Hack Trend

Slides from On-Demand talk

A few slides from my recent presentation at an event on On-Demand Economy hosted by Fairhaven Capital’s portfolio company Drizly

http://www.slideshare.net/zhurama/on-demand-economy-primer

What Uber is really selling

I’ve come to live on Uber (regrettably, I am not an investor in the company), it’s a great service that makes me think: why hasn’t it been done sooner?  Uber is an on-demand car service, you open your smartphone, it picks up your location and sends you a car. All the payment is taken care of via your account in the app, based on distance and time of your trip.  Tip is factored in automatically.  It’s a two-sided platform where drivers sign up to the app to get business.  Uber started out as a black car service only, and now includes regular taxis and UberX which are drivers with their own cars, nice clean cars but not black cars.

So what is Uber really selling.  It’s not just the on-demand transportation.  It’s the experience - convenience and more importantly service.  In fact it’s so good, I would argue, that it makes users take a car service more often than they would normally do without Uber - in other words, it expands the market for car service. This also improves Uber’s user monetization (LTV) and justification for its user acquisition cost.  

I want to emphasize service - how is Uber able to provide better service.  Well let’s start with what the world was like before Uber.  Taxi drivers were rude, because they have a difficult low paying job that involves servicing customers going short distances and lots of idle time with no business.  But more importantly, any one rider has no power to demand better service - the cab driver can treat a customer rudely and never risk encountering the same customer again.  Cab companies are also unresponsive to complaints.

What Uber has created is a consumer advocate for better service, it has aggregated the consumers such that consumer feedback now has an impact on drivers.  Drivers are rated, and Uber stays on top of those ratings.  In fact, riders are also rated and drivers can respond to pickup requests accordingly.  Uber can create better service because it has better data.  It has information on exactly how long a trip took, the exact route the driver took and of course the bill to the rider.  So if the driver takes an in-optimal route, whether on purpose or not, and the customer complains, Uber can adjust the fares to match the average optimal route.  It’s happened to me twice and both times I got a fare reduction. Now, if a driver is rated poorly or takes improper routes, guess what, Uber can adjust their algorithm to send them less business. 

It may be costly for Uber in the short term to provide these types of partial refunds to customers based on in-optimal routes, but they know they are adjusting the fares down fairly. By routing business to the most efficient and best drivers over time, Uber creates efficiency in the overall market.  In other words, Uber is making sure that drivers are courteous and take the most optimal routes, across all drivers and all customers.  These efficiencies eventually reduces costs for consumers in aggregate, and expands the market for car service, assuming price sensitivity and supply/demand curve behavior in this market.  Uber takes a transaction fee, so it’s pretty clear that it benefits by being the enforcer of better service and therefore increasing business. (Of course, Uber’s marketplace model has built in supply/demand equalization capabilities - for example Uber dynamically adjusts pricing to ensure there are enough drivers during peak demand.  UberX is itself a mechanism to bring more drivers online and increase availability of cars.  Uber’s by-passing of taxi companies enables it to offer a larger revenue share to the drivers.)

Uber’s level of data-driven service is pretty much unprecedented in the consumer space.  EBay and Amazon have already applied reviews and ratings successfully to physical products and sellers of those products.  Now we are seeing feedback metrics being applied to service (TaskRabbit is another example). GrubHub is applying data-driven service to food, like how long it took for the food to be delivered.  Airbnb has applied it to the lodging experience etc.  There will be many more businesses created based on an improved user experience enabled by data-driven customer service that shifts the power to the consumer and improves efficiency of large markets.

NEVN takes over Fenway Park!

Thanks to everyone who came out for our Spring Fling event this past Wed. There’s nothing like renting out Fenway Park and hitting baseballs to the tunes of 90s hip hop!

I’m excited to be the new co-chair of the New England Venture Network (NEVN) this year and have a great steering committee in place to help me. With our 180 active VC members (300+ if you count alums from recent years), we are now the largest organization of young VCs on the east coast. Over the years, NEVN has served as an important networking and professional development platform for VCs in the Boston region.

Going forward, we want to continue our regular social and networking events.  There are a few areas we’re looking to add additional focus including engaging other members of the startup ecosystem such as angel investors and founders, and professional development events such as fundraising and mentoring/career coaching for VCs.  We’re also working on initiatives to support women founders and investors.

I’m looking forward to a great year ahead!  Please follow @NEVNBoston on Twitter for updates and email cochairs@nevn.org if you’re a VC in the Boston area and would like to join our organization.

Investor Pitch Workshop at GA - thanks for coming!

