Private equity’s impending disruption

The operating model for sponsors has held steady for 30 years, but data science advances are set to change all that, say Ian Picache and Sajjad Jaffer of Two Six Capital.

Seven years ago, Marc Andreessen issued a warning when he wrote that “companies in every industry need to assume that a software revolution is coming.” In his op-ed, published in The Wall Street Journal, the co-founder of the influential venture capital firm Andreessen Horowitz then went on to provide no fewer than 15 separate examples of how software was transforming different industries, from media and entertainment to autos and oil and gas. The private equity industry at large apparently missed the memo, as few, if any, firms have moved beyond Excel as an analysis tool within their business. Of course, revolutions don’t exactly knock first before turning an industry on its head.

To be sure, the private equity industry has evolved quite a bit over the years. Firms continue to refine their operating capabilities, develop new specialisations or add complementary products to their fund lineup, be it private debt vehicles, longer-life funds or something else. Yet the business model, itself, has changed very little over the past 30 years. In its current state, private equity very much remains a relationship-driven business dominated by outsized personalities with deep financial acumen.

This is about to change. Advances in technology have opened the door for capabilities that couldn’t exist until now. As data volumes have grown and as computing power, through the cloud, enables ever-deeper analysis, GPs have at their disposal data-driven insights that can materially optimise the factors that drive performance.

Several factors speak to why the industry is ripe for disruption. Most notable is that the tools available today have altered what’s possible. In the public markets, hedge funds have already tapped into advances in big data and analytics to carve out a unique and material edge. Private equity’s impending innovation wave will similarly be powered by the cloud and data science.

Exponential growth

The scalability of cloud and parallel computing facilitates the ability to ingest billions of rows of data. Excel, in contrast, becomes noticeably slower at approximately 100,000 rows. Alongside the exponential growth in computing power, the explosion in data volumes and data types allows investors to apply statistics at scale – assuming, of course, they have the capabilities to do so.

Again, public investors have already leveraged big data, whether it’s through satellite imagery of parking lots that can pinpoint retail sales trends or website scraping that identifies otherwise unseen movements in sentiment. For private capital investors, however, data science and engineering can help sponsors arrive at a differentiated and conclusive view of a company’s intrinsic value, providing a better foundation for entry valuations, value creation and exit timing.

Alternatively, hard data – and the ability to leverage the flood of information to understand distinct business drivers – instills a far deeper conviction around valuations. And this provides a basis for everything else. Applying statistical models developed by professors at The Wharton School and using data stores inclusive of transactional PoS, CRM and ERP data can deliver a very granular view into the value of each and every customer and product. As Wharton professor and Two Six research advisor Eric Bradlow says: “Whether you have 100 customers or 10 million, profits are made one customer at a time.”

Predictive analytics

The data, which can be cut an infinite number of ways, also reveals metrics around customer behaviour trends over time, the channels that produce the most profitable customers with the highest ROI, as well as insights on product lifecycle and SKU rationalisation. The ability to harness predictive analytics can then allow for scenario-building offering a roadmap for growth.

At the outset, insights can inform thesis development for sponsors and strengthen due diligence efforts. It can tell prospective acquirers when to pay a premium for assets, or conversely, at which point valuations introduce too much risk.

It’s akin to a watchmaker, or horologist, who can take apart a vintage mechanical timepiece to understand what works, what doesn’t and any potential vulnerabilities. Most assume everything is in working order if the second hand is moving; the horologist, though, understands how the 300-plus moving parts work together.

The ability to manipulate and unpack such large volumes of data can also be instrumental in value creation through supporting resource-allocation decisions. Again, the applications are almost limitless, but at a high level, analysis arms marketing, finance and product teams to improve customer segmentation, identify new markets or cross-selling opportunities, or optimise R&D spending. Moreover, sponsors can recognise the appropriate inflection points at which to exit an investment, thus maximising returns and providing the next owner a framework to realise similar gains.

All of this sounds good in theory. Real-life applications, however, better demonstrate the true utility of data science. For instance, co-investing alongside a large international PE firm, we worked with a global software company to assess the scope of the opportunity. Prior to acquisition, third-party consultants had surmised that the US was oversaturated and offered little white space for the company. The data said otherwise, allowing the investor group to pay more for the asset and underwrite an untapped (and significant) growth strategy. Post close, the data further guided management to refine its target markets, focusing on global regions with the highest ROIC, while mapping out a path to adopt a subscription model that both stabilised and grew top- and bottom-line performance.

While the need for technology becomes more acute and the capabilities exponentially more dynamic, the challenge for sponsors is that to build this kind of platform in house requires a deep investment and skillsets not typical to private equity. So while the evolution may be more protracted than say the disruption facing bricks-and-mortar retail, PE’s operating model of the future will over time better resemble the agile structure of a technology innovator, with cross-functional teams of deal professionals, data scientists and engineers.

With a data science backbone in place, the GP of tomorrow will be more scalable, requiring fewer people to produce higher conviction, faster decision making and differentiated, alpha-producing insights. Private equity, a model of active management, will see the potential for added value become more pronounced through operational dashboards offering real time feedback and agility. And the very culture of the industry, long reflecting an investment banking and consulting lineage, will see a noticeable shift as deal and operations teams collaborate far more closely with data science professionals.

The same way Bain Capital reimagined the PE model around consulting capabilities, technology will usher in the next era for the asset class. In addition to better returns through data-powered investing, these capabilities will eventually create a barrier to entry that separates the leaders from the laggards.

In today’s data-rich and fast-computing world, those who can monetise this philosophy should enjoy a long-term competitive advantage. And just like the PE pioneers, data will allow tomorrow’s top quartile to see value where others don’t. The difference is that it will also allow these firms to see and build value in ways others can’t.

Ian Picache and Sajjad Jaffer are managing partners and co-founders of Two Six Capital, which has developed a proprietary technology platform that leverages big data and research from Wharton.