Predictions for 2016: Self-Driving Trucks, AI, and Brain Monitoring

8 Jan

This post originally appeared on Xconomy here.

Whether we have been in a tech bubble or not is frankly not that interesting. What is interesting is that the foundation for innovation is as strong as we’ve ever seen and entrepreneurs are bringing the future to reality at an amazing pace. Here are a few of my predictions for what we’ll see in 2016:

1. Self-driving vehicles hit the road for real, led by commercial trucks on interstate highways. So far most conversations around self-driving cars focus on personal vehicles. It’s unlikely we’ll take a fully autonomous car for our daily commute for quite a few years, but commercial trucking will see self-driving vehicles emerge far sooner. One company, Peloton, is already making this a reality. Long haul trucking is an easier technical challenge because interstates are generally straight lines, well-marked, digitally mapped in high definition, and generally free of pedestrians, bicycles, and other random obstacles; and the economic productivity of trucks can justify substantial investment in sophisticated cameras, sensors, and computers needed for autopilot systems. The economic need for autonomous trucks is huge due to the high cost and shortage of drivers, regulatory limits on driving time, and the fuel efficiency gained from convoys travelling close together in peloton formation.

2. Artificial intelligence will improve by leaps and bounds, and so will the way we interact with it. At first Siri frustrated us with its faults, and Google Now annoyed us with random cards on our screen, but I’ve noticed that recently both systems have gotten much broader and more accurate. Most of us barely scratch the surface in terms of their capabilities. In 2016 we will become more accustomed to interacting with AI systems of all kinds that are more natural, comfortable, and intelligent. Just as it took time for us to get used to self-checkout stations at the grocery store and the early days of voicemail were profoundly awkward, societal norms will need to adjust, and designers will need to create better user experiences for us to accept without pause that we’re interacting with an AI system and not a human. As we get better at these interactions, the AI technology gets smarter. Our digital assistant will ask nuanced questions (e.g. “did you mean the fruit or the company?”) to ensure it’s answering correctly. Smarter AI will enable automation of the rote components across a huge spectrum of jobs categories including dietary/fitness training, customer service, financial advice, education, and medicine, freeing up humans to focus on the most value added components of these occupations. As our expectations for human to computer interactions continue to grow, AI systems will rise to the challenge.

3. Wearable brain-monitoring devices for mindfulness training will become mainstream. It’s rare to see a person without a smartphone glued to his or her hand in almost any setting these days, though many people are becoming aware of the downsides of digital addiction and its effect on mental and physical health and our relationships. Couple this with growing public interest in meditation, yoga, and digital detox, and we’ll see mindfulness training become front and center in 2016 in wearable device form. Wearable fitness devices like the Fitbit or Apple Watch have become part of everyday life and early pioneers in this space have developed devices to monitor brainwaves, such as the Muse Brain Sensing Headband. The irony of a digital device helping with meditation and mindfulness may make you cringe, but brain training is hard and feedback essential and self-quantification of progress almost impossible until recently. Brain wave sensing devices will improve as our awareness and need for them grows, and they’ll soon become as mainstream as heart rate monitors.

Regardless of what the financial markets do in 2016, innovation will continue at fever pitch and cool new products and technologies will become newly indispensable parts of our work and lives.

How to End Your Parking Nightmares Once and For All

3 Mar

I love cities, but a settled family and the location of our offices relegate me to life in the suburbs at present. I drive up to San Francisco several days most weeks and though I love the energy, creativity, food, culture and walkability, I truly hate parking. My parking frustrations start with the high cost, expand to the inconvenience of finding a lot and a spot (usually at the point I am already running late due to traffic), and culminate in the unpredictable risk that all nearby lots might actually be full due to a Giant’s game, a convention in town, or just the fact that it’s 8:58am on a weekday and the San Francisco economy is booming.

As a technology venture investor and former product manager I am further frustrated that parking need not be the daily battle it is in most dense cities. There are in fact enough parking spots in most cities most of the time. Reports suggest that US cities average between four and eight parking spaces per vehicle and in some cities parking lots cover more than one-third of the metropolitan footprint. The issue is the information gap of knowing where to find an available spot at a specific time, at a price you are willing to pay, as close as possible to your destination. This sort of linear optimization along three dimensions (location, time and price) would be simple for software to solve if only we had the right real-time data. Unfortunately we don’t.

I am well aware of and have indeed tried many of the parking apps that list garages and their prices, and some that even attempt to indicate (or predict) availability, and yet others that enable you to pay with your smartphone. The problem is that even in their flagship cities no app yet has particularly good coverage of all parking options and the real-time availability data is woefully inaccurate. Even if the perfect app did exist, it is hardly ideal for me to be fumbling with my smartphone at the most chaotic and stressful last mile of my drive–and the app still may dump me many long blocks away from my actual destination. A “full stack” solution is necessary.

I first heard about Luxe while chitchatting with a friend who frequently drives from Palo Alto to various meetings around San Francisco. She emphatically heralded Luxe as an app that has made her life better. I tried the service the very next day and saw exactly what she meant. The experience starts by entering your destination on the home screen of the app. Then start driving. No need to indicate your arrival time as Luxe can track your inbound progress and predict your arrival time based on distance, speed and real-time traffic. As you near your destination the app pops a picture of your specific valet with their name and a brief blurb about their personal interests, setting the tone for a very warm and human experience. The app zooms in a map to show the exact location of your valet, who is actually quite easy to spot on the sidewalk in their bright blue track jacket. You jump out, they jump in, and you are walking to your destination feeling like we live in an age of magic. Luxe will even fill your tank or get your car washed for a small service fee. When you need your car back just confirm your pickup location and click the “return my car” button in the app and watch the icon of your valet retrieving your car and then driving your car towards you. I find Luxe especially awesome when I have a series of meetings in the city such that my last appointment is far away from my first appointment–Luxe brings my car to me so I don’t have to trek my way back across town to where my day started. And the best part is that the cost of parking via Luxe is almost always 25% to 50% less than what I would pay at the nearby commercial garages. Luxe can price attractively due to the volume discounts they get from garages and the fact that their valets can run or kickscooter further from the busiest parts of town than you or I care to when we are rushing to our appointment.

To be fair, this is a very hard business to build due to bursty demand and the unpredictability of traffic, road construction, weather and other variables. Maintaining rapid response times at peak rush hours is a challenge. Algorithms which predict demand, routing and dispatch optimization, personalized CRM, and high standards in hiring, training, and live customer service are key’s to Luxe’s current and future success. Attention to detail and a customer centric culture are essential. While this is not an easy business to manage, I believe it is one of those rare business where generating demand will be far easier than fulfillment. Do not let the name confuse you, Luxe is not a service meant solely for the pampered ultra-wealthy who only fly private and gets massages in their home twice a week. Luxe is an extremely cost effective solution to everything that sucks about parking in busy cities and the service will only get better over time as they grow and expand their coverage universe.

Venrock led the Series A for Luxe because we believe in the team, the concept, and the market opportunity. Finally we can enjoy our cities, parking included.

Will There Really Be an Uber for Everything?

9 Feb


The Next Uber

This post originally appeared on TechCrunch here.

