Darwin, meet Willy Loman

We have a saying around our shop that Darwin was an optimist.  Darwin never met Willy Loman.

It’s always interesting when people ask “where are the next great enterprise opportunities?” Many are not surprised when I cite the obvious tectonic forces of the cloud, open source development and content management platforms, Big Data, NoSQL and transformative mobile technologies.  Oh, and SaaS delivery wiping out the need for on-premise servers.

All compelling enablers to the next generation of innovation.

But an enabler just as dramatic is the disruption that’s progressively occurred over the last decade on the business side of the enterprise fence.

The business disruption enabled by SaaS delivery, subscription licensing and low-touch, high-velocity selling models enable each of the above areas of technology disruption.  Disruptive costs of delivery and customer acquisition have enabled business tectonic forces to emerge alongside their technology companions.

There‘s been much written over the past week or so since the great Oracle miss. It’s a surprise to some that the old school enterprise sales rep is an endangered species.  Truth is, the enterprise sales model as we knew it has been living on borrowed time for the past decade.  Barb Darrow of GigaOM recently wrote a great piece to this point on  the Death of the IT Salesman.

I spent the first two thirds of my career as an operating guy, building and presiding over enterprise selling efforts of all shapes and sizes. I’ve spent the last third of my career investing in next-generation software start ups that have some common threads running between them.

• They sell by means of new-school, low-touch, high velocity sales models.
• Their product value propositions are directed towards individuals and workgroups as much as organizations.
• Their products are delivered with multiple tiers of  functionality.
• They’ve perfected (or working on perfecting) the art of initially exposing just enough functionality to deliver critically-needed value to obvious problems.
• They deliver quick value and can be self-served and self-supported.
• Their user experiences are elegantly simplistic.

Business-level individuals (rather than IT staff) learn about them, quickly download a portion of the product that exposes just the right amount of value to get them hooked on their use.  These start ups don’t have expensive, outside sales forces that sell their products top-down to C-level executives via long, complicated, cross-level influencing campaigns. Or followed by still-longer deployment periods.

You see, the really big change in selling to the enterprise is about how customers buy.

Legacy software providers are forced to confront a fundamental shift in how companies buy enterprise IT.  A shift in the database mix to NoSQL products which add non-relational database capabilities to manage, query and curate unstructured data.  A shift to distributed data stores. A shift to pay-as you-consume value. A shift away from IT leading the way.

Enterprises large, medium and small no longer are willing to pay big, up-front fees for annual software and upgrades when they can easily move workloads to Amazon Web Services.  And move databases to AWS Dynamo services.  And move shared files from local networks and storage to Egnyte cloud services.

But even more importantly, the buyer has changed.  Certainly at the application level, individuals make the buying decisions now.  File-sharing and collaboration applications enable not just advocacy but virality across work groups, and eventually across organizations.

Because the buyer has changed, product design has changed.

LogMeIn, Lookout and Dropbox and others set the bar for one-button simplicity. Freemium and free trials focus on quick time-to-value. Self-served or assisted by inside sales, added functionality is up-sold and quickly deployed.  Value is shared and the gospel is spread from user to user, without IT involvement nor blessing.

And marketing has changed.  It’s now focused on supporting this low touch model.  Fine-grained analytics help to predict conversion success, to pinpoint areas where inside sales assistance is needed, and to optimize product iteration though understanding user experience.

Five years ago, it was conventional wisdom that innovative software start ups never stood a chance to sell to enterprise customers.  But that was when IT was the gatekeeper.

Now the user is the key to selling to the enterprise.

High velocity enterprise sales are all about targeting the individual as the point of entry, exposing and realizing quick value, elegant design which minimizes customer support requirements, and promoting viral expansion across organizations.

The evolutionary process is too big for Willy and his kind.  We’re well into massive transformation and we’ll never go back.

Good thing for buyers and sellers alike.

Freemium Isn’t for Everyone

Time for a guest post from @seanellis.

I’ve been fortunate to have worked closely with Sean through our investments and my BoD efforts at LogMeIn and most recently, Qualaroo, his newest company.

