A Very personal, Personal Capital Story

29 Jun

Congratulations to the entire Personal Capital team! Today the company announced it is being acquired by its strategic partner and investor, Empower Retirement. The very attractive price makes us not only financially happy, but grateful that a sophisticated global financial services firm recognizes the innovative way in which Personal Capital is revolutionizing wealth management, and the special care and value we deliver to our customers. This is only a waypoint on Personal Capital’s journey, and there is so much more they will accomplish, especially with the reach, resources, and power of our new owners.

My personal story regarding Personal Capital began in early 1997. I was a Product Manager for Intuit’s Quicken product, which at the time was still burned on discs and shipped in boxes. The Internet was gaining steam however, and I wanted to be part of it, so I asked to work on Intuit’s fledgling personal finance portal (remember that word?), known as the Quicken Financial Network. The executive sponsor for QFN was Bill Harris, and I distinctly remember the first meeting in which I encountered Bill. There were a group of us already in a conference room when Bill came bursting into the room with the energy of Sonic the Hedgehog and the wisecracking confidence of Jon Stewart, flinging ideas at the whiteboard like Jackson Pollock. I knew then and there that this was not your typical big company executive, but an entrepreneurial executive, the likes I had never seen.

After another year or so, I finished my time at Intuit and joined Venrock as a Kauffman Fellow. Bill went on to be CEO of Intuit and PayPal after that. Bill then went into an entrepreneurial phase, mostly outside of fintech, during which I made it a point to invite him out for burritos every six months or so at Bravo Taqueria on Woodside Road. Many of his business ideas were beyond my coverage zone, but in late 2009 he started talking about a return to personal finance. His idea was to offer best-in-class digital tools for managing personal investment portfolios (from my Quicken days I knew firsthand how hard this was), while also offering investment management services under an RIA model for those that wanted help managing their nest eggs. This was in stark contrast to the way the PFM category, pioneered and dominated by Intuit, had operated–build great software but leave financial services to traditional financial institutions. The phrase Bill used was combining “high tech with high touch”. At the time the fledgling startup was called SafeBank, and I was immediately intrigued, especially by the strategy of using highly sticky free digital tools to establish a large base of customer prospects, and converting some percentage of them to paid clients of the services business over time.

In July of 2011 Venrock led the Series B in what by that time had been renamed Personal Capital. The company was still in Alpha, and would first launch to the public a few months later. I distinctly remember the issues we wrestled with while making our investment decision:

  1. Would affluent clients fork over sizable accounts to an advisory firm whom they had not met in person?
  2. Will customers react positively to our global multi-asset-class allocation strategy, which sought to control risk and optimize taxes through passive trading with continuous rebalancing, or did they want superstar stock pickers like Ken Fisher or the top wirehouses to generate Alpha?
  3. Would the customer acquisition costs and the economics of staffing live advisors be viable?

Within the first year or two of launch, the answers to questions 1 and 2 became clearly “yes”. It seems almost quaint in these days of COVID, where so much of our daily routine and heretofore IRL services have moved online, that there was a time when investment advisors met almost all of their prospective clients in person. From day zero Personal Capital’s model was phone and interactive video based, and our customers have always preferred it that way. Likewise, with global assets in ETFs (a classically passive, albeit mass produced, strategy) now surpassing $6 trillion, it is important to remember that this represents 6x growth since the year that Personal Capital was started. It turns out that Personal Capital’s highly personalized, tax efficient, Smart Weighting strategy was exactly in line with what customers wanted. 

Question three, however, took many years of hard work and fine tuning to get the business model humming. Affluent and High Net Worth households are a small percentage of the population and neither easy to reach, nor inexpensive to win as clients for such an important and considered purchase. It takes patience, human interaction, and demonstration of value to land clients of the size that Personal Capital does. RoboAdvisors (a term that barely existed when Personal Capital started) tend to go after young, digitally native clients with small accounts, and simple financial lives, and thus can acquire customers with no human interaction. Personal Capital has always offered a hybrid model of expert human advisors, combined with world class digital tools, which is not the cheapest operating model, though we’ve always believed it is the right model. It is validating to see that the industry (Robos included) has realised that wealth management is the type of service that truly benefits from skilled advice from a highly trained human who takes the time to understand a client’s needs and answer their myriad of questions. Through tireless iteration, massive investments in digital tools, and economies of scale (our investment tools are now used by over 2.5M users and we provide advisory services to over 24,000 households), Personal Capital has achieved a business model that is both efficient and highly scalable.

