NextWala

Compete with CLOSED-LOOP DATA, not software

[Photo from CBS]

A huge thanks goes out to the Jedis of Data: Russ Glass of Bizo; Jason Cavnar of Singly; Gil Elbaz, Eva Ho, Tyler Bell and Vikas Gupta at Factual; Miten Sampat of Quova; and Ivan Mitrovic of Where.com/Paypal; as well as my colleagues David Pakman and Brian Ascher for critiquing and suggesting content for this article. 

This decade will be the decade of Data.  Why? Because software development has become commoditized and super-cheap - the main dimension of competition has shifted to data and the intelligence that comes with it.  Secondary reasons for this are that we've never had so much data collected in the past; and the tools to analyze this ginormous amount of data have now become widely available.

I think large companies will be built around data that is dynamic or difficult-to-aggregate as well as have algorithms that have network effects, feedback loops and machine learning built in.  I call these closed-loop data businesses, adjacent to Big Data.  Adding closed-loop data to your product and business model can help your startup break through the software arms-race and margin pressure of a standard data business.

More on closed-loop data businesses further down in this post. Let's first start with a general overview of data businesses.  

DATA BUSINESSES

Typically, data businesses arise when:

1. companies realize that data is critical to their business but their own efforts at building a usable database will be too costly or time-consuming (e.g. governments/businesses needing to do geospatial planning but not having real detailed geographic maps) OR 
2. multiple competitors need to pool data in a neutral company to fix something fundamentally broken in their business models (e.g. credit bureaus to fix/address the problem of high delinquency rates) OR
3. new methods of data collection (e.g. networks of sensors like GPS devices or smart meters) and processing arise that are 10x faster/cheaper/easier than existing methods OR
4. new pools of disparate data can be exclusively or de-facto exclusively accessed for the first time

Strategically, data businesses have several advantages.  If one can build a network-effect-based data business, then it is hard for competitors to catch up because of all the data that has been processed that has in turn improved proprietary algorithms through machine learning.  Also, at scale, margins tend to be quite high (85%+) as data can be produced once and consumed multiple times with almost zero marginal cost.  This low marginal cost also allows data companies to give away free access to certain slices of data to drive initial customer value, drive paid acquisition and generate leads for paying customers.  And finally, once customers have incorporated this data into their own businesses, switching costs are usually high because of semi-proprietary formats (sometimes evolving into de-facto industry standards such as FICO scores)

There are challenges, though.  It usually takes a long time (sometimes years) to build out a big enough and accurate enough database - there is a problem of acquiring enough data to build out tuned algorithms and an accurate value-added database.  Ingested data changes (sometimes quite dynamically) and the value-added database needs to get updated quite often.  In the early days, labor costs are usually high in manually normalizing, processing and cleaning this data.  And sometimes data collection is not possible from third-parties and has to be done manually (Navteq famously spends upwards of $400 million per year on its van network to collect and process map data).  Because third-party data sources are many times paid sources, companies have to figure out how to scale their revenue without scaling data costs in line with revenue.  And finally, data businesses are usually further down the stack and therefore at the mercy of competitive or governmental forces threatening their customers. 

Some more recent challenges that have emerged: First, data businesses with direct revenue from their customers are now being threatened by data businesses that monetize through reach-driven advertising, not direct revenue.  Second, think carefully before trying to exactly replicate a US or Europe data business in an emerging market - in many cases, the sophistication of the specific vertical eco-system may be in earlier stages and markets may not have evolved enough to understand the value of nor respect data.  And finally, many consumer businesses have swapped or augmented advertising with data sales as a business model - having user data doesn't mean someone will pay a lot for this data nor that the data is at scale or is differentiated. (thanks, Brian).

