Digital Deal Sourcing in Private Equity

By Christy Carter and Pratik Shah

The global private equity (PE) industry is continuing to outperform other asset classes, but at the same time, it is being buffeted by a changing environment. Unprecedented levels of dry powder and a steady rise in the number of new firms are leading to intense competition for high-quality assets. Average deal multiples have increased by 40% over the past five years, and the number of deals that close in less than five weeks has risen tenfold. Compounding these issues is the surge in unregulated yet high-caliber sources of private-debt financing; it is further driving up the price of quality assets across deal sizes.

These challenges spotlight an opportunity for investment professionals at PE firms: as the world becomes increasingly digitized and analytics tools become more powerful, investors can gain a clear edge over competitors by using data analytics to better identify and assess targets. Top-performing firms are beginning to view data analytics as a complement to their existing channels, procedures, and processes.

Data analytics can improve a PE firm’s performance all along the deal value chain. But in this article, we focus on the initial stages: deal sourcing and due diligence.

Deal Sourcing

Deal sourcing is the first stage of the PE value chain. It is often the most important stage for generating a competitive advantage and still very much a human endeavor—there is no algorithm or digital process that can source winning investments.

But by using data analytics tools, investment professionals can gain access to far more information about a much larger universe of companies than they could possibly do through traditional processes. They can also evaluate a tremendous amount of unstructured data—such as social media posts, quality ratings, and customer reviews—to identify potential targets. Moreover, new tools are emerging to help investors understand which information is truly relevant and the meaning of data patterns and trends.

In short, data analytics tools can provide richer and more comprehensive information about a potential opportunity than conventional processes can produce. These tools can also help investors assess multiple variables, such as increasing consumer engagement and the opinions of influencers, and thus generate deeper and more actionable insights. Professionals who use these tools will be able to see various aspects of a potential investment (or the sector in which it operates) faster and with more granularity than their competitors will be able to do. (See the exhibit below.)

For example, one rapidly evolving digital tool, social media analytics, can monitor brands—tracking the number of customer mentions or brand hits in the media, for example—and send customized alerts when patterns shift or certain thresholds are reached. Receiving advanced notice about brands that are generating buzz lets investment professionals further analyze those companies, assess the market positioning of the brands, and evaluate consumer sentiment in online discussions. (See “How Social Media Analytics Can Be Applied to PE Deal Sourcing.”)


Social media analytics is an evolving tool that can help investment professionals translate broad investing themes and trends into actionable insights, in the form of attractive investment opportunities. PE firms can contract with a growing number of vendors that provide social media analytics as a service.

Here’s an example of how this tool could be used. (See the exhibit below.)


  • A PE investment professional starts with an initial hypothesis. For example, consumers’ rising living standards, their greater focus on health, and an increase in their disposable income will lead to higher spending on health and wellness products and services.
  • By tracking comments across numerous social media platforms and in various digital communities and forums, the investor begins to focus on a target category: essential oils.
  • The investor builds search strings to find companies that are in the value chain of that category. Such companies may include manufacturers and refiners, distributors, and retailers.
  • After the investor chooses which companies to follow, the analytics tool sends automatic notifications when it detects spikes in social media traffic that indicate a selected company is trending (for example, when the number of company hits or mentions increases by 5% in one day or when the number of company followers grows by more than 10% in a week).
  • After receiving these notifications, the investor examines the underlying cause of the spike and the sentiment expressed in the traffic. The use of terms such as “quality” and “innovation” could signal ways in which the brand differentiates itself from its competitors. Those terms could also suggest that other factors, such as price, are less important to customers. The investor also reviews the demographics behind the social media hits. For example, the analytics tool may determine that the majority of participants in the discussion forums are women under the age of 35 who primarily live in coastal cities.
  • Through this analysis, the investor surmises that essential oils could be an interesting avenue to explore further and begins to identify companies and set up meetings with their management team to learn more. This is where digital and conventional analyses conflate.