There was one more challenge which Personal Capital faced and nailed. CEO transitions are never easy. Founders are iconic and integral, and Bill Harris was both of these. But Bill is an entrepreneur and firestarter at his core–a one man idea factory that never rests. After nearly 8 years of leading Personal Capital, he himself realized that the company was now in rigorous execution mode, and needed a CEO whose core was disciplined and thoughtful execution. Jay Shah joined Personal Capital in October 2009, nearly two years before the public launch. He was Personal Capital’s COO for almost 5 years, and in 2017 when Bill decided to step down as CEO, Jay was the perfect choice to take the baton and lead the company through its next phase. It was as seamless a transition as I have ever seen, and I have tremendous respect for both of them for the way they handled this changing of the guard. Jay has done a phenomenal job scaling the company, sweating every detail, metric, and strategy, and has matured into a phenomenal Chief Executive. He is smart, hard working, and a truly good person. He is also lucky to have, and deserving of, the world class team that works with him 24×7 to serve our clients, building a business to be proud of. 

And on that point, beyond achieving a terrific exit, everyone at Personal Capital is proudest of the way we put our clients’ needs first, the award winning technology, and the incredibly high ethical standards which guide every decision. Thank you Personal Capital for letting me be a part of the journey. 

To Measure Sales Efficiency, SaaS Startups Should Use the 4×2

6 Jan

Once you’ve found product/market fit, scaling a SaaS business is all about honing your go-to-market efficiency. Many extremely helpful metrics and analytics have been developed to provide instrumentation for this journey. LTV (lifetime value of a customer), CAC (customer acquisition cost), Magic Number, and SaaS Quick Ratio, are all very valuable tools. The challenge in using derived metrics such as these, however, is that there are often many assumptions, simplifications, and sampling choices that need to go into these calculations, thus leaving the door open to skewed results.

For example, when your company has only been selling for a year or two, it is extremely hard to know your true Lifetime Value of a Customer. For starters, how do you know the right length of a “lifetime”? Taking 1 divided by your annual dollar churn rate is quite imperfect, especially if all or most of your customers have not yet reached their first renewal decision. How much account expansion is reasonable to assume if you only have limited evidence? LTV is most helpful if based on gross margin, not revenue, but gross margins are often skewed initially. When there are only a few customers to service, COGS can appear artificially low because the true costs to serve have not yet been tracked as distinct cost centers as most of your team members wear multiple hats and pitch in ad hoc. Likewise, metrics derived from Sales and Marketing costs, such as CAC and Magic Number, can also require many subjective assumptions. When it’s just founders selling, how much of their time and overhead do you put into sales costs? Did you include all sales related travel, event marketing, and PR costs? I can’t tell you the number of times entrepreneurs have touted having a near zero CAC when they are just starting out and have only handfuls of customers–which were mostly sold by the founder or are “friendly” relationships. Even if you think you have nearly zero CAC today, you should expect dramatically rising sales costs once professional sellers, marketers, managers, and programs are put in place as you scale.

One alternative to using derived metrics is to examine raw data, less prone to assumptions and subjectivity. The problem is how to do this efficiently and without losing the forest for the trees. The best tool I have encountered for measuring sales efficiency is called the 4×2 (that’s “four by two”) which I credit to Steve Walske, one of the master strategists of software sales, and the former CEO of PTC, a company renowned for their sales effectiveness and sales culture. [Here’s a podcast I did with Steve on How to Build a Sales Team.]

The 4×2 is a color coded chart where each row is an individual seller on your team and the columns are their quarterly performance shown as dollars sold. [See a 4×2 chart example below]. Sales are usually measured as net new ARR, which includes new accounts and existing account expansions net of contraction, but you can also use new TCV (total contract value), depending on which number your team most focuses. In addition to sales dollars, the percentage of quarterly quota attainment is shown. The name 4×2 comes from the time frame shown: trailing four quarters, the current quarter, and next quarter. Color coding the cells turns this tool from a dense table of numbers into a powerful data visualization. Thresholds for the heatmap can be determined according to your own needs and culture. For example, green can be 80% of quota attainment or above, yellow can be 60% to 79% of quota, and red can be anything below 60%. Examining individual seller performance in every board meeting or deck is a terrific way to quickly answer many important questions, especially early on as you try to figure out your true position on the Sales Learning Curve. Publishing such “leaderboards” for your Board to see also tends to motivate your sales people, who are usually highly competitive and appreciate public recognition for a job well done, and likewise loathe to fall short of their targets in a public setting. 

