Dec 22, 2019
Phil Dur and Brian Mulvey are Managing Partners of PeakSpan Capital, a growth equity firm they co-founded in 2015 following seven years of working together at Investor Growth Capital. With offices in New York City and Silicon Valley, PeakSpan is pioneering a differentiated growth equity model that marries technology enablement with Partner-led relationship cultivation, positioning itself as the partner of choice to the management teams of leading growth-stage, B2B software companies. PeakSpan complements this approach with a focus on fewer than ten concurrent investment themes to enhance the knowledge and value they bring to the boards they join. Prior to Investor Growth Capital, Brian worked in the Boston and London offices of Summit Partners, and Phil worked at Morgan Stanley Venture Partners and Morgan Stanley Capital Partners.
When rich, highly accurate and curated pipeline is provided automatically to a growth equity firm through the application of technology, exciting opportunities become available. A technology-enabled model elevates prospect identification and cultivation from a largely manual process by a firm’s most junior team members into the hands of experienced, senior Partners. This allows the decision-makers who will ultimately consummate any partnership to engage directly with entrepreneurs right from the initial outreach, through every ensuing discussion and ultimately to closing, resulting in high confidence and conviction.
Growth equity, which sits between venture capital and private equity, has seen increased momentum over the last several years (after decades in the making) to emerge as a distinct asset class. Increased interest has been driven in part by growth equity’s perceived embodiment of some of the best attributes and qualities of the two asset classes it borders — namely, attractive relative downside protection (consistent with private equity) coupled with aspirational exposure to strong upside return potential (consistent with venture capital). The ultimate goal of a well-executed growth equity strategy is to produce a portfolio with a more concentrated set of likely return outcomes than would be the case with an early-stage venture capital strategy (where returns tend to be more ‘hit-driven’) as well as a lower expected capital loss ratio.
In their 2013 report, Growth Equity Is All Grown Up, Cambridge Associates likened growth equity to an old Reese’s Peanut Butter Cup television ad (‘You’ve got venture capital in my private equity!’ ‘No, you’ve got private equity in my venture capital!’). While the analogy was tongue-in-cheek, the data presented by Cambridge exhibited the power of the model and pointed to ten- year US end-to-end net asset class returns of 12.7% for growth equity versus 14.2% for buyouts and 6.9% for venture capital, along with a capital loss ratio for growth equity of 13.4% versus 15.1% for buyouts and 35.4% for venture capital.(1)
A properly executed growth equity investment strategy has the potential to deliver this powerful combination of risk-mitigated returns. Target businesses have typically evolved to a point where many of the ‘binary’ risk elements that characterize classic venture capital investing are more predictable and manageable (for example, product or technology risk, business model risk, market adoption risk, etc.) whilst still being in the steep slope zone of the company and/or market value creation curve. Over the course of the investment, the primary return driver for growth equity investors tends to be revenue growth, strategic development, and evidence of (or the potential for) attractive operating leverage over time (as opposed to financial engineering, financial leverage or valuation multiple expansion.) Growth equity investors also tend to be more cautious than their venture capital counterparts when it comes to pricing, structure, and governance and will often pursue investment opportunities that have been more lightly capitalized than is frequently the case in venture-backed businesses — historical capital efficiency can be a strong predictor of excellent product/market fit, a relatively gentle competitive dynamic, a sound unit economic model, and solid managerial ‘DNA’. Growth equity investors seek to optimize efforts on investment prospects that, by definition, are not “frothy” (i.e. highly contested and priced); they do so by avoiding geographies with dense investor concentration, like the San Francisco Bay Area, and/or “over-hyped” industry segments, which are more susceptible to overpopulation by startups and investor interest, both of which can lead to higher pricing.
While the business, category, and investment profile sought by growth equity investors is relatively attractive, execution of a successful strategy in this category carries a unique set of challenges. These include:
Media coverage over the last several years has been dominated by the meteoric rise of so-called “unicorns” — companies with outsized visibility, valuation expectations (by definition, unicorns are valued at $1bn+), and risk appetite. These unicorns generally pursue a growth-at-all-costs mantra, which often consists of unsustainable investment in sales and marketing programs (from our perspective). As opposed to hunting unicorns, growth equity investors are more closely akin to deer hunters, as their target investment prospects tend to have much lower relative visibility and are more geographically dispersed. An essential element of the growth equity investment mandate is efficiently identifying and engaging with high-quality prospects (“deer”) while avoiding those that upon closer review are a poor fit for the firm’s investment mandate (“goats”). This is difficult since deer are shy and do not make themselves immediately visible; moreover, there are a lot of hunters and the forest is vast and opaque. Knowing where to deploy experienced hunters (analagous to Partners in a growth equity context) is challenging, and poor allocation of resources can produce a dismal pipeline and a bare portfolio larder.
In light of these constraints, when growth equity emerged in the 1980s, a disruptive sourcing model was innovated that over time has become almost synonymous with growth equity itself. Due in no small part to the success of the early growth equity firms, this model has now been widely adopted by dozens of investment firms. Under this approach, colloquially referred to as a “boiler room” model, firms hire and deploy large numbers of their most junior team members (Analysts or Associates) who typically are freshly minted college undergraduates or recent graduates from an investment banking analyst program. These junior professionals engage in an intensely metrics-driven “cold” outreach program, consisting of outbound contacts to thousands of unique company leads sourced from a broad range of marketing lists and databases. Their goal is to establish CEO contact and qualify “fit” for their firm’s investment strategy, with a successful call leading to a visit by a mid-level professional (e.g. a Vice President). Only after fit and the presence of a live investment opportunity have been qualified does the prospect company merit a senior professional’s attention and engagement. In many cases, these boiler room junior professionals have little to no actual knowledge of the target prospect’s category or business, and instead are seeking to secure financial detail (revenue growth, profitability, etc.) and a sense of the likelihood of the company to transact and raise capital in the near term.
