp focus and ability to generate quick wins and medium-term momentum, across a wide variety of problem statements and industries.

In addition, PE firms have begun to leverage the power of cross-portfolio analytics to drive synergies in procurement spend, talent management, vendor consolidations, etc. Also, sharing best practices and successful strategies across the portfolio can be better driven by data-driven insights that have proved to be value-generating.

As the field of Data Analytics becomes more mature, there is a corresponding increase in the transparency of operations and robustness in business decision-making process. This has enabled the Private Equity industry to base its investment decisions on contextually meaningful insights drawn from the abundant pool of data available today, thus revolutionizing the sourcing and diligence process which is the cornerstone for investment decisions. This has also led to long-term value creation by bringing in cutting-edge capabilities in Data Analytics to accelerate the growth story for portfolio companies.