Though the press has turned on them as of late, seizing on every allegation and misstep, I love using Uber. From the very first time (September 28, 2010 to be exact) I saw the little town car icon crawling across the map coming towards my little green dot I knew taxi cabs, airport car services, and parking lot attendants in downtown SF were going to see a whole lot less of me.   Tens of millions of riders around the globe love this service so much it has become its own verb. And at a $40 billion valuation, it is no wonder that it has become cliché to describe other on demand mobile services (ODMS) as the “Uber for X”. Any offline service that can be reserved, or delivered to you physically, or transmitted to you virtually through your smartphone seems to have a startup or several trying to become the Uber for that particular vertical. A few of these will turn into very large and successful global internet brands, grabbing major market share and even greater market capitalization from the offline rivals they out innovate. Most, however, will succeed on a much more limited scale, making only a small dent in their industry and servicing limited geographic markets.

My own framework for trying to determine which markets and which companies will be truly transformational is basic in concept. Start with a service where the greatest percentage of customers are most painfully unhappy with the existing providers. Fortunately for entrepreneurs and investors (but unfortunate for our daily lives as consumers), many service sectors suffer major challenges around availability, quality, transparency, and pricing. Not all problems are equally painful, however. Most people don’t consider the logistics around getting a massage or attending a yoga class nearly the same level of pain and frustration as home renovation or trying to sell their old car privately. There is also the question of how often one needs such a service, with frequently used services having the advantage of being more likely to become sticky habits versus one-off trials that may be forgotten over time. We are much more motivated to find solutions for frequently encountered pain than occasional pain. Next, ask yourself how can an on demand mobile service leverage smartphone technology, network effects, economies of scale, rich data, crowdsourcing, and the other tools found in a tech entrepreneur’s arsenal to build a service that truly delights customers and “bends the curve” in terms of customer experience. This is obviously the really hard part. Great service is hard to consistently deliver in general, but there are those services that are innately more challenging, such as home or auto repair, where the nature of the service is to diagnose and fix idiosyncratic physical problems that catch consumers by surprise leading to an initial state of frustration and financial worry. While aspirational to think that an ODMS can fix even the most broken service sector, often the symptoms of pain in the hardest industries may need to be treated progressively over time. Thus, it is the total distance travelled between the typical incumbent service level and the redefined ODMS service level, rather than the start or end point on an absolute scale, which creates the opportunity to create a truly great business.

How can on demand mobile services create delight versus their offline incumbents?

Immediacy and Reliability—the main point of most ODMS is to use the smartphone to be your remote control for life so that when you push the button for your ODMS stuff needs to happen, as fast and consistently as possible. Uber leverages local network effects between drivers and riders, and invests heavily in data science and AI simulations to insure that rider wait times are as short as possible and drivers are as busy as possible so they can earn the most money. Without short wait times Uber would not be nearly the magical experience we all love. Another example of instant fulfillment is Doctor on Demand*, a service providing immediate smartphone video visits with a board-certified physician so that you don’t have to wait for days or weeks to get an appointment to see your doctor or head to an after-hours clinic or emergency room for routine medical needs. Clearly you wouldn’t use Doctor on Demand if you have severe chest pain or are bleeding profusely, but there are a huge variety of use cases for which you don’t need to be in the same room as your doctor and the convenience of an immediate appointment, at one third the cost (on average) compared to an in-office visit, is so compelling that employers are offering DoD as a benefit to their employees.

For the majority of services that can’t be delivered virtually like Doctor on Demand, the act of rolling out city by city is expensive and time consuming, often requiring an investment in “boots on the ground” to recruit and train workers, market to new users, and assure quality in new cities. If you are truly bending the curve with a revolutionary service breakthrough you can attain a superior growth rate which attracts the capital to enable a nationwide and even global expansion strategy like in the case of Uber or Airbnb. For many ODMS that are only incrementally improving upon the traditional service model, geographic expansion will likely have to come more slowly and may ultimately max out at the major US cities or even just a region or two. This is not necessarily a bad thing as many enduring businesses can be built as the most technologically advanced player in a region. Personally we still enjoy PurpleTie’s drycleaning home delivery services, which started as a 1999 VC backed effort to go big with an online nationwide dry cleaning service but failed and got acquired by bootstrapped CleanSleeves (who apparently liked the PurpleTie name better.) Fifteen years later operates only in the Bay Area between San Mateo and San Jose and seems to have a healthy business. Perhaps the new generation of ODMS startups providing dry cleaning and laundry deliver services will go substantially further than did CleanSleeves, and if so it will be because they figured out how to create more customer delight than just mobile app order placement and efficient delivery. My wife is quite eager to give a try, but whether or not she would stay loyal to them versus the next cheaper version will depend on how well they turn a relatively commoditized service category into a truly differentiated experience.

Quality—Service businesses are so hard to build because they rely on people to deliver service and interact with customers as much or more than they rely on computer code. Managing people, especially a workforce of independent contractors rather than full time employees, is a lot more variable than executing software routines and so recruiting, selecting, training, and managing workers is a core element of any ODMS. Background checks, license verification, detailed applications and face to face interviews are all part of the selection process. Most services rely on their customers to rate service providers and tend to ruthlessly cull those drivers/doctors/plumbers/etc that fall below a rating threshold, often a fairly high bar. Doctor on Demand checks the lighting, sound, and appearance of every doctor before every virtual shift. Some services even provide a satisfaction guarantee on the completed job, such as Red Beacon’s* $500 offer. Service quality can often be a matter of individual taste. For example, in the home cleaning category services like Homejoy may delight 9 out of 10 customers but it will be an endless uphill battle to please the pickiest consumers when it comes to something as subjective as a clean home.   Quality is not just the absence of problems, but also those unexpected touches which delight. Good Eggs, for example, would unexpectedly throw in a free gourmet treat or two when we first started the service. The freebies stopped once they hooked us as repeat customers but the amazing quality, friendly service, and personal touches like handwritten notes have made us loyal. There are simply no shortcuts when it comes to delivering consistently great services levels and ultimately quality can make or break a business regardless of whether they have the best looking mobile app.

Price—Tech enabled services are often far more efficient than traditional businesses at acquiring customers and aggregating demand through digital channels, viral marketing, and highly visible brands. This often enables cutting out layers of middlemen in the value chain. Additionally ODMS can rely on large regional facilities on cheaper real estate for physical goods processing versus sub-scale storefronts and expensive Main Street locations of their offline peers. Passing a good portion of these savings on to consumers is perhaps the smartest way to generate trial, grow quickly and hook customers on your service. BloomThat is a flower and gift delivery service that does a wonderful job of curating their selection and providing same day delivery, but their pricing advantage vs 1-800-FLOWERS is so significant that they have dramatically grown the frequency of gift giving among their customers far beyond the traditional Mother’s Day and Valentines Day holiday spikes. Some of the smartest pricing plans still include premium and ultra-premium levels, such as Uber Black or Uber Lux, for the truly price insensitive segment, but the mass market almost always appreciates a good value, especially when being asked to try a brand new service through a new medium. In the long run, however, one hopes that there is enough technology leverage, economy of scale, and disintermediation in your ODMS to be the good margin, low cost provider in your industry, not just the company most willing to subsidize losses indefinitely.