Thanks to everyone who came to the Investor Pitch Workshop event at General Assembly in NYC last Friday 3/30.  It was a full house, and a fun happy hour afterwards!  Here are the companies and founders that presented:

• Metropolist - JC Goodrich
• Peer Belt - Krassimir Fotev
• Screach - David Weinfeld
• Paperlex - Alison Anthoine
• TigerTrade - Tanjila Islam
• BusyFlow - Jaro Šatkevič

And thank you judges for your valuable feedback:

• Weston Gaddy, Bain Capital Ventures
• Alex Goldberg, Canary Ventures
• Kamran Ansari, Greycroft Partners

DARPA’s call for humanoid robots

Interesting announcement by DARPA that they will fund six hardware teams and twelve software teams in the Grand Challenge to design a humanoid robot. This is the extension of the same challenge for the autonomous driverless cars that we’ve all heard of.  

The requirements for the humanoid robot are:

• Navigate itself into a open-frame utility vehicle, hop into the driver’s seat, and drive the vehicle to a specified location.
• Exit the vehicle, unlock a door, and go through the door.
• Safely travel down a 100 meter-long hallyway littered with debris.
• Climb a ladder
• Fix a gas-leaking pipe
• Replace a broken pump

I wonder if there are requirements on battery power, speed, toughness, etc.  More on this from VentureBeat.

Slides from GA talk

Here are slides from my General Assembly talk on product development for entrepreneurs on 3/28/12:

Factors for Success in Consumer Internet

Over the past few weeks, I’ve enlisted the help of a few MIT Sloan Master of Finance students to take a look at the consumer internet space and identify high level factors for success. In the next post, I will share some of those findings.  

But to start, here are a few areas that I think are important drivers.  Let’s see how these stack up against their analysis for the next post.

• Is viral built into the product i.e. users are incentivized to share with other users, resulting in non-linear user acquisition. Are there network effects, i.e. single sided, cross platform, both etc – each user that join makes platform more valuable to other users or other side of platform i.e. advertisers, and more users makes it more difficult to leave (higher retention). Social networks are inherently viral.  Daily deal sites like Groupon have designed viral into the product (share with X number of people to activate the offer or receive a further discount).
• Related to above point, certain models built around helping consumers store data like Shutterfly and one of our companies SpringPad, the more you store the more you have to stay on the platform because leaving means you abandon all your data.  Sharing then multiplies this effect as more people become participants and are dependent on this data.
• User engagement – how often user comes back to the site – which then drives monetization, either ad-based (media model), ecommerce, virtual goods, or subscription. Google isn’t viral but it’s a better mouse trap for search, and once you use it and get good results, you use it all the time, and every search query is a proxy for user intent so it’s highly monetizable via targeted advertising.  Another example would be addictive games like Angry Birds monetized through ad views and the mighty eagle in-game upgrade.
• Distribution/buzz, getting access to the right channels, partnerships. This includes factors like celebrity power i.e. Ashton Kutcher putting Twitter on the map.  Influential angel investors also serve as signals to the market. Examples in the Bay Area include Reid Hoffman, Peter Thiel, Ron Conway, Kevin Rose, Ashton Kutcher (who’s also an investor) etc
• Input / output value ratio.  Low friction to participate and get started and low effort for users to get value out of the product. The value can be of various forms - it can be entertainment, discovery, discounts, news, productivity enhancements etc.
• The factors above are interdependent and drive each other. Generating PR buzz with viral loops built into the product extends the effect of the PR. Each new user brings additional users through sharing content, driving higher engagement and monetization opportunities.  If the content shared is valuable and need to be stored/accessible everywhere, this again creates stickiness and increases engagement, which in turn drives more users through sharing etc. High value to input ratio ensures that users actually complete the steps in the product and get to the “share” button at the end, in addition to encouraging repeat usage.
• High value user demographics i.e. female audience, and product categories such as luxury/fashion – recent examples include Gilt, Rue La La, Beyond the rack, Shoedazzle, One King’s Lane etc
• New channel/device.  We’ve seen the movie repeat itself.  New businesses are built (many times adapting past successful models) each time a new channel, device, mode of interaction emerges. Evolution of channels/media: newspaper, radio, TV, web, social media etc. Interaction: text, rich media (video), voice, touch etc.  Device: TV, game consoles, desktops/laptops, mobile, sensor-enabled devices.  This then creates b2b2c opportunities for sectors like advertising, as advertisers need new ways to reach consumers who are now spending time and engaging on different channels and devices.
• The anti-X.  In the social media world of over-sharing and hosted centralized world where individual ownership and controls is reduced, could there be opportunities to attract the fraction of population that don’t want to participate?  Does this create opportunities for security, privacy solutions?