In between, Sean’s advised and helped build the go-to-market strategies behind Dropbox, Lookout and Eventbrite, among others.  There’s nobody on the planet that knows more about all things Freemium than Sean.

His post was just picked up by the WSJ The Accelerators series and follows. Happy reading

GUEST MENTOR Sean Ellis, founder and CEO of Qualaroo: Freemium is a popular emerging business model among software, web services and media companies. It generally means giving away a free version of your product and generating revenue by “upselling” some users to paid versions or services. It is possible to give away a free version because there is little or no marginal cost for most digital products.

The model has always been a bit controversial. I remember when we implemented it at my first startup nearly 10 years ago. My father, who also happened to be an investor in the startup, was doubtful asking: “How can you make money by giving away something for free?” Since that time, I’ve helped implement or execute the model in startups that now have a cumulative market cap of around seven billion dollars.

See what other startup mentors have to say about making money on apps.

Why Freemium Works. Freemium is the perfect response to the increasing competition for consumer attention on the Internet. According to this IAB report, over the last seven years, marketers have increased their online advertising in the U.S. by about 330%. It is now harder than ever to drive attention and trials for a digital product through online advertising.

Rather than competing to buy attention through advertising, freemium apps and services rely on strong evangelism from their free users for a large percentage of their new signups. Free users tend to be the types of people, usually individuals or very small businesses, who are attracted by the free price and spread the word when a free service provides real utility.

As you aggregate free users you create your own exclusive advertising medium for marketing your paid version to them. In advertising context is extremely important, and the free version of your product provides the perfect context for introducing your premium versions.

Common Freemium Mistakes. A common mistake when trying to execute a freemium model is to introduce a weak free offering. While this may give you a higher upgrade rate, you’ll be selling into a very small pool of free users. It is important to remember that the best freemium businesses have a valuable free version that inspires user evangelism. This user evangelism is the growth engine that makes freemium work. Without strong user evangelism, freemium generally fails.

Instead of marketing a weak free version to drive upgrade rates, focus on clearly differentiating value in your premium offering. Remember that a 2% upgrade rate on a fast growing free user base creates a more valuable company than a 4% upgrade rate on a free user base with no growth.

Freemium is Not for Everyone. Given my advocacy for the freemium model, people are often surprised that at my latest startup, Qualaroo, we recently discontinued our free version. We did this for a couple of reasons. First, our product isn’t really intended for individuals. Our primary customers are marketers, who use it to manage the customer experience on their website.

The second reason we stopped offering a free version is that our free and low-cost versions were anchoring our solution at a price point that did not support our revenue growth objectives. New customers are now happy to pay the higher prices because they haven’t been conditioned to think of it as a cheap or free solution. And as we now generate higher average revenue per customer, we can spend more money profitably acquiring them.

However, I continue to be a strong advocate for freemium as a board member at two other companies (Mavenlink and SignNow). These companies both have collaborative business solutions that benefit from not having purchase friction on an initial collaborative user experience. SignNow also meets a second factor that helps freemium work, the ability to digitally sign documents on a computer or mobile device is a value for individual users.

Should Your Company Consider Freemium? If your business category can support a freemium model, you should give it careful consideration. If you don’t use the model yourself, you’ll likely find yourself competing against someone else who does implement it.

The Democratization of Data

Big Data is the fuel to re-invent the enterprise.  And that fuel is no longer the exclusive franchise of the enterprise.

Like other assets in America these days, it’s being re-distributed.  Here, it’s to small/medium business, to the healthcare industry and to consumers.

The explosion of data and the need to index, discover, integrate and analyze it represents a tectonic shift the likes of which we’ve not seen since the rise of the Internet.

Roman Stanek, Founder and CEO of GoodData, calls this fuel and the predictive analytics opportunity “The Oil of this Century.”

We know it’s impossible to get through a day without hearing “Big Data” tossed around.  But it’s as much about “Big Analytics”.

The Democratization of Data is not about collecting data — its about about predictive analysis which enables real-time, actionable insights.  Insights put into the hands of more business managers and fewer IT managers.

In the enterprise, this gets done across legacy silos where it’s been held for years, and increasingly, outside enterprise walls with trading partners and customers.  The goal is to generate  insights which might improve performance and relationships.  Or to generate benchmarks which compare performance to peers and competitors.