Payments—Rolling out of your UberX curbside without having to fumble through your wallet for cash nor waiting for your credit card to be run through a mobile POS is simply addictive. Getting food delivered by services like DoorDash or Seamless without the awkward eye to eye tipping procedure with the pizza guy is very easy to get used to.   Customers simply expect that effortless payment is part of the magic in a service that has been newly redefined as on demand and mobile. The nice part is that this also solves many business model problems around your workers handling cash or credit card numbers, deadbeat customers, and leakage from your workers attempting to cut you out of a side deal they offered your customer after you so nicely made the match between them.   The downside for the ODMS is that for low priced transactions the interchange fees on these credit card payments can be a significant hit to your margin, and on the other end of the spectrum certain high ticket services that require onsite estimates like home renovation may not easily lend themselves to being in the payment flow. Over time, however, we will see the vast majority of ODMS handle payments in the background as part of the consumer experience.

So, will there be an Uber for every service industry? There will be some for sure, but not many in terms of a global, dominant, hugely valuable iconic brands.   Some industries are just not important and/or frequent enough to our daily lives, or unpleasant enough as they exist today, whereas other industries face service challenges so fundamentally hard to solve that it will be a long while before we see an ODMS truly solve them. Just like with the B2B Marketplace craze of the late 1990s we will see massive experimentation across an enormous swath of the consumer services sector. We will also see traditional offline service businesses forced to up their game and become more technologically sophisticated. So while there may only be a handful or so of Uber-sized winners, there will be many smaller ODMS who find some degree of success, and the biggest winners of all will be consumers themselves.

*Current or past Venrock investment.

Why Beckon Beckoned to Me: The Arrival of Marketing Performance Management

27 Jan

Beckon 1

In today’s online and mobile world customers are educating themselves about products and services long before any flesh and blood sales persons utters a word. Consumers can easily find their own way to detailed product information, user reviews, professional reviews, demonstration videos, user generated unboxing videos, sales rankings, price comparisons, social media sentiment, buying guides, and more. For this reason, marketing is more important than ever and the Chief Marketing Officer has never been more powerful nor controlled more budget, for both media and technology.

At the same time, however, the job of a marketing leader has never been harder or more stressful. The digital landscape has become so vast and dynamic that the marketer must master an endless parade of new channels. Just as they were getting used to Facebook, Twitter, and Instagram, along comes Pinterest and SnapChat, and undoubtedly the next hot channel is just around the corner. With an expanding universe of marketing channels comes an ever increasing volume of data. With all this data available, CMOs are expected to quantify their results with the precision of a CFO. While each new channel begets a host of new adtech and marketing tools to help the CMO manage campaigns, measure performance, and optimize results, each of these solutions produces data and reports in their own unique format.

Beckon 2

It has gotten to the point that when the CEO asks “how is our marketing doing?” it strikes fear in the heart of the CMO. This perfectly benign question usually kicks off an all-night exercise of cutting and pasting data and charts from various marketing execution systems into a lengthy presentation to answer the CEO’s question. When PowerPoint (now in its 28th year) is the tool of choice for marketers to aggregate and translate performance data from their various systems you know the situation is dire. Making matters even worse is the fact that many large brands can’t even access their own campaign data as it is held hostage by their various marketing agencies. Not only does the client get charged a markup by the agency for report requests, but it’s the classic case of the “fox guarding the henhouse” to ask an agency to report on their own performance.

Several years ago, a former marketing leader from a Venrock portfolio company did a stint as an Entrepreneur-In-Residence in our offices. She identified this lack of a single “CMO dashboard” integrating data from various point solutions as a problem she had experienced firsthand. Essentially she wanted a “System of Record” for marketing. After several months of researching potential technical solutions she concluded that, despite the crying need for such a product, building one would be too difficult. She gave up frustrated and joined another best of breed marketing tool company. This unsolved problem stuck in my head.

A few years later I met Beckon. The team at Beckon have been marketers, built marketing point tools, worked in agencies, and have built, installed and used systems of record in other enterprise functional domains such as finance and sales. Having seen the problems facing modern marketers firsthand they have taken a novel approach to building a system which can pull in data from over 100 different marketing point tools. While some of this data is available via well supported APIs, much of the data comes in via spreadsheet imports and email parsing (think TripIt.)   The next thing Beckon does is normalize the data so that marketers can compare different campaigns across different channels with one common taxonomy. They allow the marketing team to add metadata such as geographies or regions, product names or categories, customer segments, agencies, objectives, and so on, in order to put the marketing results in appropriate context. They allow for What If analysis, planning, and time series tracking. Beckons creates beautiful visualizations and answers to plain English questions that don’t require analysts skilled in SQL queries. And because this is not a BI tool, but rather an application built “by marketers, for marketers”, it is loaded with best practices for omni-channel marketing performance management right out of the box with no IT Department involvement.

Beckon 3

Over the past year some of the best brands in the world have adopted Beckon. Coke, Microsoft, GAP, and BSkyB are among the clients using Beckon to manage their omnichannel marketing. The real sweet spot for Beckon are mass marketers and indirect sellers who spend across at least five different channels. While I have written about my keen interest in predictive and intelligent software, the truth is that relevant, advanced modeling is only possible if the data sets the models are built on are comprehensive, normalized and continuously updated. Finance, for example, measures its performance according to GAAP (Generally Accepted Accounting Principles), a consistent, agreed-upon methodology shared within and across companies. As a result, we can understand a company’s financial performance quarter to quarter and compare performance to other companies in a standardized way. Marketers have never had a similar system. That’s what Beckon finally brings to marketing – a strong, united data foundation upon which all kinds of consistent, robust marketing analyses can flow – benchmarking, planned versus actuals, test and control, lift over baseline calculations, econometric (mix) models and more. Beckon gives marketers self-serve access to many of these analyses within its application and can also flow its standardized, merged and continuously updated data sets to advanced analytics teams and tools.

Marketing can finally have its own system of record the way sales has Salesforce, manufacturing has SAP, Finance has Oracle, and HR has Workday. Beckon is Marketing Performance Management. Finally the CMO does not have to hide when the CEO calls (or beckons) them to their office.

Beckon 4

The Future of Software is…Wicked Smaaht

18 Apr

smarter software

Software as a Service and cloud computing has been transformational for the software industry.  But compared to what is coming next, you ain’t seen nothing yet.  First, to appreciate where we are heading a quick review of where we’ve been is in order.  Back in the olden days of business software a software company sold you an application which you installed on your servers and desktops which made business processes more efficient, facilitated workflow, and sped up information retrieval.  As you used it this software accumulated data such as your customer records, financial results and manufacturing statistics.  If you wanted to deeply analyze this data for trends and insights you bought Business Intelligence or Analytics packages from a different set of software vendors so you could slice and dice your data, generate reports for executives, and hopefully decipher interesting trends about your business that you would then go act on.  In the early 2000s Software-as-a Service companies emerged and enabled you to “rent” business applications, rather than buy them, as your employees accessed them through the Internet and their web browsers.  This came with many advantages in total cost of ownership and manageability, but fundamentally most of the first SaaS applications were about workflow and data storage/retrieval just like their on-premise software forefathers.  In the last few years we’ve had a “Big Data” explosion and a host of new open source technologies like Hadoop, MapReduce, and Cassandra packaged by a set of new companies that help businesses manage and manipulate their ever expanding mountains of data.  Also emerging is a new generation of cloud based analytics companies that make it easy to slice, dice, and visualize big data sets.

