Private equity has long relied on spreadsheets based on available data to guide decision-making on which companies to acquire and which strategies to pursue in order to generate meaningful returns. This approach helped vault the global private equity industry into the $4.4 trillion juggernaut it is today.
Until recently, analyst experience, personal relationships, and Excel prowess were generally relied on to guide decision-making and review of target companies. But new methods have emerged and are quickly advancing.
The volume and granularity of the data available today would have been unimaginable even just a few decades ago. The ability to access and parse this ocean of data will be a key determinant of business success moving forward. For the private equity industry, now is the time to solidify and accelerate a comprehensive approach to data science.
Data science is multidisciplinary in nature: It brings together statistics, econometrics, data engineering, and computer science to collect, combine, and analyze significant amounts of structured and unstructured information to provide better insights and predictions to drive strategic decision making. When applied to private equity, this means a faster, more accurate way of assessing value both pre- and post-deal.
At the same time, more private equity assets are under management than ever. Competition for deals has increased and company valuations have been steadily rising. Any ability to cut down turnaround time between acquisition and analysis is valuable. This is a landscape where the consequences of slower decision-making or a false step from faulty reasoning or incomplete information has significant costs. As a result, the future of private equity is one where data science is fully integrated into deal investments and firm operations. It’s time to double down on data.
Billions of clicks, purchase decisions, and interactions are recorded every day. Enabled by cloud-computing adoption and rapid technology advances, the amount of granular, real-time data about consumers and companies has never been more readily available. In fact, the number of alternative-data providers is more than 20 times larger now than it was 30 years ago—with more than 400 currently active providers compared to just 20 in 1990.
Within these vast troves of records, machine learning and AI scripts enabled by data science can uncover value in ways humans—or Excel—simply can’t. Pivot tables in Excel can grind to a halt as the number of rows increases. Putting the pivot table into a cloud "data cube" improves the issue but can only be used to mine insights from historical data without any advanced modeling capability to make the future predictions our private equity clients need. We found in a recent deal that early user activity had high correlation with subsequent retention, which helped the client get comfort around the influx of customers during the pandemic.
In addition to the proliferation of data from companies within a portfolio or acquisition targets, third-party databases provide even more information. This “alternative data” sold by a variety of providers covers areas such as parking lot utilization rates for stores as a measurement of foot traffic. Frequency or keywords in search terms, social media sentiment and activity, phone usage, demographics, and census data and satellite feeds are among the many other sources from which meaningful insights can be drawn.
Understanding and effectively answering questions like, “What share of the consumer wallet does company X have of its consumers, relative to competitors in the same industry/genre?” requires use of these external data sources, married with company data, to reach a sound conclusion. The volume and complexity of the inputs available requires private equity to continue to shift to more advanced forms of analysis in order to stay competitive.
Data science helps private equity make smarter, faster decisions by providing more confident rationales for investment or uncovering red flags that stop a deal from completion, thus allowing that money to be deployed more profitably elsewhere.
In a recent client engagement reviewing a target company, we found that the number of new customers buying was slowing. However, new customers from more recent cohorts were spending more than those from older cohorts. With this evidence, we made the determination that this seemingly red flag was merely a yellow warning—not a deal breaker. Analyzing customer-level data was the only way to draw this conclusion.
The amount of data private equity firms have at their disposal can—when combined with rapidly advancing analytical capabilities—identify value others can’t see. In order to properly evaluate a company, it’s critical to pinpoint answers on their current portfolio of customers, comparative KPIs within the industry, and understand the LTV:CAC ratio, amont other indicative criteria. Those answers can all be found, but only with sophisticated analysis of the large reams of available data.
Data science identifies where and how operating executives should focus their resources. Using billions of data points, scarce company resources can be directed to initiatives that generate the highest ROI. For example, West Monroe recently worked with a private equity-backed software company to use data science to evaluate a number of value-driving questions, including: Who are the customers we can upgrade? What value should we place on the opportunity to cross-sell? How can we quantify our churn risk? Which of these drivers will impact revenue growth most quickly?
In just a few short weeks, our in-house data science teams and proprietary Intellio® suite of assets uncovered over $1 billion in cross-selling opportunities, segmented and identified the most likely customers, quantified the churn risk and identified churn triggers for the company to mitigate.
These are just some of the business questions that can be answered with access to data and the data science know-how.
Upgrading your approach to data science should involve a comprehensive investment and change-management strategy that considers people, process, and technology.
You need a data infrastructure and a technology stack to bring in the pipeline of data from the companies as well as outside databases. You need data engineers and scientists to bring in and analyze the data. And you need the language and mindset of data analytics disseminated throughout senior leadership and deal-making teams to turn insights into action in pursuit of profits.
The opportunities for bringing data science into the deal process have dramatically changed from just a few years ago. Private equity firms now have the opportunity to implement sophisticated methods of analyzing data to uncover value. The shift is underway, and firms should invest accordingly.
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