In the healthcare industry, this gets done across patient, practice management and health plan claims silos.  The goal is to help provide accountable care for patients, improved health outcomes, and higher care quality.

For consumers, this gets done across the silos created by multiple social graphs. Recognizing location, preference, content, and even intent. The goal is to create better and better user experiences and granular, actionable data for monetization.

• The Volume

Data grows in size not just because of the internet, email, business applications, and enterprise legacy growth, but also because it’s being gathered every moment by iPhones, server logs, medical equipment — you name it.

Most of that data is unstructured. When we invested in Archivas in 2006, the view was unstructured data would amount to a $6B addressable market by 2015. Today, most estimates are that it will be $20B by that time. The world’s technological capacity to store information has doubled every 40 months since the 1980’s. The volume of data stored out there is so large it’s measured not in pedabytes — but zettabytes.

The bottleneck to getting value from all of this volume is analytics.  See Einstein: “Information is not knowledge.”

• The Reality

Big Data is not new. Enterprises have been working with massively-big data sets for decades. They’ve sifted through mountains of information on premise to gain insight into customers, purchasing behavior, and demographics. But they did so using rudimentary tools, where  discovery + analysis  = manually clicking through reams of files, then cataloging results into spreadsheet trending models.

• The Open Web

The information that social interaction and opt-in behavior represents is a tsunami at work. The Social Web’s rise is staggering. There are over a billion people sharing personal experiences on Facebook. Social media is creating arguably the most valuable source of information in human history.

Each day, 300 million photos are uploaded to Facebook and 400 million tweets are shared. Every person, experience, place, event, topic and organizational affiliation is being documented and discussed. There are as many objects created in Facebook and Twitter each month as there are web pages in Google’s entire index. The social web has eclipsed Google’s index as the most dynamic source of information. As Metcalfe’s Law tells us, this will only increase as even more users come online and next generation user applications become even more intuitive.

Spindle developed its social discovery engine to aim squarely at that opportunistic reality. They harvest the graph across social silos and leverage signals like time and location to deliver content sorted by preferences, themes and relationships.

• The Democratization of Data

In the Old School, “Big Data” was limited to big companies who had BI functions staffed through massive IT investments.

Now, it’s democratized to the point where one encounters it every day in every business.

SaaS is the Great Communicator.  SaaS is the major trend that has made data available and more useful to more constituencies.  Business processes have become homogenous. Packaged SaaS applications that reach extreme mass are what make Big Data solutions available to all.

SaaS has enabled hundreds of millions of users of Salesforce.com, Marketo, Workday, WordPress and other ubiquitous apps to think about how to discover, curate, integrate and analyze their data to add value to daily business and personal process. Upstarts like Nimble and Cloze collect troves of data at the nexus of personal, social and business connections. These applications throw off their own data mountains — which in turn can be integrated into other packaged arrays and processes.

Democratization means taking Big Data Analysis out of the exclusive hands of Fortune 1000 IT and placing into the hand of the Fortune 5M.

• The Democratization of Infrastructure

The Great Enablers are the Cloud, Amazon Dynamo, Microsoft Azure, and Open Source.

Hadoop puts scalable, distributed computing for large data sets in the hands of the masses. Designed to scale up from single servers to thousands of machines, it’s a certified game-changer.

Disk-based relational database products built on SQL (think Oracle) can’t handle the tsunami. The era of Big Data demands new strategies and new approaches to indexing, search and interrogation.

NoSQL distributed database models handle the massive data quantities that traditional RDMS can’t.   MongoDB and Couch are two great examples.  Both are document-oriented, open source solutions that dramatically make web and mobile developers’ lives easier, improving time to market and productivity.  All are available to developer teams large or small.

But platform technology is only part of the New Democracy.  IT developers require access to tool sets that help them design for their data business requirements.  To get entire communities to use them, they need to incredibly easy to use and customizable, easy to manage, and easy to connect with mobile platforms.

Cloudant is delivering a  “data layer” which facilitates mobile developers tapping into a back-ended “database as a service”.

Logentries, enables companies large and small to gain operational insight into machine data. Log data thrown off from web servers, application servers. They create actionable insights which enable better software development, better application performance, better website efficacy, better business decisions.

