Why Startup Accelerators and Early Stage VCs Should Provide Centralized Analytics as a Value-Added Service

enter Note: This is a significantly longer version of an article I recently published on VentureBeat.

“Startups are the sum of the decisions made by the people who run them.” — Uzi Shmilovici, in Data Driven Decisions for Startups.

Uzi’s recent observation that “startups are the sum of decisions made by the people who run them” really resonated with me, and he’s absolutely right — the earlier startups begin collecting data, the better their decisions will be. There’s only one catch: Most startups don’t have any bench strength in Analytics. All too frequently, time spent on developing scalable transactional database schemas, coherent campaign tracking, clean success metrics, and automated reporting simply means time NOT spent developing a minimum viable product and pivoting to find their market. This effectively leaves startups to compete on product and marketing — without the ability to seriously compete on analytics.

The Big Idea

The purpose of this article is to explore a single idea: What if angel investor groups, early-stage venture capital firms, and micro VCs provided centralized analytics and data management resources as part of their investment in startups? Giving startups the tools and resources they need to compete on analytics — and to quickly gain strategic advantage over competing startups — gives investment firms and their startups a huge competitive advantage in the marketplace. As we’ll discover, centralizing an analytics group within the VC firm positions those startups to grow revenue more quickly, reduces their level of technology debt at a key inflection point in their lifecycle, and in practice provides investors with a significantly enhanced probability of return.

VCs want to maximize each of their startups’ chances for success. They also know what KPIs they want startup entrepreneurs to manage to, and they need everyone on the same page. So how, exactly, is this going to work? First, a VC-based analytics group efficiently offloads a number of distractions from the startup. It then works to organize the startup’s data infrastructure with growth in mind, while minimizing exploratory diversions and providing access to world-class analytics skillsets that would otherwise be unavailable to most startups. As a final note, we’ll briefly discuss what it takes to put this together and consider the potential return on investment that a centralized analytics organization can provide to the VC firm.

A/B Testing and Statistical Modeling

As a practitioner in direct marketing and e-commerce analytics for my entire career, one thing that’s always struck me is how long it took online businesses to independently discover and begin to effectively monetize the A/B testing and predictive modeling skillsets that have been the bread-and-butter of the direct mail industry for decades. While tools such as Google Website Optimizer and Optimizely have effectively brought A/B testing the masses, they represent only the tip of the iceberg in terms of the tests and analyses that can be done to identify, model, and optimize the range of opportunities to generate incremental revenue. In a sense, analytics is a function that picks up money your business may otherwise be leaving on the table.

The seemingly forgotten history of analytics aside, startups that are actively working to find their market niche have a huge advantage with access to statistical modeling and analysis resources. Extreme examples exist — such as Demand Media’s use of machine learning to build a billion-dollar business by beating Google at their own game. However, analytics as a practice in the Internet space has certainly come into its own and established a very strong track record of contributing revenue. While a simple A/B test can easily return gains of 5%, a one-to-one marketing model may increase conversion by well over 10%; a price optimization study may return 25% or more incremental revenue; and a business rule optimization, gained from detailed analysis of customer behavior, can be a complete game-changer.

Of course, in most scenarios, the advanced Analytics function is a specialized occupation that is highly dependent on specific infrastructure and the existence of other specialized skillsets (notably including database administration.) Most startups don’t have the resources or bandwidth to kick-start an Analytics group early in their lifecycles. What they can do — and what VCs can help them do — is to plan strategically for this eventuality. They can avoid missteps that later make starting up an analytics group more difficult and costly, and gain time-share access to skillsets they wouldn’t otherwise have access to. For startups, access to world-class analytics resources early-on provides them with a huge competitive advantage to identify and maximize conversion opportunities, evaluate and test site or product features with minimal development investment, and bank strategic data-capture opportunities for future returns.

Set Up Databases with Growth in Mind

Fresh startups are excited about their applications and about reaching their market. Having been involved in one or two myself, I’ve experienced first-hand that the energy and excitement is contagious. Startups are also anxious to get things done quickly, which often leads to setting up whatever freeware database they last used so they can start throwing records at it immediately. While this obviously works in the short-term, it inevitably leads in the medium-term to scalability issues and countless lost opportunities to store transactional and behavioral data. Depending on the database technology that was chosen, the simple task of retrieving structured data back out of the system for analysis may even require developer time that could be more productively spent on core features.

While big-data solutions have quickly become the talk of the town, relational databases aren’t going away within our lifetimes; they’re simply too useful, reliable, and effective across a large range of general applications. And it’s precisely at this point in the startup’s lifecycle — when they have a prototype that’s seen a few development cycles — that it makes sense to evaluate the existing database schemas, identify opportunities for strategic expansion and optimization, and plan appropriately for anticipated growth. This vital investment reduces the amount of technology debt the startup carries and, if done effectively, provides greatly enhanced ability for the startup to identify signals for otherwise-unseen market opportunities and inflection points that suggest a potential strategic pivot. This kind of strategic investment would be an expensive diversion for a startup in terms of time-to-market. However, it also significantly enhances the startup’s ability to gain advantage in the marketplace and increases the probability of its success. By providing this assistance as a value-added consulting service (in addition to capital infusion) the VC firm has a significant opportunity to enhance its own probability of return with minimal additional capital investment.