So what’s the point of this history lesson?  The point is that, for the most part, all of these business applications and more recent Big Data tools have left the burden of capturing real business insight, making decisions, and taking action on the business customers themselves.  In essence, if you wanted real business value you had to create that value yourself by getting your employees to use the applications (which often means manual data input), have analysts mine and interpret the data, and ask managers and executives to make decisions based on what they see in the reports and charts.  For example, if your company used Sales Force Automation, whether it be Siebel on premise or in the cloud, your sales reps had to diligently input data about their sales calls and management had to be smart about logging in to read the reports, suggest actions for each account and discern broader trends across the data.  A new breed of software company is emerging, however, that combines data science expertise with deep understanding of business problems.  I call them Data Driven Solutions.  These solutions use algorithmic data mining, not only on your own data but often on external third party data sets accessible by cloud ecosystems and APIs.  Data Driven Solutions make predictions about business functions, prescribe what to do next, and in many cases take action autonomously.  Trained analysts are not required to query databases but rather business users get answers directly from the software.  These answers typically feed seamlessly into the flow of business activity, often invisibly.  While this distinction may seem subtle, I believe it is fundamental and disruptive, and represents the future of software.  This is in no way the end of the SaaS, but in fact where SaaS is going next and presents massive opportunity to new SaaS innovators and a potential threat to incumbents who do not adapt.

Data Driven Solutions

8 Suggestions for Building Data Driven Applications

Think Moneyball, for everything.  Billy Beane of the Oakland A’s defied the conventional wisdom of traditional baseball talent scouts by recruiting players other teams underappreciated but whom he believed represented great return on investment.  He did this not by relying on his own brilliant sense of which players to recruit but by letting a math whiz run regression analysis on player statistics to figure out which lesser heralded stats were most predictive of winning baseball games.  The math predicted results and told him which players to acquire, predictions which Beane followed to his team’s competitive advantage.  Opportunities to apply this approach in business are practically everywhere.  6Sense* is a new SaaS company that analyzes B2B website traffic and third party data to predict which prospects are most likely to buy from you, what they will buy, when they will buy, and how much they will buy.  Like Beane, they don’t rely on rules of thumb in scoring prospects, such as whether the prospect downloaded a white paper, viewed lots of product detail webpages, or has “Procurement” in their job title.  6Sense has found that these heuristics yield only about 50% accurate forecasts which is not enough to compel a sales person to trust the results.  Instead, 6Sense uses a variety of machine learning statistical models to uncover the unexpected correlations which drive predictive accuracy up to 85-90% accuracy which definitely gets a sale rep’s attention.   Instead of being a chore to use like Sales Force Automation, 6Sense tells sales reps how to close more deals and earn more commissions.  Infer, Lattice, and C9 are also innovating in the area of predictive CRM solutions.  Use your domain expertise to figure out what problems to solve for your customers, but let the data lead to new and unexpected insights.

Build in Data Learning Loops   Google enjoys a very powerful form of Network Effect.   The more searches they run and resultant clicks they see the better they understand the intent of what a searcher wanted to find.  This makes their search algorithms better which earns them more user searches, which keeps the search quality/volume flywheel spinning.  This notion of a “learning loop” can be applied to many business settings as long as you find a way to “close the loop” and see how your prediction or answer actually fared.  For example, AppNexus* is an AdTech company that operates an exchange where publishers and ad networks on one side are matched by algorithms with advertisers and agencies on the other side to put the right ad in front of the right audience at the right time.  Learning loops are built in to the bidding and optimization algorithms which get the chance to learn from their results more than a billion times per day.  Data Learning Loops are powerful sources of competitive advantage akin to natural monopolies for those who achieve greatest scale.

Don’t Just DescribePredict and Prescribe   Some may ask whether Data Driven Solutions are just a new name for Business Intelligence.  I don’t think so.  Analytics packages mostly describe what is happening by sorting and filtering your data to show sums and averages and trend lines in tabular or graphical format.  Data Driven Solutions go much further by using the data to make predictions and even prescribe or execute actions.  A good example is the retail industry and Point of Sale results.  POS data is the basket by basket, sku by sku, store by store sales results that are collected across hundreds of thousands of retail outlets daily.  Nielsen has been compiling this data for decades and batch processes data sets for retailers and their vendors to study on a monthly basis.  How those vendors and retailers derive value from the data is up to them.  Retail Solutions* is a data driven solutions company which also gets POS data from retailers and shares it with vendors, but on a near real-time or daily basis.  More important than freshness of the data, however, is that Retail Solutions offers predictions and prescribes actions as solutions to business problems.  RSI doesn’t just create reports, they predict when you will be out of stock on a given SKU and sends mobile alerts to shop clerks, distributors and store managers to make sure the shelves stay full.  This is one of over ten predictive and prescriptive applications they provide in order to maximize return on investment for their customers.  Pretty charts are not enough.

Data is not the point, Focus on Solutions   Lots of companies market themselves as “Big Data” companies, but unless you are selling to IT Departments whose problems actually include managing lots of data, most business customers don’t really care about data.  They care about solving business problems.  Athenahealth* helps doctors get paid faster.  Turns out cashflow is really important to doctors and as a result Athena has grown very quickly and is now one of the largest SaaS companies.  Doctors don’t care how Athena actually does what they do, which happens to involve statistical analysis of massive amounts of insurance claims data and heavy use of learning loops.  The team at Athena deeply understands healthcare and doctors and so astutely markets themselves as a complete solution to real problems, and resist pounding their chest about how smart they are at Big Data.  In fact, the word “data” does not appear even once on their homepage.  Smart move.

Horizontal Strategy: Solve New Problems in New Ways   Providing applications for horizontal business functions like sales, finance, or human resources that function similarly across many industries represents very large opportunities because the market sizes are huge.  As a result there are powerful SaaS incumbents, such as, Netsuite, and Workday, in each of these functional domains.  As you would expect, these players are starting to add data driven application intelligence to their offerings.  Fortunately for startups the challenges businesses face are constantly changing thus creating opportunities to be the first to solve new problems with a new approach.  In the realm of marketing, for example, “Content Marketing” is the hottest new trend and is the digital marketing approach seeing the greatest increase in budget allocation.  Yet marketers are highly confused as to what content to produce, how to produce it, where and how to distribute it, and especially how to measure ROI.  Captora is a young startup that has jumped on this new problem with data and domain expertise and is seeing rapid growth and using their head start to establish a beachhead before direct competition comes at them.  Knowing the experience of the team they won’t be resting on the side of the road but rather racing ahead to broaden their solution in synch with new challenges facing modern marketers.