How web/mobile is changing consumer healthcare

After returning from the Partners’ Connected Health Symposium this past week, I have a few thoughts on how web/mobile/social media is changing the way consumers interact with healthcare:

• Social media - patientslikeme already demonstrated that patient-reported data from a user base that is highly engaged is valuable, and that there can be a real business model from data analytics, clinical trials recruitment etc.  What made it work?  First is engagement. Chronic disease patients are more incentivized to learn about and improve their conditions, because they identify with it and therefore are more active in these online support communities.  Second is high patient value, and therefore higher value lead-gen opportunities with pharma and researchers who want to target those patients.  Third is proprietary data and analytics that can be created, such as ability to analyze whether certain medications actually made an impact on treating the disease, adjusting for the natural progression of the disease.
• Mobile / sensors - GreenGoose takes an interesting approach of making wireless sensors very cheap and in the form of stickers, so they can be applied to physical items such as toothbrushes, bicycles, frisbees etc to allow passive tracking of activity data.  Passive is the key, it doesn’t all have to be coming from smart-phone based apps, it just has be easy and low friction for users.  Fitbit is another example, offering a device+subscription model to help users track, analyze and compare their activity levels.  Zeo is doing the same, but for sleep - they call themselves the WeightWatchers of sleep.  Mobile and sensors adds granularity to data which creates more frequent engagement that in turn drives greater value in consumer applications.  There was a talk during the conference by Rosalind Picard who founded Affectiva, an MIT Media lab spinout that uses a device called the Q sensor to measure skin conductance which is an emotional indicator – it turns out that the data collected by the device can be used as a real-time predictor of the onset of seizures.  Separately, Picard’s research group has also developed an algorithm for measuring vital signs of a person such as heartbeat through just an ordinary web cam, bypassing body sensors entirely.
• Making information available to consumers - very simple concept, but still not the norm in healthcare.  Starting with doctor’s appointment times online, which ZocDoc has gotten traction in offering to consumers - the business model there is bringing new patients to doctors and helping doctors optimize scheduling of patients (and thereby optimizing revenue / reducing lost revenue).  How about cost information around health procedures and doctor visits?  Castlight is doing it from the employer-sponsored health plan angle.  160M people in the US are covered via their employers’ health plans, so employers are an important stakeholder as well as a go-to-market channel.  As the world is moving more towards high deductible plans, consumers will be more incentivized to shop around and price compare when it comes to healthcare.  lifeIMAGE is applying cloud data storage (dropbox-style) to medical imaging, allowing patients and physicians to access X-Ray, MRI etc images online - the interface is simple, it looks and feels like email.  Integrating hospital ratings, performance data (i.e. mortality rates), doctor ratings, etc will become standard feature in many consumer facing health apps.

TechStars NYC demo day October 2011 - Trends and Patterns

I went to TechStars demo day in NYC yesterday (Tue 10/18).  From what I saw, I’m increasingly convinced that we are now in the second wave of the new consumer web because we are seeing verticalization, aggregation, and normalization of models that were successful in the first wave.  Examples:

• Verticalization - Contently is a marketplace for brands to hire professional writers.  This is a verticalization of elance / odesk / craigslist.  Similarly, Coursekit which is creating a social network for education, is a verticalization of Facebook.  Ordr.in is a verticalization of ad networks (yahoo, google) and lead-gen models applied to restaurants.  As first wave consumer web services get big, they accustom the consumer to a type of usage behavior and interface which creates market readiness for these types of verticalized models.  The other thread here is the application of successful consumer web models to disrupting industries which have been resistant to change due to the particular industry structure.  In that case, what determines the success of these types of new services will be how they go to market and get adoption, such as circumventing established buying hierarchies (education is a good example).
• Aggregation / normalization - As more and more similar consumer web offerings come online, opportunity is created for aggregation of these services.  One function or value of aggregation is normalization which is a way to perform an action across multiple services, abstracting away the particularities of each service.  Examples of this include Dispatch which integrates across file sharing services that give users a drag-and-drop way to collaborate across these services.  This model can be extended to other actions like scheduling and time, where Spontaneously is focused so that regardless of your calendaring system there should be one place to figure out when to do what with your friends/colleagues.
• The next iteration of X - the next generation of an existing technology or usage model by incorporation of new data types, devices, etc leveraging major trends such as mobile, rich media etc.  One example is Piictu which is basically group chat with images (the next iteration of chat) - users post interesting photos around certain topics and other users can respond with photos.

The demo day show also included a speech by Mayor Bloomberg who was predictably and justifiably optimistic about New York’s startup scene.  An interesting stat he mentioned is that NYC has more undergraduate and graduate students than Boston has people.  Being a Bostonian and a former New Yorker, I was still a bit shocked with that stat so I looked it up. Well it was close: 594K university students in NYC vs 620K people in Boston city proper.  In the greater Boston area there’s about 330K university students (out of a population of about 4.5M).  This makes the ratio of university students to total population for both cities at about 7.3-7.4%.  At the end of the day, NYC just has a lot of people (8 million) at high density which makes it a great test market for consumer internet startups.