Company data is often a source of very low-hanging fruit for generating higher returns.
What does this mean for PE firms and their portfolio companies? Done right, short and intensive data & analytics initiatives can generate substantially higher returns.
Let’s get real: everyone and their moms are data scientists these days. That’s because data is fun, cool, cutting-edge - it’s the new oil. Well, if you had held oil as a portfolio investment for the last 40 years, you’d have been in for quite a rocky ride. And the reward for that risk? A gross annual return of 0.39%. That’s right, less than one half of one percent!
The metaphor is apt not because data (or oil) is worthless, but because it is worthless on its own. It must be used intelligently to create value.
Our key insight from years of data work is that most mid-market companies do not use their operations data strategically. This includes tech firms, which often use data and advanced methods in their products but usually have significant low-hanging fruit when it comes to using data to manage the direction of the company.
In the past, these represented “frosting on the cake” types of opportunities for mid-market PE firms and PE-backed companies. Now, however, intense competition for deals, leading PE firms’ significant investments in data science capabilities, and increasingly demanding LPs mean that company management and their equity sponsors cannot afford to ignore the potential value and increased returns that simple but smart uses of data can generate.
So how does this work?
First, how does smarter use of data lead to higher returns?
Simply put:
Smart data initiatives implemented quickly can reduce costs or increase revenue by 10-20% within the first year following deal close. Below, we’ll show you real-world examples of how.
These gains drop straight to the bottom line, not as a one-off event but as a sustained increase in margin and EBITDA.
At exit, a multiple of 10-20x or more gets applied to the increased EBITDA. For the typical mid-market tech company, this means a boost in enterprise value of 7% or more (i.e., millions of dollars).
The cost? Essentially nothing. There is usually no offset to EBITDA since the work involved represents CapEx, not OpEx, because the company is investing in durable, value-generating assets.
And there are large positive spillovers from the muscle built during this short period of heightened focus and collaboration. Our lean strike team runs an agile process with frequent check-ins on drafts, progress updates, and a high level of responsiveness and customization. During the engagement, the company’s management team is closely involved in the work, promoting better communication and alignment on goals and workflows moving forward. This clarity makes future value creation initiatives targeting operations, sales, and products much more successful, thereby generating significant benefits beyond the immediate data initiative.
Sure, but how does smarter use of data lead to higher returns?
Here are five real case studies where Horizon Data Science helped mid-market PE firms and their portfolio companies do just that. In each case, our work helped generate substantially higher realized or projected returns.
Smart Analytics & Dashboarding
Because you can’t operate a company efficiently without an instrument panel, and you can’t build that instrument panel with data stuck in separate point solutions.
Case Study 1: Improved operational and tactical visibility leading to a 15% increase in enterprise-wide labor productivity driven by more efficient technology utilization and better team coordination (PDF)
Case Study 2: Call center data overhaul increased customer retention rate by 10% (PDF)
Case Study 3: Sales rep incentive program powered by transaction-level data reduced costs by 40% (PDF)
In other Analytics & Dashboarding work with mid-market PE firms and their portfolio companies, we’ve developed ops dashboards that continue to be used daily by CEOs and COOs; increased win rates through data-driven sales enablement by leveraging data from multiple point solutions within the same company (e.g., SFDC and Marketo); and increased the value of an IoT-enabled data product by >50% through integration with publicly-available external data and a machine learning-enabled anomaly detection service.
Economic Modeling
We combine data science with basic principles of microeconomics to show what a company’s potential is and what it needs to do to get there.
Case Study 4: 20% productivity increase enterprise-wide from insights into organizational potential and mix of inputs required to achieve it obtained through economic modeling (PDF)
Case Study 5: Increased revenue from higher retention rates through economic modeling (PDF)
The New Commercial Due Diligence
Historically, mid-market PE firms and management teams have only thought through data & analytics capabilities and opportunities after deal close. But now, many are now gaining a competitive edge even earlier in the PE life cycle by taking data & analytics more seriously in their deal prep and commercial due diligence processes.
How exactly?
Historically, PE firms have often thought of CDD as an end-to-end solution. But many PE firms are starting to realize there is a better model. It’s one that still allows investors to get the same value and quality, low risk, tight timeline, etc., but also tap into specialized expertise to extract more helpful detail in the diligence process from a data perspective and help get everyone better prepared to hit the ground running post-close with executing on the value creation plan.
First, PE firms are engaging in more strategic and customized commercial due diligence processes - asking for much more detail in vendor scopes of work and flexibility in execution depending on results obtained midway through the process. The goal here is definitely not to confirm previously-held conceptions. Nor is it only to conduct a thorough and objective assessment of assumptions. It’s also very much about gaining actionable, granular information with which the PE ops team, company management, and the board can roll into the post-close period, get pointed on a more accurate heading, and get going faster.
Second, they’re tapping into the kinds of specialized expertise and execution with data initiatives that Horizon provides.
Here are two more case studies detailing some of our recent work supporting mid-market PE firms with specialized commercial due diligence:
Case Study 6: Refined investment thesis and laid the groundwork for post-close success by deploying cloud-based data tools to rapidly generate insights on potential drivers of market penetration through big data consolidation and economic modeling (PDF)
Case Study 7: Turbocharged deal team collaboration through rapid deployment of big data analytics and development of interactive dashboard (PDF)
Next steps
The boost to EBITDA and investor returns through smarter use of data are there for the taking. The reason? Most mid-market companies simply do not make strategic use of their operations data.
Private equity firms can help their management teams mine the value in this data and, in doing so, build more valuable relationships with those teams, substantially increase enterprise value, and generate meaningful additional returns. The same PE firms can also enhance the effectiveness of their commercial due diligence processes, seizing opportunities to use analytics, dashboarding, and economic modeling on their target companies’ data to better understand growth drivers and help smooth the transition to post-close execution of their value creation plans.
If management doesn’t have ongoing, logical visibility into the company’s operations data - for whatever reason - then they cannot be expected to run the company efficiently. Private equity firms know that in these cases, Horizon Data Science is here to help.
About Horizon Data Science
Horizon Data Science is a boutique consultancy with offices in Boston and Washington, DC. Paul Karner, Horizon’s founder, is a PhD economist and sought-after expert on data strategy and analytics. Paul and his Horizon colleagues help PE firms and management teams identify and execute on opportunities to generate higher returns by creating custom data and analytics solutions that quickly increase margins and accelerate growth. Their work generates unparalleled value through a unique pairing of highly-individualized client attention with cutting-edge tools based on economics and data science.
Contact:
Paul Karner, PhD
Founder, Horizon Data Science
paul@DataSciApps.com
(617) 843-3404