Vertical Strategy: Feed the Starving   Providing deep solutions in specific industry verticals like healthcare, entertainment or education can be a huge opportunity.  This is especially true in industries where data has largely been non-existent or hard to access as has been the case in the three industries I just mentioned.  If a Data Driven Solution can access, interpret, or create new data and use it to solve a big problem the market reaction can be like a starving person being offering a hot meal.    Castlight Health*, for example, solves the problem that in healthcare it is generally impossible to know what a given service (an office visit or a test for example) will cost until after you’ve consumed the service and you receive your bill 30 days later.  It turns out that the variance in pricing for even a commodity service like an MRI test can be 5 to 10x in a given 5 mile radius.  If one could know the price difference ahead of time they can consume intelligently–as we do in most other shopping situations.    Large employers, who tend to be self-insured, really like the idea of helping their employees spend less on healthcare as those savings drop straight to the bottom line, and as a result some of the largest employers in America have adopted Castlights’ solution.  Customers like CVS Caremark, Microsoft, and Wal-Mart don’t really care about the big data blahdy blah that Castlight uses to come up with their solution, they just know they are starving for ways to lower their employee health care costs and Castlight has an effective solution.

Vertical Strategy   While some industries are just getting their first taste of Big Data, others have been sophisticated handlers and miners of Big Data for a long time, such as the investment industry, airlines, and eCommerce.  In those fields a small incremental advantage afforded by a data driven vertical solution can be extremely valuable.  DataMinr* is a company that transforms the full Twitter stream of public tweets using sophisticated math to discern important news events amid all the noisy babble as quickly as possible ahead of the media.  Investment hedge funds will pay handsomely for incremental advantage and getting a jump on news that might move the market or a particular stock is something they are eager to buy even amidst all their number crunching sophistication and home grown solutions.  On April 23, 2013 when the stock market “Flash Crash” occurred based on a rumor that the Whitehouse was under attack, Dataminr’s algorithms figured out the attack was a hoax a full two minutes before other new outlets and their clients were able to act on the news ahead of the market’s rapid recovery from the severe dip the rumor had caused.  It turns out that news agencies like CNN, which typically rely on human reporters and shoe leather to beak news, have also turned to Dataminr as a solution to their problem.  Dataminr thus serves both a very sophisticated big data segment, investment funds, and an industry at the opposite end of the data automation curve, the news industry, with a solution that simply could not have existed until very recently.

Consumer solutions can be driven by data too  Using Uber is a magical experience.  Push a button on your phone and a car appears within an instant to take you where you want to go—no hailing, no reservations, no need to reach into your pocket for payment, and remarkably little waiting for your ride to arrive.  If Uber simply sent messages to available drivers about customers needing rides the system might still be good but customers would have longer wait times, which wouldn’t be as magical.  Instead, Uber uses statistical analysis on data coming from their drivers and riders to predict where demand will be highest and recommends that drivers congregate there to be ready for ride requests.  Nowhere in their marketing does Uber talk about data or “quantifying” your ride patterns—consumers don’t need to know how the magic happens as long as their ride shows up quickly.  Similarly, Better Finance* makes secured loans to consumers with low or no credit so they can buy smartphones and other high-value items.  Better Finance can do this at rates far less than payday lenders because of their data driven underwriting and feedback loops coming from high loan volumes and thus their underwriting algorithms constantly improve—to the benefit of Better Finance and their customers.  Opportunities to create consumer solutions enabled by big data are everywhere…just don’t mention the word data.

Traditional SaaS and on-premise software will be around for a long time in the future and these vendors will add more and more data intelligence to their offerings.  They will be joined however, and possibly threatened by, a new generation of nimble and innovative next generation SaaS companies that will combine data and domain expertise to add massive business value to their customers.

I look forward to meeting and helping as many of those companies as possible.

*Venrock is an investor in these companies.

A Management Tool I Learned While Skiing

9 Feb

The team members all seemed this happy

The other weekend while enjoying some rare snow this season, in Utah, I had the chance to listen to Bob Wheaton the President of Deer Valley Resort Company give a talk about his management techniques.  Bob started his career at Deer Valley as a ski instructor in 1981 and worked his way up through a variety of positions.  He came across as a humble, straight shooting leader, and many of the techniques he mentioned were what you would expect from a modern business leader.  He makes sure to hit the slopes daily to ask customers and employees how things are going.  He has weekly stand-up meetings with his senior executive direct reports to synch up on operational issues.  He sends regular broadcasts to all of Deer Valley Resort Co.’s roughly 2,800 employees and he routinely holds open office hours.  One tool, however, struck me as relatively unique and powerful even though it is quite simple.  It is a weekly meeting Bob calls the Managers Meeting.

This meeting is for all of his direct reports’ direct reports, about 60 managers in all.  Interestingly, Bob’s own direct reports are not there, so the middle managers are free from having their own bosses in the room.  This serves to remove inhibitions about upsetting or upstaging your supervisor.  The minutes of these meetings, however, are carefully transcribed and distributed to ALL company employees so the senior leaders are not in the dark or suspicious about what occurred in the meeting.  The meeting is also large enough that it would be inappropriate and self-destructive  to air personal grievances about one’s boss.  It does, however, give middle managers a chance to be heard by the President in their own voice on a routine basis, and hear directly from the top rather than always through the filter of their supervisor.  The fact that the meeting is held weekly means that issues get dealt with promptly and the frequency keeps Bob in touch with operational details he otherwise might not be exposed to.  The weekly cadence means they get past the high level and into tangible and actionable topics.  It struck me as an elegantly balanced yin-yang leadership method that is both effective and efficient, and would probably work in many other industries.  I can say that the level of professionalism and smiling attitude of the Dear Valley team feels palpably different than most other resorts, and I suspect Bob’s leadership, and this particular tool, play a big part in that.

Goldilocks and the 3 SaaS Go-To-Markets Models

25 Nov

Software as a Service (SaaS) is having its moment.  Customers, entrepreneurs, and capital markets are all enamored with the SaaS model– with good reason.  For customers, software as a service can yield dramatic reductions in total cost of ownership, quicker time to value, and pricing models which let you pay for only what you need and as you go versus all up-front.  For entrepreneurs, the recurring nature of subscription pricing gives more forward revenue and cash flow visibility, enables new customer acquisition models (such as Freemium), and the single code base for all customers is significantly easier to support than custom installs on-premise or supporting multiple generations of packaged software releases (and the Operating  Systems they run on.)  Investors love the predictable revenue, high margins and high growth rates.  This love affair with the SaaS model is likely to continue for a very long time. The vast majority of business software is still custom and/or on-premise license based, so there is more than a decade of disruption and growth ahead.

When we dive one fathom deeper into the SaaS model, however, we quickly discover that there is not one single model but at least three very distinct Go-To-Market archetypes.  At one end of the spectrum are the high-volume, low priced offerings such as Dropbox, Evernote, and Cloudflare that often deploy Freemium models, providing value to millions of individual users at no charge and converting some small percentage of them to premium paid accounts.    Workgroup collaboration and social/viral features are often built in to these products to help turbo-charge organic growth and online acquisition characterized by self-service signup and setup.  There are many entrepreneurs and investors who believe the whole point of SaaS is to get away from expensive direct selling in favor of these “self-service” models.  As an example, I was recently asked by an entrepreneur if I was in the “pro-sales or anti-sales camp.”  I am pretty sure they were referring to the need for salespeople, not sales themselves.  For the record, I like sales very much.

At the opposite end of the spectrum are sophisticated enterprise offerings such as Workday, Veeva and Castlight Health that are used by large enterprises and can justify pricing of millions of dollar per year.  There solutions are usually sold by experienced field sales teams, skilled in solution selling and navigating long and complex sales cycles.  These products are feature rich in terms of end-user capabilities but also in terms of security, administration and ability to integrate with legacy systems.

