4 Reasons Enterprise Analytics Will Define 2015

4 Reasons Enterprise Analytics Will Define 2015

May 14, 2015

In 2015, Enterprise Analytics will not only attract more corporate spend, but also serve as a major driver of high-growth technology M&A going forward. Enterprise Analytics solutions are finally proving out their long-touted promise of eliminating waste and improving business performance in large organizations by crunching data and delivering actionable to-do’s in a way (or at a speed) that humans couldn’t. It’s a old concept, but one that in my view is just now a clear reality to corporate stakeholders.

First, let’s be clear on what I mean by Enterprise Analytics. I define the group as any software solution that helps a large organization to better understand its customers, resources or other key data, and use those insights to optimize business performance. Sectors that most commonly fit in this bucket would be Business Intelligence & Visualization (BI), Enterprise Resource Planning (ERP) (including sub-sectors HCM tech & financial management / CPM), Supply Chain Management (SCM), as well as vertical software that serves one of these purposes.

Second, let’s take a look at enterprise M&A overall to set the stage. Last year we wrote a piece in Fortune outlining where CxOs are in their current enterprise technology upgrade cycle, a trend that lies at the core of Bowery Capital’s investment thesis and, naturally, a big driver of software acquisitions. As a result of this spend shift, we expect “next-gen” technology spend to hit $375B by 2020 (upwardly revised from our last estimate) as legacy technologies are swapped out for new. Taking a look at exit data since our last update, we continue to believe that we’re in the early days of this cycle. Cumulative revenues of exited next-gen companies represent only 15% of the estimated market opportunity in 2020. Below is a chart I pulled together at the end of Q1 2015 showing the top 10 cloud exits over the preceding year, including both enterprise M&A and IPO events.

Enterprise Analytics

On the table above, only a few of the targets are really Enterprise Analytics companies. But for one, this includes IPOs, which are more defined by financial performance, private capital availability and macro markets than strategic needs amongst big corporates. Second, many of the large acquisitions over the last year or two have been buyouts / take-privates of lower-growth companies that have been around for a while, often by financial buyers.

If we were to instead take a look at notable ($100MM+ Enterprise Value) acquisitions of higher-growth, younger enterprise software companies (founded <10 years ago) over the same time period (2014+), the picture changes. Of the 12 resulting companies, 9 are squarely in the Enterprise Analytics bucket (with the remaining 3, RelateIQ, Bizo & Adometry not too far off conceptually): highlights include Hitachi / Pentaho, Insight Venture Partners / E2Open, Infosys / Panaya, and Microsoft / Revolution Analytics. The average EV / LTM Revenue multiple of these deals, moreover, was above 9x.

So why is Enterprise Analytics such a popular area of focus amongst big companies, in M&A and otherwise? And why will that accelerate into 2015?

1) Lack Of Internal Talent And / Or Buy-In

Complex technical products common in Enterprise Analytics require very specific talent types that are more likely to come from the startup world than large corporates. This is especially true in more niche markets (e.g. vertical ones, a topic we’ve covered thoroughly of late), where technology may have been slower to penetrate. Per Bain research, for example, fewer then 15% of companies in transportation, energy, manufacturing, and several other sectors view themselves as being active cloud software adopters. Cloud software, which is generally cheaper to build from the get-go, can move faster to build best-of-breed point solutions in Enterprise Analytics; but the necessary next-gen SaaS talent is the standard in startups while rare in the F500. Even if the resources and CEO buy-in are there to build vs. buy, attracting the best young data scientists for example may be harder, especially in a very entrepreneurially friendly market like ours today. When a big company sees that a startup is providing better analytics around an area of core competency than its in-house team of dozens can, that’s a strong argument for acquisition.

2) Complex Product + Need For Testing With External Data

Enterprise Analytics perform complex functions that are very difficult for large companies to build in-house from a cost and time perspective. Many large companies are finding themselves caught off-guard by how quickly the need to understand internal data has ramped. For example, certain categories of commerce moved online seemingly overnight (e.g. flowers, office supplies), and the same is happening in mobile. In order to market to, track, engage with and support customers on new platforms like mobile, enterprise retailers must employ next-gen solutions that can transact in new forms of data. Connected industrial sensors, in-store beacons, mobile marketing attribution, mobile CDN, mobile retailing / training are just a few examples. Whereas startups have been attacking the mobile analytics problem from myriad angles over the last several years, even F500 companies (and to some extent the big consultancies) are finding themselves unable to build, making the buy all the more likely.

3) Clear Value Proposition That Impacts P&L

The need for in-house analytics teams are growing exponentially (alongside the volume of enterprise data itself), and data-science talent isn’t cheap. By reducing spend allocated to services or personnel, next-gen Enterprise Analytics solutions are bringing new dollars into the tech market while cutting costs for companies. Yesterday’s personnel or consulting expenditures become tomorrow’s SaaS or IoT revenues. Because Enterprise Analytics startups have been focused on proving the value of their analysis from the get-go, moreover, even the younger players have quantitative data points showing that they can increase a key metric that likely grows revenue (e.g. conversion lifts, time to sale), cuts costs (e.g. employee efficiency or retention, compliance risk reduction), or hits the bottom-line somehow. Often, acquisitions of high-growth tech startups are seen as “visionary” plays to preserve innovation or build a future product line; just the sort of M&A the media loves but that activist investors hate. In contrast, in the case of Enterprise Analytics, the ROI is more likely to be straightforward and quantitative.

4) Valuation vs. Traction

In my view, there’s a more tactical reason why Enterprise Analytics will continue to be a hot area of investment, spend and acquisition activity this year. A huge chunk of tech M&A happens in the $50-200MM band. This range still allows an acquirer to aim for innovative, high-growth targets without breaking the bank. To explain this, let’s take the revenue multiple of ~10x from our bucket of deals mentioned above, a healthy, if not blowout acquisition multiple by anyone’s standards, and a few turns above public SaaS averages today (setting aside the difference between GAAP revenue and ARR for now). Applying this to our $50-200MM EV band, we’re left with startup M&A targets of between $5-20MM ARR. Broadly speaking, this would be post-Series A but likely before the growth rounds that vault valuations up near “unicorn” status, making them unaffordable. At say $10MM in ARR, many tech startups are still figuring out how, if at all they’ll become a truly massive business. For Enterprise Analytics startups, however, they already have a few years of traction analyzing data and producing outcomes. Said another way, Enterprise Analytics startups can prove value prop relatively early on given their more quantitative nature.

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