Offload the Distractions

You know how it is — every once in awhile you’ll get this crazy idea for a Web site and go buy the domain. Sometimes you actually work on it and maybe even get a home page up. I’m as guilty of this as the next person, and if there’s one thing I’ve learned the hard way, it’s that I can totally do my own graphic design. Eventually. After days of unproductive work. Instead of working on the actual product.

Startups, like any other small business, are beset by distractions. Accelerators have identified this and often provide startups with legal, marketing, and mentorship assistance so that the founders can spend more time building out a viable product. In addition to graphic design, legal, and some aspects of marketing, there are a number of mission-critical technologies that are easy for experts to provide but that can be a huge drain on productivity for startups. I’ll name three prime offenders:

  1. Google Analytics. Google Analytics (GA) has grown up over the past few years as an almost indispensable tool, but can be a time sink. Few organizations invest in getting their content naming / campaign tracking / event tracking methodology really nailed down; fewer still make tagging a formal or required part of their release process. In effect, it often simply doesn’t get done, and the business has either unusable data or no data at all. Creating and implementing a documented process for page / conversion / event tracking in the early stages gives startups the best-practice skills they need without having to take the time to learn it the hard way. Perhaps more importantly, it integrates a data-driven culture into the startup’s DNA at an early stage.
  2. Search Engine Optimization. SEO is often simplistically thought of as “get your meta tags right and produce a lot of content,” when in fact the most effective SEO strategy purposely directs page-rank flow toward specific landing pages (and therefore toward specific marketing objectives.) This provides an opportunity for the VC to provide high-level, strategic input to long-term marketing implementation while significantly clarifying when on-the-ground marketers should apply individual channels, including Twitter, Facebook, and inbound links. Put simply, providing startups with a framework to easily figure out “which marketing efforts go where” — and helping them rank higher in search results — makes it a lot easier for those startups to promote themselves more frequently and more effectively.
  3. Database administration. Correctly designing and implementing the startup’s database infrastructure (the database engineering or DBE side mentioned above) is critical for getting startups on the right path. Getting those databases to perform well under load, managing backup, monitoring capacity, and maintaining security measures are all vital ingredients in keeping an online business running. They are also tasks that can take non-experts a huge amount of time to manage relative to the time it takes an expert. And, given the level of risk involved — particularly with managing backups and security — offloading these non-core tasks from the startup significantly reduces the risk of a data breach or an unrecoverable data loss.

Getting Ready for Big Data

Big data has received a spectacular amount of press recently. Its popularity is growing exponentially, and with good reason: big data solutions bring data storage and analysis into the realm of possibility for applications that generate data at the petabyte level and beyond.

However, unless a startup is specifically a big data company — in which case it already has deep big-data experience — big data solutions aren’t a near-term need for most startups. If there’s one thing thing you absolutely need to know about big data, it’s this: Big data solutions are highly application-specific. My own experience with big data is that — along with some very clear potential benefits — big data makes it seductively easy for businesses to not plan for how data are going to be used other than feeding the application. At the end of the day, big data requires more work and planning than relational databases do to effectively serve multiple specialized skillsets within the organization.

Big data has gotten so much buzz that businesses spend time evaluating it even when they have no legitimate use case for it. However, a key investment a VC firm can make is to create a data roadmap, document expansion plans, and provide startups with the information and planning they need to execute effectively with the technology they have — without the distraction of evaluating and testing potential down-the-road big data implementations. This key service can help startups stay on plan and on track, sets them up to make optimal decisions later, and allows them to focus on execution rather than worrying about what happens if their product takes off overnight.

Putting It All Together

If you’re not out-innovating the competition, they’re out-innovating you. Acquiring the necessary skillsets and infrastructure for a world-class analytics organization is a significant expense for any organization, and cost-prohibitive for the average startup. However, cost-averaged across a number of startups, a world-class analytics organization can provide an enormous return for VCs and their startups for marginal investment relative to overall outstanding capital investment. In broad strokes, here’s what it might take to organize a centralized analytics consulting group within the VC firm:

  1. Hire at least two specialists — an experienced database engineer and an analyst with skills in experimental design and predictive modeling. Depending on the number of new startups that are being funded, two specialists should be able to effectively service at least a dozen different properties simultaneously, and possibly many more. Staff up when, where, and if further investment makes sense.
  2. Bring in a suite of technology — including database, reporting tools, and analytical tools — that provide your analytics team with the infrastructure they need to be successful and that make sense within the culture and constraints of your organization.
  3. Establish implementation plans and documentation for bringing best-practices into your funded startups, including everything from Google Analytics and campaign tracking to data storage conventions and what-if conversion dashboards. Identify a test-case startup and light it up.

The End

Startups with the resources, infrastructure, and guidance they need to effectively compete on analytics will have a significant competitive advantage in the marketplace. Identifying and implementing additional revenue and growth opportunities, reducing the risk of catastrophic failure, and allowing entrepreneurs simply spend more time away from distractions to focus on what they do best, all contribute directly to the startup’s bottom line — and therefore to the VC’s bottom line as well.

VC firms have a sizable financial opportunity to improve their rates of return — and to differentiate themselves in the VC market — by providing the data and analytical bench strength their startups need to be world-class competitors. By adding centralized analytics and data management resources as part of their investment in startups, venture capital firms will be positioning themselves and their lucky startups for maximum growth and success.

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