Social media analytics is often associated with B2C industries. Many PE firms view it as a way for consumer-focused companies to anticipate current and potential customers’ wants and needs and stay a step ahead of the competition. However, such tools are equally applicable to all industries.

Indeed, the global PE industry is beginning to understand the sheer potential of applying these innovative tools early on in the investment value chain. “Digital and advanced analytics during the sourcing process allow us to move around pieces of complex data in different ways to really see the excitement of an opportunity,” noted a partner at a large-cap fund. “We have the same data as everyone else, but we can generate completely new insights if we just interpret patterns differently. We’re not quite there yet, but we’re rapidly moving in that direction.”

Importantly, data analytics tools enable investment professionals to identify more proprietary deals. (A proprietary deal can take one of two forms: a deal in which a firm is the only bidder, or a deal in which a firm is the first of multiple bidders and has a significant head start in terms of a relationship and access to company information.) The investment director at a leading midmarket fund told us, “Of course, everyone is trying to find a proprietary deal, but we know that it’s nearly impossible once transaction sizes move beyond the lower midmarket.”

Due Diligence

After identifying potential targets, investment professionals can partner with companies that have the data analytics tools required to create multidimensional views of those opportunities. These tools can complement conventional analytics processes and, in the hands of those who understand how to use them, allow investors to dig deeper into companies’ performance and potential.

For example, data analytics tools can help an investor explore how to develop or grow a market for a company or its products. Key areas of analysis may include potential pockets of value, customer demographics, brand value relative to the competition, and potential new customers. These tools can also help validate the market—and company—specific assumptions being used in valuation models and inform a PE fund’s subsequent bidding strategy. By combining data analyses with conventional analyses, investors can gain unique insights and spot early indications of value creation opportunities. (See “Data Analytics Delivers Due Diligence Insights for a PE Firm.”)


An investment professional at a leading PE fund was conducting due diligence on a consumer products company. In addition to using more conventional analyses, the investor and his strategic partner used data analytics to understand how the company performed relative to its competitors in several areas. (See the exhibit below.)


  • Social Media Ratios. By expressing the number of social media followers of the company as a multiple of revenue, and comparing the multiple to that of competitors, the investor determined that the company had the potential to monetize a larger fan base and thus strengthen its market position.
  • Influencer Metrics. After evaluating the social media comments from influencers, the investor determined that the company’s network of influential customers was larger than that of its competitors. The company’s customers also spread its brand news faster. Thus, the company’s revenue growth potential was stronger relative to that of its competitors.
  • Search Trends. Looking at Google search trends, the investor found that consumer interest in the company grew much faster than consumer interest in its competitors. This was true across a variety of regions, which was additional evidence of the company’s strong potential for revenue growth.
  • Sentiment and Attribution Analyses. By aggregating and analyzing 5,986 product reviews, the investor understood customers’ product-specific reactions and sentiment for the company’s portfolio of offerings. The investor also grasped what was unique about the brand and what differentiated it from its competitors. 

A Wide Variety of Benefits

In addition to the uses we’ve mentioned, data analytics tools are helping PE firms in the following ways:

  • To act on attractive businesses that are below the firm’s threshold for acceptable deal sizes; by identifying and investing in several “too small to consider” businesses—and finding a way to link them together—an investment professional can potentially create a strong and stable investment platform in a given segment
  • To identify opportunities for established companies that are in the firm’s portfolio; such investments may include smaller add-on businesses that have been operating under the radar
  • To generate insights regarding disruptions that may affect the value of existing portfolio assets (and also affect the timing and means by which the fund exits those investments)
  • To change the culture and mindset of investment professionals, encouraging them to think about value creation from multiple angles and in unconventional ways

Data analytics tools are increasingly applicable to helping PE investment professionals identify potential targets and conduct due diligence. And as the world is increasingly digitized, the trend is likely to gather momentum. These tools are continuously evolving and can be customized to reflect the theses and investment strategy of a given PE firm; we have only scratched the surface of the proprietary insights that can be generated. The investment professionals who embrace and apply these tools will give themselves a sustainable edge. Those who don’t risk putting themselves at a permanent disadvantage.