Here are some examples of data businesses:

• financial Services (Equifax, CIBIL India, CCIS China, FICO, Bloomberg, Venrock-backed BillFloat)
• retail (Catalina Marketing, Venrock-backed RSi)
• location (Skyhook, Navteq, Factual, OpenStreetMap, MapMyIndia, Venrock-backed INRIX)
• search/advertising (Google search, Dataxu, Rapleaf, BlueKai, Venrock-based Bizo and Media6Degrees)
• healthcare (Venrock-backed Castlight and Kyru.us) 
• travel (ITA Software, Galileo, Sabre)
• social (LinkedIn, Bit.ly, Klout, Singly, Gnip)

Let's turn now to a special subset of data businesses:

CLOSED-LOOP DATA BUSINESSES

These businesses:

• collect data from third-party sources (and potentially their own proprietary application/sources)
• process that data with their own 'special sauce' i.e. proprietary rules and algorithms to produce value-added data
• (In some cases,) monetize by directly selling data to third parties. (In some other cases,) monetize by using the data to improve core product (like Google and LinkedIn do) or provide other value-added services such as an ad network or an application.
• receive data back from consumers of their data to aid in machine learning and data quality verification. 

Here's a rough diagram of the data flow in a closed-loop data business:

Feedback loops are the crux of the issue here: depending on the vertical you're in, you can spot these in the form of click-through rates, ground-truth testing, fraud rates, acceptance rates, user complaint rates, redemption rates, multi-source cross-checking etc.

In starting a data business, think about whether you can turn it into a closed-loop data business.  Here are some questions to ask yourself in doing so:

Data sourcing and normalization:

• Can you source some of your data from businesses that collect this data as a byproduct of their business?
• Can you source data from free and easily-accessible databases e.g. Tiger maps, info.gov, etc.
• Can you get exclusive access to some of this data?
• Can your find or buy orphan data businesses from other companies in your eco-system?  As a corollary, can you get other players in the eco-system to outsource their data function to you.
• Can you barter with data suppliers so you reduce your COGS and increase gross margins?
• Can you involve the community to explicitly or implicitly input more data and verify/correct the data?

Data processing & machine learning:

• Can you introduce feedback loops from consumers of your data? Can it be made automatic, rather than manual?  Can your algorithms learn from this feedback loop?
• How will you verify data quality and prove this to customers?  Different customer segments may require different levels of quality.
• Do you have data scientists (and perhaps algorithms) to model and produce value-added data?  Are you giving your data scientists the mandate to come up with new products? They are the only ones that can discover new products in data that consumer product managers typically don't see and can't plan for. (thanks, Ivan)
• Be ready to innovate on data pipeline and data storage technologies. Off-the-shelf "Big Data" technologies may not cut it. Example: LinkedIn's Voldemort project. (thanks, Ivan)
• What scale do you need to reach to have minimum viable database size?

Pricing:

• Does your pricing model reflect the business model already accepted by buyers in the industry? It should.
• Can you give away any lower value slice of the data to drive adoption and lower customer acquisition costs/barriers? (thanks, Russ)
• Are you pricing below other higher-cost data providers in your sector? You may or may not need to engage in price-based competition.
• Is the value in the data itself or in the application of the data?  Should you keep the data and build out the application instead to capture full value? (thanks, Vikas)

Packaging and Standards:

• Your value-added data is not one big monolithic product you sell to customers. Think about how you would slice this up to provide different levels of value to different customers (e.g. historical slices, real-time feeds, segmented data, different data delivery types).  
• Can you provide applications or value-added algorithms for specific segments of customers?
• Flexible APIs and tools will drive faster adoption e.g. provide sample code in all popular languages, write plug-ins for popular CMS programs, make the data available in popular formats etc.  Don't just provide a read API. Think about mandating a write in exchange for certain types of data that your customers read from you.
• Are you adopting industry standards for how your data is stored and transferred? In other words, are you on the side of open or on the side of proprietary? (thanks, Gil).
• Invest early on in visualization capabilities - your customers will need to see and manipulate the data before they understand the true value it may bring them. Your data scientists and data visualization should be able to produce not just charts but customer insights. (thanks, Miten).