Sample 4×2 Chart

Some questions the 4×2 can answer:

Overall Performance and Quota Targets: How are you doing against your sales plan? Lots of red is obviously bad, while lots of green is good. But all green may mean that quotas are being set too low. Raising quotas even by a small increment for each seller quickly compounds to yield big difference as you scale, so having evidence to help you adjust your targets can be powerful. A reasonable assumption would be annual quota for a given rep set at 4 to 5 times their on-target earnings potential.

Trendlines and Seasonality: What has been the performance trendline? Are results generally improving? Was the quarter that you badly missed an isolated instance or an ominous trend? Is there seasonality in your business that you have not properly accounted for in planning? Are a few elephant sized deals creating a roller-coaster of hitting then missing your quarterly targets?

Hiring and Promoting Sales Reps: What is the true ramp time for a new rep? You’ll see in the sample 4×2 chart that it is advised to show start dates for each rep and indicate any initial quarter(s) where they are not expected to produce at full capacity. But should their first productive quarter be their second quarter or third? Should their goal for the first productive quarter(s) be 25% of fully ramped quota or 50%? Which reps are the best role models for the others? What productivity will you need to backfill if you promote your star seller into a managerial role?

Evaluating Sales Reps: Are you hitting your numbers because a few reps are crushing it but most are missing their numbers significantly? Early on, one rep coming in at 400% can cover a lot of sins for the rest of the team. Has one of your productive sellers fallen off the wagon or does it look like they just had a bad quarter and expect to recover soon? Is that rep you hired 9 months ago not working out or just coming up to speed? 

Teams and Geographies: It is generally useful to add a column indicating the region for each rep’s territory and/or their manager. Are certain regions more consistent than others? Are there managers that are better at onboarding their new sellers than others?

Capacity Planning: Having a tool that shows you actual rep productivity and ramp times gives you the evidence-based information you need to do proper capacity planning. For example, if you hope to double sales next year from $5M ARR to $10MM, how many reps do you need to add–and when–based on time to hire, time to ramp, and the percentage of reps likely to hit their targets? Too few companies do detailed bottoms up planning at this level of granularity, and as a result fail to hit their sales plans because they simply have too few productive reps in place early enough.

There are many other questions the 4×2 can shed light on. I find it especially helpful during product/market fit phase and initial scaling. We tend to spend a few minutes every board meeting on this chart, with the VP Sales providing voice over, but not reciting every line item. As your team gets larger and the 4×2 no longer fits on one slide, it might be time to place the 4×2 in the appendix of your Board deck for reference. 

One question often asked about implementing the 4×2 is what numbers to use for the current and next quarter forecasts? If your sales cycles are long, such as 6 months, then weighted pipeline or “commits” are good proxies for your forward forecasts. In GTM models with very short cycles, it will require more subjective judgement to forecast a quarter forward, so I recommend adding a symbol such as ↑, ↔, or ↓ indicating whether the current and forward quarter forecast is higher, lower, or the same as the last time you updated the 4×2. Over time you’ll begin to see whether your initial forecasts tend to bias too aggressively or too conservatively, which is a useful thing to discover and incorporate into future planning.

There are lots of ways to tweak this tool to make it more useful to you and your team. But what I like most about it is that unlike many derived SaaS metrics, the 4×2 is based on very simple numbers and thus “the figures don’t lie.” 


To Measure Sales Efficiency, SaaS Startups Should Use the 4×2 was originally published on TechCrunch‘s Extra Crunch.

Source: https://techcrunch.com/extracrunch/

Why I Fell for Nightfall AI

7 Nov

Four years ago I met a recent Stanford grad named Isaac Madan. He had an impressive computer science background, had founded a startup in college, and was interested in venture capital. Though we usually look to hire folks with a bit more experience under their belt, Isaac was exceptionally bright and had strong references from people we trusted. Isaac joined Venrock and for the next two years immersed himself in all corners of technology, mostly gravitating towards enterprise software companies that were utilizing Artificial Intelligence and Machine Learning. Isaac packed his schedule morning, noon, and night meeting with entrepreneurs, developing a deep understanding of technologies, go-to-market strategies, and what makes great teams tick. Isaac was a careful listener, and when he spoke, his comments were always insightful, unique, and precise. Within a year, he sounded like he had been operating in enterprise software for over a decade.

