Welcome back to our series on private equity and data! If you’re just stumbling across this article, you can read the first two installments to learn about how to setup best-in-class portfolio management and maturity comparisons using cutting-edge analytics. Today, in our final installment of this series, we’ll discuss how private equity firms can leverage recent innovations in predictive AI to identify new acquisition targets.
To find differentiated deal flows, private equity firms need to look beyond the obvious targets and explore untapped markets or overlooked sectors. Firms are constantly looking for new data sources and new analytics approaches to uncover hidden gems that may have been missed by their competitors. For example, one firm used satellite imagery and geospatial data to identify undervalued real estate assets in emerging markets, while another leveraged social media data to assess consumer sentiment and brand loyalty for a potential consumer goods investment.
These are tactics that all firms should be exploring—but as the landscape becomes increasingly competitive, PE firms will need to be faster and more accurate. By harnessing the potential of predictive AI, private equity firms can gain a deeper understanding of their target companies, industries, and markets, allowing them to make more accurate projections and valuations.
To get started with AI-powered acquisition analysis, PE companies should start by making sure their fundamentals are in order, including analyses of the market, cost, and customers of acquisition targets. By conducting thorough analysis of market dynamics, cost structures, and customer preferences, you can identify sectors with high growth potential. This strategic approach allows you to allocate resources effectively and capitalize on emerging opportunities.
After setting up foundational data elements to drive the models, recent innovations in AI like Snowflake Cortex have allowed companies to implement AI solutions without a specialized data scientist. With a simple, one-line SQL-like function, any business intelligence analyst can use pre-built modeling algorithms to run complex data models in minutes instead of weeks.
A data cloud with these integrated AI tools can increase decision speed, modeling future cash flows, price multiples, industry strength and vulnerability there are hundreds of factors to be considered. Given a set of data on a potential acquisition, cortex can create projections that account for any of the following factors:
AI and LLMs have the capabilities to go beyond simple text generation. Given financial information such as balance sheet, P/Ls and cost of capital, predictive AI can be used to generate DCF, NPV, and FCF models while taking into account the factors mentioned above. With Spaulding Ridge’s propriety accelerators, projections for a variety of market conditions can be created quickly and accurately.
PE firms are increasingly competing on their fluency with data, and as the discipline of analytics evolves, acquisitions will be an area where advanced techniques such as natural language processing and predictive analytics can drive value. Spaulding Ridge has helped private equity and venture capital firms achieve their investment goals through the strategic use of cloud technology, analytics, AI, and more—and we’re uniquely positioned to help you navigate this complex and rapidly-changing environment. If you’re curious about how AI can benefit your organization, we’d love to hear from you.