The model can aptly be characterized as “transactional”, consisting of a high volume of outreach with low conversion, and is predicated on a senior stakeholder (typically a CEO or CFO) at a target company being willing to engage and to share sensitive information with an inexperienced and unknown caller. In effect, in a boiler room model, Analysts and Associates are beating the proverbial bushes and trying to direct qualified leads to a sales funnel, narrowing them down to a shortlist of candidates that are a good fit for the firm’s mandate. All of this is done in pursuit of the goal of “proprietary” deal flow (as an aside, we believe this is elusive or even mythical at this point, due to the number of firms deploying the same boiler room strategy). The model worked brilliantly for its early pioneers and before the advent of tools like LinkedIn, which provide CEO/CFO targets with a rapid assessment of the qualifications of the caller. With a dramatic increase in the number of firms applying the same model and, arguably, a decrease in the quality of any single outreach owing to competitive frenzy, entrepreneurs have become more wary of engaging without greater confidence in the quality of the experience they will have.
With the same boiler room approach being used by a growing number of competitors in growth equity, an alternative approach is to turn the model on its head. If the purpose of a boiler room approach is to qualify “deer” at scale, how can one do this better, faster, with greater accuracy, and with a differentiated approach?
Marc Andreesen was right when he famously penned that “software is eating the world.”(2) The disruptive impact of software across countless industries is increasing momentum due to these solutions producing new and exciting types of digital data never before available. Public market investors, particularly hedge funds, took notice years ago, to inject greater intelligence and efficiency into their own investment strategies. However, the utilization and impact of software has been noticeably more muted within private markets, including growth equity. The rise of digital “breadcrumbs” — the data trail within websites and software-as-a-service applications that companies and individuals leave behind in the normal course of business (e.g. job postings, resumes, social following, content marketing, etc.) — and the amount of supplemental information available from third-party sources (product reviews from customers, financing activity, press coverage, etc.) has never been greater, and new channels of information are emerging every day. Rather than deploying the sweat of dozens of Analyst/Associate brows to try to gather data through brute human force, what if these data breadcrumbs could be swept up and clumped together in an automated fashion with the application of software to provide an accurate assessment of things investors care about — revenue scale, growth, capital efficiency, product quality, company quality, share of market voice, momentum, and many other important elements?
At PeakSpan, we’ve developed a technology-assisted approach — and, importantly, refined a process over the last eight years since its inception — which we believe highlights the potential for a new data-driven approach to prospect qualification. One of our new technology platforms, called ADA, represents a new solution to the same conundrum of identifying high-quality prospects at scale and ensuring that the firm’s Partner and team resources are directed to opportunities that are an excellent fit for our investment mandate. Our process is predicated on scale-out data collection of digital breadcrumbs related to employee movement and satisfaction data, product review and customer satisfaction data, and many other nuanced signals. We collect almost a terabyte of signal data monthly from over 60 online destinations. By applying algorithms to this signal data, we are able to estimate proprietary metrics that matter to our investment mandate with high confidence and high directional accuracy: revenue scale, growth, capital efficiency, market size, management team efficacy, etc. With ADA, we have been able to develop niche profiles for over 130,000 business software companies globally organized into over 4,000 discrete market sectors and subsectors.(3) Another platform we have developed, called DEWEY, complements our company and market intelligence by collecting and analyzing full historical content published across hundreds of relevant sources, before tagging this content based on a 65,000-term taxonomy to analyze sector trends, share of market voice and thought leadership, competitive dynamics, and market standing, among many other elements.
By applying technology, we are able to achieve the same objectives as a boiler room model, but arguably with:
The application of data allows Partners, as opposed to Associates/Analysts, to sit at the tip of the spear and serve as the face of the firm in its outreach to senior management at attractive investment prospects. When Partners are further empowered by focusing on a discrete, core group of select domains or market segments (such as information security or human capital management) and only hunt in patches of forest that they know intimately, the results can be even more powerful, as Partner and CEO/entrepreneur share affinity in interest and experience. This Senior-led model can produce a dramatically better perceived experience for the Founder/CEO, relative to undifferentiated and generalist outreach from dozens of investment juniors. The conversation can immediately be elevated to a discussion of strategic topics, rather than extraction of financial metrics, and the investor likewise benefits from a much tighter initial review process, as a domain expert is engaged from the outset.
Professor Amy Cuddy of the Harvard University Business School has done extensive research on how human beings assess one another in business engagements.(4) Her groundbreaking work has consistently pointed to an immediate assessment by counterparties of respective competence (i.e. “Can you help me?”) and confidence (i.e. “Do I trust you and should I do business with you?”). A boiler room approach fails to recognize or capitalize on this fundamental human process. By contrast, under a Senior-led model (ideally driven by a domain expert in the same category as the entrepreneur), confidence and competence can be established and reinforced by the person at a firm most qualified to and with whom entrepreneurs prefer to engage. A technology-enabled, Senior-led model focuses a precious Partner resource on the all-important entrepreneurial relationship-building process and creates the opportunity for a proprietary relationship that stands in stark contrast to the competitive field, providing the opportunity to build relationship goodwill and rapport that can result in a higher win rate on attractive terms. When conviction-filled data is married to deep domain experience, hunting conditions have never been better.