After two years in venture, Isaac got the itch to found another startup. He paired up with a childhood friend, Rohan Sathe, who had been working at Uber. Rohan was the founding engineer of UberEats, which as we all know, grew exceptionally fast, and today generates over $8Bn in revenue. Rohan was responsible for the back-end systems, and saw firsthand how data was sprayed across hundreds of SaaS and data infrastructure systems. Rohan had observed that the combination of massive scale and rapid business change created significant challenges in managing and protecting sensitive data. As soon as they teamed up, Isaac and Rohan went on a “listening tour,” meeting with enterprise IT buyers asking about their business priorities and unsolved problems to see if Rohan’s observations held true in other enterprises. Isaac and I checked in regularly, and he proved to be an extraordinary networker, leveraging his contacts, his resume, and the tenacity to cold call, conducting well over 100 discovery interviews. Through these sessions, it was clear that Isaac and Rohan were onto something. They quietly raised a seed round last year from Pear, Bain Capital Ventures, and Venrock, and started building.

Nightfall founders 11-04-2019 v2[2][1].png
Founders of Nightfall AI: Rohan Sathe (left) and Isaac Madan (right)

One of the broad themes that Isaac and I worked on together while at Venrock was looking for ways in which AI & ML could re-invent existing categories of software and/or solve previously unresolvable problems. Nightfall AI does both. 

On the one hand, Nightfall (formerly known as Watchtower) is the next generation of DLP (Data Loss Prevention), which helps enterprises to detect and prevent data breaches, such as from insider threats–either intentional or inadvertent. DLPs can stop data exfiltration and help identify sensitive data that shows up in systems it should not. Vontu was one of the pioneers of this category, and happened to be a Venrock investment in 2002. The company was ultimately acquired by Symantec in 2007 at the time that our Nightfall co-investor from Bain, Enrique Salem, was the CEO of Symantec. The DLP category became must-have and enjoyed strong market adoption, but deploying first-generation DLP required extensive configuration and tuning of rules to determine what sensitive data to look for and what to do with it. Changes to DLP rules required much effort and constant maintenance, and false positives created significant operational overhead. 

Enter Nightfall AI. Using advanced machine learning, Nightfall can automatically classify dozens of different types of sensitive data, such as Personally Identifiable Information (PII), without static rules or definitions. Nightfall’s false positive rate is exceptionally low, and their catch rate extremely high. The other thing about legacy DLPs is that they were conceived at a time when the vast majority of enterprise data was still in on-premise systems. Today, however, the SaaS revolution has meant that most modern businesses have a high percentage of their data on cloud platforms. Add to this the fact that the number of business applications and end-users has grown exponentially, and you have an environment where sensitive data shows up in a myriad of cloud environments, some of them expected, like your CRM, and some of them unexpected and inadvertent, like PII or patient health data showing up in Slack, log files, or long forgotten APIs. This is the unsolved problem that drew Isaac and Rohan to start Nightfall. 

More than just a next-gen DLP, Nightfall is building the control plane for cloud data. By automatically discovering, classifying, and protecting sensitive data across cloud apps and data infrastructure, Nightfall not only secures data, but helps ensure regulatory compliance, data governance, safer cloud sharing and collaboration, and more. We believe the team’s impressive early traction, paired with their clarity of vision, will not only upend a stale legacy category in security but also usher in an entirely new way of thinking about data security and management in the cloud.This open ended opportunity is what really hooked Venrock on Nightfall.

Over the past year, Nightfall has scaled rapidly to a broad set of customers, ranging from hyper-growth tech startups to multiple Fortune 100 enterprises, across consumer-facing and highly-regulated industries like healthcare, insurance, and education. In our calls with customers we consistently heard that Nightfall’s product is super fast and easy to deploy, highly accurate, and uniquely easy to manage. Venrock is pleased to be co-leading Nightfall’s Series A with Bain and our friends at Pear. After 21 years in venture, the thing that I still enjoy most is working closely with entrepreneurs to solve hard problems. It is all the more meaningful when I can work with a high potential young founder, from essentially the beginning of their career, and see them develop into an experienced entrepreneur and leader. I am thrilled to be working with Isaac for a second time, and grateful to be part of Nightfall’s journey.