In the middle are solutions that usually charge tens of thousands of dollars to low hundreds of thousands per year and are sold largely over the phone by an inside sales team and can be reasonably configurable.  Customers may be medium sized companies or departments or business units of larger companies.  Examples of this model are Salesforce, Netsuite, Hubspot, and Smartling.

So which of these three models are best?  Is there one “just right” answer as there was for Goldilocks?  Or do we take the Three Bears perspective that as long as you line up the size of the chair, temperature of the porridge and firmness of the bed with the needs of your target market, all three models can be equally successful.   Clearly the latter, as one can point to several highly successful billion dollar market cap SaaS providers deploying each of the three models.  The key is to line up product/market fit, sales and support, and price in a consistent and appropriate fashion.

It should be noted that it is possible to expand across models over time, such as who both sells over the phone to mid-market customers and also deploys a field sales teams to sell bigger deals to large enterprises.  Another example is which can be used by individuals, small teams, and large enterprises with pricing, feature sets and support options appropriate to each tier.

But what happens when the product, Go-To-Market strategy, and price are misaligned?  Here are the most common mistakes we tend to see:

Market too small or product too narrow for Freemium: Free is a very compelling price, especially when trying to entice consumers to try something new, and this model can certainly lead to lots of users relatively quickly.  However, employing this model in too small a market or with a product that lacks broad appeal faces the problem of there not being enough “top of funnel” free users from which some single digit percentage (typically) will convert to paying users to grow a sustainable business.  In B2B markets free can be a red herring as there ought to be enough ROI (return on investment) enjoyed by customers using your product, such that they will happily pay at least some minimal monthly payment.  Those business customers that don’t see such value likely won’t remain engaged over the long term as free users anyway.  Switching to a paid-only offering, perhaps with a brief free trial period or money back guarantee, can be an accelerant to SaaS companies if they make the change early enough to avoid the messiness of taking away a free service from your early adopters.  Some interesting case studies of SaaS offerings that saw their businesses grow rapidly when they dropped Freemium can be found here and here.  Even large SaaS companies in big horizontal markets such as DocuSign and 37Signals have greatly downplayed their free versions over time, in some cases removing them from the pricing pages of their websites, though customers can still find these free options offered if you search a little.

Underpowered and underpriced for large enterprise: We sometimes see impressive Fortune 500 logos on a customer list only to discover that the price points and deployments are quite modest.  These customers were acquired via heroic in-person selling efforts by the Founders and below market price points for non-strategic use cases.  The hope is usually  that this will catalyze “land and expand” proliferation, but unfortunately oftentimes the product is not sophisticated enough to deploy enterprise-wide or the sales team is incapable of selling at a price point that can ultimately sustain field sales efforts or a product roadmap necessary to serve large enterprise accounts.   While these “lighthouse” accounts are meant to serve as references upon which future inside sales efforts can draw credibility, the fundamental problem space can sometimes be too complex for effective phone sales to customers of any sizeAria Systems is a SaaS subscription billing provider that serves large enterprises and has found that to truly handle the needs of core business units within Fortune 500 customers requires a field sales team, sophisticated product feature sets, high touch support, and price points that can sustain such service levels.  Aria has left the opposite end of the market, serving small developers with an inexpensive and simple online billing service, to competitors that are better tuned to the broad low-end of the market and cannot compete with Aria for the narrower high-end of the market.

Overbuilding for long tail markets:  The opposite mistake from that just mentioned is trying to serve long tail markets with a product too complex and expensive for widespread appeal, leaving oneself vulnerable to much simpler, cheaper, easier to use products.  This is particularly true when marketing to developers where “cheap and cheerful” is more than adequate for most applications.  Stripe and Twilio have done a nice job of providing appropriately simple developer-centric solutions at the low ends of their respective markets, payments and voice/messaging services, stealing this opportunity from incumbent providers who were too expensive, too complicated, and too hard to do business with.

Too many flavors all at once: While true that established vendors like Cornerstore OnDemand and Concur can serve the spectrum from small business up to global enterprise, generally young startups lack the resources to serve multiple audiences at once.  Those that allow themselves to be pulled thin in multiple directions find they serve no segment particularly well and have cost structures that are unsustainable.  Better to nail one of the three basic models and let the market pull you emphatically up or down market as a means of successful expansion.   When are you ready to broaden?

My advice is to wait until you are sure that you are sufficiently up the Sales Learning Curve, that you are sure you can recoup your paid sales and marketing expenses in an appropriately short timeframe (usually a year or less) given your particular customer churn rate, margin profile and price points.  Once you are happy with your Customer Acquisition Costs (CAC) Payback  period, you can respond to market signals pulling you up or down market.  Likewise, I recommend making sure that your product is optimized for easy onboarding and support of the mid-market before adding sophisticated enterprise features to go upmarket or your development team may be overwhelmed and your user experience compromised.  In general there seem to be more examples of moving up market than down market.  It is fundamentally easier to add features and sales people to serve more sophisticated needs up market than to make a product simpler and master indirect channels to go down market.   When cooking porridge you can add salt, sugar and spice, but is much harder to take them away.

It’s a great time to build, buy or invest in Software-as-a-Service.  Recognizing that there are multiple, distinct Go-To-Market models, each equally valid in the right circumstances, enables a clear-eyed and internally consistent strategy that avoids the mistakes describe above and captures the high level benefits of SaaS.

Slide1Note:  Companies in italics are Venrock portfolio companies.  


10 Rules For Disruptors In The Financial Services Industry

20 Mar

Having worked in the FinTech space many years ago, invested in the space for over a decade, and met with hundreds of talented teams in this area, I have observed the following ten traits among the most successful companies:

Rule #1: Unlock Economic Value   Most traditional financial service firms have invested heavily in branch networks that create expensive cost structures which result in higher prices to customers. Mass-marketing channels and poor customer segmentation also result in higher costs and marketing expenses which translate to higher prices. Online-only financial services can unlock significant economic value and pass this along to consumers. Lending Club offers borrowers better rates and more credit than they can get from traditional banks, while offering lenders better rates of return than they can get from savings accounts or CDs. SoFi is disrupting the world of student loans with better rates to student borrowers and superior returns to alumni lenders relative to comparable fixed income investment opportunities.

Rule #2: Champion the Consumer   Consumers are disenchanted and distrustful of existing financial institutions. Let’s take this historic opportunity to champion their interests and build brands deserving of their love. The team at Simple has envisioned a new online banking experience that puts the consumer first via transparency, simplicity and accessibility. Its blog reads like a manifesto for consumer-friendly financial service delivery. LearnVest is another company on a consumer-first mission to “empower people everywhere to take control of their money.” Its low-cost pricing model is clear and free of conflicts of interest that are rampant in the financial sector.  There is plenty of margin to be made in championing the consumer. The speed at which consumer sentiment spreads online these days creates an opportunity to become the Zappos or Virgin Airlines of financial services in relatively short order.