• Government Reform & The New World Order

The Great Stimulator. Government Reform. Risk management is on the minds of everyone in the Healthcare industry. In The Brave New World of Accountable Care, providers (physicians) are motivated to improve, measure and compare patient outcomes. Payers (health plans) are motivated to arrive at personalized health plans which lower costs.

Phytel, makes its bones by enabling physicians, hospitals and payers to compare patient health data to relevant populations. They can cross-mine the clinical silos with insurance claims silos to predicatively analyze treatment courses, minimize readmissions, and improve outcomes. That data also enables hospitals to compare care-giving performance against peers.

Big, game-changing stuff.  Bringing actionable data to the people to improve lives.

• The Big Quake to Come

Over the next 12 months, more unstructured data will be generated than in all previous years combined. Think about that.

The value of big data comes from the knowledge gained from it and what you do with it. The promise of big data lies within the ability to make predictions based on it.  That’s what gets people excited.

There’s no dispute as to the magnitude of the tectonic shift that’s occurring. Driven by the cloud, SaaS models and mobile devices, it has the potential for the Old School and for the nimble startup to each participate in the $1trillion transfer of wealth predicted to come.

Releasing The (Sales & Marketing) Hounds

I was at an early-stage SaaS company’s BoD meeting the other day.

The company is doing really well.  Strong, elegant, simple to use product.  Addresses a hard-to-solve, important, urgent customer problem, lots of free users, strong early conversion to paid usage. Subscription service with a high volume transactions.

Customers love them.  In a relatively short period, over 2K customers have been acquired solely as a result of inbound interest and assisted self-service. They have a very effective inbound machine. Sales are growing nicely. There’s much underway in all other functions of the company to prepare for the move to the next level.

Two discussion threads emerged:

• When’s the right time to turn up the heat to proactively drive outbound web marketing & selling efforts?
• Marketing mandate is clear.  But on the sales end, do we start now by hiring the uber Sales exec? Or do we begin by bringing on a director-level “player-coach” while we learn and iterate to develop the most effective, repeatable inside selling model?

First Question.

The easy one.  The team has done an exemplary job of building a great first product, has iterated around improving customer experience.  It’s safe to say that that a B2B SaaS product is ready to turn the corner towards building out a proactive selling effort when it’s experienced profound inbound interest.  Interest that’s proven to be compelling enough to convert to early paying customers.  When prospects hear about you, search on you, call or email you, request join.me meetings  – or best of all, are referred by paying customers or customers who delightfully blog about the company’s product — well, we’re ready for Prime Time.

Time to aggressively start the  journey to arrive at a repeatable outbound, high-velocity selling model.

Second Question.

There’s no right or wrong answer here.  Only a pattern that’s hard to refute.

The perfect recruiting & selection scenario begins with finding a sales leader that has deep success in building & leading a model that’s most directly-analogous to the starting thesis for our outbound sales model.  But this much we know — the model will change. One size rarely fits all in the brave new world of SMB & enterprise sales.

Rare is the high-velocity selling model that gets it completely right from the jump. Constant iteration is the not-so-new normal.

The first-in sales leader will need to come in with a thesis, a bit of a playbook from the the past, hopefully a passion for fine-grained analytics and process.  But most importantly — she/he will need to come in with the flexibility & agility needed to refine & refine again until the selling effort is truly repeatable, measurable & predictable.  The only way it do it right is to spend time up front on the phone and with prospects and customers.  Playing before coaching.  No hiring beyond a “pod” of 1-3 to start.

Over-generalization perhaps, but almost always best to start with a director-level player-coach.

Unless lightning strikes in finding all of the above in a VP who can balance the short and long views and strap on the headset in the early going to experience it all first-hand.  It’s rare but certainly not impossible to learn around the more-senior guy who is driven to do the above first.  And then build out process, infrastructure and team second.

Instrumenting and hiring a team around the wrong model is a momentum killer at best.  A company killer at worst.

Getting there to quickly arrive at the right high velocity model fit is a beautiful thing.  Scaling is the fun part, but the part that has got to take a back seat to rapid learning.