Q&A: Talking fintech with Venrock partner Brian Ascher

13 Jun

This post was originally published in PitchBook

Can you tell me your view on fintech and why it’s so important?

The financial services sector is enormous and spans a variety of trillion and multi-hundred billion dollar markets from mortgages and loans, to investments, payments, insurance, and several others. Finance is an intangible concept so well suited to digital technology, yet traditionally financial services have been delivered through massive brick and mortar networks with armies of people and paper intensive processes. Fintech can provide financial services more efficiently through direct to consumer online channels as well as remove the expensive middlemen that take a heavy toll in terms of fees and commissions ultimately born by the consumers, whether those consumers realize it or not. This increased efficiency and transparency means the elimination of mispricing so that consumers pay fairer prices for better services and results, a major reason why online lending and digital wealth management have exploded over the last five years. Cost and waste comes out of the system and benefits both the consumer and the disruptor.

Banks have started to invest in their own fintech apps and services to counter startups entering the space. How will fintech startups continue to compete with large banks?

Financial service markets are generally not winner-take-all (or even “most”) the way they are in social networks, search, or eCommerce. I think we will see some huge fintech companies created that will thrive as large independent companies. But traditional banks, investment companies, and insurance carriers are not going away; instead they are already starting to adapt to the digital consumer and are experimenting with new delivery models to attract Millennial customers. There are also plenty of software companies that want to sell white label technology to financial institutions, and there are FIs that will build good solutions in-house. And of course the incumbents will continue to acquire startups for the teams, skillsets and technology. I believe we will also see more hybrid offerings that blend digital offerings with human service advisors to provide the consumer with the best of both worlds. A great example of melding digital tools with more traditional human interaction is Personal Capital, a Venrock portfolio company. They have found a way to scale the provision of dedicated advisors to clients when they want them, but also give those clients and massive numbers of free users best in class digital tools to stay on track with their personal financial management.

Speaking of portfolio companies, what are some of the criteria you look for in startups when investing in fintech?

Fintech entrepreneurs need a blend of the maverick disruptor mentality balanced with an appreciation for the regulatory compliance, security requirements, transparency and privacy requirements that goes along with handling people’s money. Fintech entrepreneurs often come from outside the financial industry but hire industry expertise into key roles. Other things we look for are a clear business model, ideally one that corrects a mispricing in the market and offers a very different value proposition, and brand experience versus the incumbent FIs.

The growth of fintech has been almost astronomical, largely in part to the amount of VC that has been invested in the space. Can fintech continue the growth we have seen, even if there’s a downturn in venture investing?

We are already seeing VC investments in fintech cool down a bit, especially in online lending where cheap loan capital has become more scarce, consumer acquisition costs have risen due to the huge number of startups funded over the past few years, and there is a sense that the big winners are already out on the field. Investment Management (aka Robo Advisors) may be next in this progression. Insurance is getting a lot of VC investment right now. I still believe that we are early in terms of consumer adoption of fintech and there is massive growth ahead for the industry as a whole.

How has the SEC been able to keep up with the growth of fintech? Is it moving swiftly enough to make sure regulations are put in place before something major happens, or is there a general lack of oversight at the moment?

It’s not just the SEC, but also Federal bank regulators, state regulators, the CFPB (Consumer Financial Protection Bureau), and a host of others. They certainly have their hands full with the sheer number of companies that are emerging and the pace of innovation, but there is plenty of scrutiny. Enough penalties have been levied and examples made that entrepreneurs are generally investing in compliance and seeking to play by the rules. These are very complex operations and rules, so inevitably there will be minor non-compliance incidents here and there, but I don’t see systemic intentional violations nor a lack of oversight.

Cybersecurity is a major concern these days and seems to end up in the headlines with a major breach far too often. Are security concerns seen to be a roadblock in the mass adoption of fintech?

The biggest breaches that have come to light have been across a wide range of traditional retailers, legacy financial institutions, and even large public internet companies. The reality is that fintech startups are not the juiciest targets since they are tiny compared to incumbent FIs. I think security is an issue that every single company has to contend with, even if you are a mostly brick and mortar retailer that accepts credit cards. Fintech startups need to build trusted brands to overcome cybersecurity fears, but also just to get consumers to trust that they are legitimate companies that will provide good service at fair and transparent prices.

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 PurpleTie.com 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 Wash.io 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 Salesforce.com 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 Salesforce.com, 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.