Rule #3: Serve The Underserved  In my last post explaining why the FinTech revolution is only just getting started, I described how the global credit crunch left whole segments of consumers and small businesses abandoned.  Some segments at the bottom of the economic ladder have never really been served by traditional FIs in the first place. Greendot was one of the pioneers of the reloadable prepaid cards bringing the convenience of card-based paying online and offline to those who lacked access to credit cards or even bank accounts. Boom Financial is providing mobile to mobile international money transfer at unprecedented low rates and ultra-convenience from the US to poorly served markets across Latin America and the Caribbean, and eventually globally.   No need for a bank account, a computer, or even a trip downtown to dodgy money transfer agent locations.

Rule #4: Remember the “Service” in Financial Service  Just because you are building an online financial service does not mean that your service is only delivered by computer servers.  When dealing with money matters many people want to speak to a live person from time to time or at least have this as an option just in case. Personal Capital delivers a high tech and high touch wealth management service via powerful financial aggregation and self-service analysis tools, but also provides live financial advisors for clients who want help in constructing and maintaining a diversified and balanced portfolio. These advisors are reachable via phone, email, or Facetime video chat.  As a rule of thumb every FinTech company should provide a toll-free phone number no more than one click from your homepage.

Rule #5: Put a Face on It  Chuck SchwabKen FisherJohn BogleRic Edelman.  These stock market titans may have very different investment styles but they knew that consumers want to see the person to whom they are entrusting their money and as a result they each plastered their face and viewpoints all over their marketing materials, websites, and prolific publications. If your startup wants consumers to entrust you with their nest eggs, you ought to be willing to show your face too. This means full bios of the management team, with pictures, and clear location for your company as well as numerous ways to be contacted. It’s also a good idea to make sure that your management team have detailed LinkedIn profiles and that a Google search for any of them will yield results that would comfort a consumer.

Rule #6: Be a Financial Institution, not a vendor  The real money in FinTech isn’t in generating leads for FIs or displaying ads for them. That can be a nice business, but the real margin is in making loans, investing assets, insuring assets, or settling transactions. In just a few years Wonga has a become a massive online lender in the UK by instantly underwriting and dynamically pricing short term loans. Financial Engines and a new crop of online investment advisors make and manage investment recommendations for their clients.  You do not need to become a chartered bank or an investment custodian as there are plenty of partners that can provide this behind the scenes, but if you can brave the regulatory complexity and develop the technology and skills to underwrite and/or advise exceptionally well, the opportunities are huge.

Rule #7: Use Technology Creatively  The incumbents have scale, brand history, brick and mortar presence, and armies of lawyers and lobbyists. If FinTech startups are going to disrupt the incumbents, you will need to work magic with your technology. How clever of Square to use the humble but ubiquitous audio port on smart phones to transmit data from their swipe dongle and for using GPS and the camera/photo album to make everyone feel like a familiar local when using Square Wallet.  MetroMile is a FinTech revolutionary disrupting the auto insurance market by offering pay per mile insurance so that low mileage drivers do not overpay and subsidize high mileage drives who tend to have more claims.  They do this via a GPS enabled device that plugs into your car’s OBD-II diagnostic port and transmits data via cellular data networks in real-time.  Start-ups playing in the Bitcoin ecosystem such as Coinbase and BitPay are certainly at the vanguard of creative use of technology and are tapping in to the mistrust of central banks and fiat currencies felt by a growing number citizens around the world who trust open technologies more than they do governments and banks.

Rule #8: Create Big Data Learning Loops  Of all the technologies that will disrupt financial services, Big Data is likely the most powerful. There has never been more data available about consumers and their money, and incumbent algorithms like Fair Isaac’s FICO scores leave most of these gold nuggets lying on the ground. Today’s technology entrepreneurs like those at BillfloatZestCash, and Billguard are bringing Google-like data processing technologies and online financial and social data to underwrite, advise and transact in a much smarter way. Once these companies reach enough scale such that their algorithms can learn and improve based on the results of their own past decisions, a very powerful network effect kicks in that makes them tough to catch by copycats who lack the scale and history.

Rule #9:  Beware the Tactical vs. Strategic Conundrum  One challenge when it comes to financial services is that the truly strategic and important financial decisions that will impact a person’s financial life in the long run, such as savings rate, investment diversification and asset allocation, tend to be activities that are infrequent or easily ignored.  Activities that are frequent and cannot be ignored, like paying the bills or filing tax returns, tend to be less strategic and have inherently less margin in them for FinTech providers. Real thought needs to go into how you can provide strategic, life changing services wrapped in an experience that enables you to stay top of mind with consumers so that you are the chosen one when such decisions get made. Likewise, if you provide a low margin but high frequency services like payments you must find a way to retain customers for long enough to pay multiples of your customer acquisition cost.

Rule #10: Make it Beautiful, Take it To Go  A medical Explanation of Benefit is possibly the only statement uglier and more obtuse than a typical financial statement.  Incumbent FI websites are not much better and over the past ten years many large FIs have heavily prioritized expansion of their branch networks over innovating and improving their online presence.  As a FinTech startups  you have the golden opportunity to redefine design and user experience around money matters and daresay make it fun for consumers to interact with their finances.  Mint really set the standard when it comes to user experience and beautiful design, while PageOnce pioneered mobile financial account aggregation and bill payment.  To deliver a world class consumer finance experience online today one needs to offer a product that looks, feels, and functions world class across web, mobile and tablet.

There has never been a better time to be a FinTech revolutionary, and hopefully these rules for revolutionaries provide some actionable insights for those seeking to make money in the money business.

Why The Financial Technology Revolution Is Only Just Getting Started

20 Mar


The Occupy Wall Street protestors are gone (for now), but the real revolution against banking is still taking place at breathtaking speed, thanks to a new breed of technology entrepreneurs. The financial services industry, long protected by complex regulations, high barriers to entry and economies of scale, is ripe for disruption. Here’s my take on the macro environment, how consumer attitudes are changing and why technology and available talent make now the best time to challenge the status quo.

Global credit markets clamped shut in late 2008 and froze entire sectors of consumer credit. Mortgages became less available, millions of credit cards were revoked, lines of credit dried up, and banks essentially abandoned the small business and student loan markets. This left tens of millions of households in the position of “underbanked” (have jobs and bank accounts, but little to no credit) and the “unbanked” (no traditional banking relationship at all.)  This credit crunch fueled demand for startups like WongaBillfloat, and OnDeck Capital to establish themselves and grow rapidly, and the reloadable prepaid card market pioneered by GreenDot and NetSpend soared. While credit has eased for certain segments in certain markets, there are still big opportunities to fill credit voids, especially at the lower end of the market.

The last few years have seen significant changes in banking, payment, tax, investment and financial disclosure regulations. While complex legislation such as the Dodd–Frank Wall Street Reform and Consumer Protection Act is hardly intended to unleash entrepreneurial innovation, and virtually no single person can comprehend it in entirety, it does contain hundreds of provisions that restrict incumbent business practices, and typically when there is change and complexity there are new opportunities for those that can move quickest and are least encumbered by legacy. Other regulations such as the Check 21 Act which paved the way for paperless remote deposit of checks, and the JOBS Act crowd funding provision are examples of technologically and entrepreneurially progressive laws that create opportunities for entrepreneurs and tech companies. Inspired by the success of pioneers such as microfinance site Kiva and crowd funding sites like KickStarter and indiegogo, I expect that once the JOBS Act is fully enacted and allows for equity investments by unaccredited investors we will see a surge of specialized crowd funding sites with great positive impact on deserving individuals and new ventures.

Within a few weeks of Occupy Wall Street in Sept 2011, protests had spread to over 600 U.S. communities (Occupy Maui anyone?), hundreds of international cities (did I see you at Occupy Ulaanbaatar Mongolia?), and every continent except Antarctica. Regardless of what you think of such protests, it is safe to say that as a whole we are more skeptical and distrustful of financial institutions than virtually any other industry. Clay Shirky’s term “confuseopoly”, in which incumbent institutions overload consumers with information and (sometimes intentional) complexity in order to make it hard for them to truly understand costs and make informed decisions, is unfortunately a very apt term for the traditional financial services industry. There is thus a crying need for new service providers who truly champion consumers’ best interests and create brands based on transparency, fairness, and doing right by their customers.  Going one step further, peer-to-peer models and online lending circles enable the traditional practice of individuals helping one another without a traditional bank in the middle, but with a technology enabled matchmaker in the middle.  Perhaps the ultimate example of bypassing the mistrusted incumbents is the recent acceleration in the use of Bitcoin, a digital currency not controlled by any nation or central bank but by servers and open source cryptograpy.

As a Product Manager for Quicken back in 1995 I remember sweating through focus groups with consumers shaking with fear at the notion of online banking. Today it is second nature to view our bank balances or transfer funds on our smartphone while standing in line for a latte.  And while Blippy may have found the outer limit of our willingness to share personal financial data (for now), there is no doubt that “social” will continue to impact financial services, as evidenced by social investing companies eToro and Covestor. You can bet it will be startups that innovate around social and the incumbents who mock, then dismiss, then grope to catch up by imitating.

I think we will look back in 20 years and view the smartphone as a technical innovation on par with the jet plane, antibiotics, container shipping, and the microprocessor.  While the ever improving processing power and always-on broadband connectivity of the smartphone are the core assets, it has been interesting to see such widespread capabilities as the camera, GPS, and even audio jack used as hooks for new FinTech solutions.  While there are over a billion smartphones worldwide, the ubiquity of SMS service on virtually all mobile phones means that billions more citizens have mobile access to financial services 24×7 no matter how far they live from physical branches.  Cloud and Big Data processing capabilities are further fueling innovation in financial technology typified by the myriad startups eschewing FICO scores in favor of new proprietary scoring algorithms that leverage the exponential growth in data available to forecast credit worthiness.

Financial institutions have long employed armies of developers to maintain their complex back office systems but until recently the majority of these developers worked in programming languages such as COBOL which have little applicability to startups.  While COBOL has not gone away at the banks, more and more of the technical staff spend their time programming new features and interfaces in modern languages and web application frameworks that provide highly applicable and transferable skills to startups only too happy to hire them for their technical training and domain experience.  In addition, successful FinTech companies from the early days of the internet such as Intuit and PayPal have graduated experienced leaders who have gone on to start or play pivotal roles in the next generation of FinTech startups such as SquareXoom, Kiva, Bill.comPayCycleOutRight, Billfloat, and Personal Capital.

These are just some of the reasons now is a great time for financial technology startups and why venture capital is flooding in to the sector.  In my next post I will offer some suggestions for FinTech revolutionaries.

Network Effects are Magical

30 Mar

ImageNetwork Effects are magical.  They are the pixie dust that makes certain Information Technology businesses, especially on the Internet, into juggernauts.  They can be found in both consumer and enterprise companies.  Network Effects are special because they:

  1. Provide  logarithmic growth and value creation potential
  2. Erect barriers to entry to thwart would-be competitors
  3. Can create “Winner Take All” market opportunities

Network Effects are like a flywheel–the faster you spin it the more momentum you generate and enjoy.  But not all markets lend themselves to Network Effects.  They are not the same as Economies of Scale where “bigger is better.”  To be certain, Economies of Scale can give strong competitive advantage and defensibility to the first to get really big (or Minimum Efficient Scale as the economists call it.)  For example, SAP and Oracle benefit from having massive revenue bases which enable them to employ armies of engineers who develop rich feature sets and also to hire huge sales forces.  However large these companies are today, though, their growth rates, especially in their early years, were far more modest compared to those Network Effect companies whose growth resembled a curved ramp off of which they launched into the stratosphere.

There are four main types of Network Effects:

  1. Classic Networks, in which the value of a product or service increases exponentially with the number of others using it.  Communications networks like telephones, fax, Instant Messaging, texting, email, and Skype are all examples.  Metcalfe’s Law captured this as a simple equation where the Value of a network = N², where N is the number of nodes.  Typically, each node in a classic network is similar to each other and possesses both send and receive capabilities.  This will become clear juxtaposed against the other network effects below where there are different types of nodes.  Other examples of classic Networks are social networks (eg Facebook) and payments (eg PayPal).
  2. Marketplaces, where aggregations of buyers and sellers attract each other.  Lots of sellers means variety, competition, and price pressure, which all serve to attract more customers.  And because the customers flock, more sellers are enticed to participate in the marketplace.  eBay, stock exchanges, and advertising networks are all examples.  One nuance of marketplaces, however, is they differ in terms of the scale required for acceptable liquidity.  For example, ad networks can achieve sufficient reach and liquidity at relatively low levels which is why you see thousands of online ad networks, where they each exhibit network effects but not in a winner take all fashion.  Stock exchanges and payment networks require far greater scale for network effects to operate, which is why you see much greater concentration in these industries.
  3. Big Data Learning Loops.  “Big Data” is all the rage in techland, but just having gobs of data is not necessarily a Network Effect, nor any sort of competitive advantage per se.  What you really need is unique data and algorithms that process that data into insights which then lead to decisions and actions.  A flywheel effect comes when you get a critical mass of data that you mine for insights; pump that value back in to your product or service; which attracts more users which get you more data.  And so on.   Venrock portfolio company Inrix is a good example, where they mine GPS data points to derive automotive traffic flow data.  The more commercial fleets, mobile app users, and car companies they can get data from, the better their traffic analysis becomes, which gets them more users and hence more data.  They turn data into an accuracy advantage that earns them the right to get even more data.
  4. Platforms are a very special and powerful form of network effects.  In Information Technology, a true “platform” is where other developers build technology and businesses on top of your technology and business because you offer them one or more of the following:
    1. Lots of users/customers, and you represent a distribution opportunity for them
    2. Compelling development tools, technology, and (sometimes) advantageous pricing
    3. Monetization opportunities

Example include Operating Systems like Microsoft Windows, Apple App Store, and Amazon Web Services.

Each of these four types of network effects can be extremely powerful on their own.  Yet, even more power is derived when a business can harness multiple types of network effects in synergistic ways.  Google, Apple and Facebook do this for sure, but a less well known example is Venrock portfolio company AppNexus that operates a real-time online advertising exchange and technology platform.  The exchange aggregates advertisers, agencies, publishers and ad networks for marketplace liquidity, but also offers a hosting and technology platform for other AdTech companies and ad networks to augment their own businesses.  And the vast troves of data AppNexus processes every millisecond flows back into the system as optimized and targeted ad serving.

Network Effects are what you want fueling your business.  Sometimes you just need to get clever about discovering and harnessing them.


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