[Data visualization from LinkedIn blog post on InMaps]

Legal:

• How will you protect your data from being copied?  Dynamic, fast-changing data is hard to copy; static/slowly-changing data is easier to copy or commoditize.  Map companies insert phantom geographic features/streets into their maps to track copying by other map database companies.
• Construct the terms of service for easy adoption and maximum feedback.  Also take into account residual rights on data produced by your customer's algorithms that has your data as an input.  OpenStreetMap was in limbo for a while with the change to an Open Database License (ODbL) from a CC-BY-SA 2.0 license. (thanks, Miten)
• Be especially careful on storing Personally Identifiable Information (PII) - preferably, don't do this. Also take into account different legal definitions and standards-of-use for PII in different countries.  Consult a lawyer on this one.

TO FINISH UP...

Congratulations to those of you that have made it through this dense prose!  You'll need some of this same fortitude to build your own data business!  

As you think about your new startup idea or how to put extra legs on your existing venture, really think about whether you can introduce closed-loop data into your product and business model.  In my opinion, pure data processing businesses have a limited shelf life and ever-decreasing margins whereas closed-loop data businesses have increased odds of breaking through.

Come talk to me if you have a world-changing idea in this area and need a partner-in-crime!  And go hire a mad data scientist!

[Photo from Cayusa]

Techcrunch Disrupt: (Only) a Few Big Ideas

Perhaps I'm getting jaded, but I somewhat agreed with Vinod Khosla's comment yesterday at Techcrunch Disrupt where he said there was too much linear thinking going on with startups and something like announcing a UI change in Microsoft's IE did not belong at the conference.  

Some of the low-points of the conference:

• Several "check-in" companies checking into products, places, people, websites and who knows what's next - checking into shoes, hairstyles, weather?
• Several companies making it easier (somewhat) to manipulate, filter, stream real-time content
• Several companies enabling gamification of everything

Perhaps this is just natural evolution taking its course and perhaps this will lead us to some really compelling user experiences and value propositions down the line.  Perhaps...

However, there were only a few ideas that got me thinking "Holy s****", this is a big idea and could change some part of the world fundamentally.  So here are my "meta-idea" picks from the conference, all of which fundamentally deal with data:

• The big idea - leverage the collective brain-trust of an enterprise. The implementation: Opzi - "Quora for the enterprise" - supercharge enterprise productivity by aggregating the collective knowledge of an enterprise and making it easily accessible and addressable down to each employee
• The big idea - users own and wield their own online data.  The implementation: Shwowp - "Yodlee for the people, by the people" - enable users to aggregate their own shopping history/date and then selectively share this with retailers to drive better deals.
• The big idea - take mobile analytics beyond web analytics.  The implementation: Apsalar - "Kissmetrics for mobile" - in-depth analytics to survey, track and optimize users as they flow through and across your mobile apps.
• The big idea - present real-time information at the "right-time". The implementation: Datasift - tuning and filtering Twitter streams to your liking. I would have liked to see a use-case (e.g. commerce, research, brand monitoring) other than more personalized consumption.

And here are shoutouts to two Venrock-backed companies, Redbeacon and CloudFlare, that presented at the conference.  We are excited about the prospects for both these companies and look forward to continuing to work closely with them!

The first is Redbeacon which won Techcrunch50 last year.  Redbeacon's vision is to do to local service providers what Expedia/Travelocity/Orbitz did to airlines i.e. make it super-easy to search, compare and book local services (plumbers, electricians, gardeners, trainers etc.).  Redbeacon announced it was expanding its service to Seattle, launching with several media partners (NBC Bay Area, SF Gate, Tribune/Fox Seattle), and launching its widget for inclusion on any website.  We believe Redbeacon has the potential to disrupt the $150B local advertising and lead gen market. Last year's Techcrunch50 video here:

The second is CloudFlare which won an Innovation Award at this conference. CloudFlare's vision is to serve smaller websites and provide them the performance and security infrastructure that only the largest Internet giants can afford.  Value prop: speed up your website by 30% and protect your website's visitors from spam/malware attacks.  CEO Matthew Prince showed how CloudFlare is signing up 2 customers (websites) per second today.  TC Disrupt video here:

What About.me's launch says about the rise of the Professional Web

Tony Conrad's announcement this past week of About.me got me thinking that it's about time we gathered up all these threads of the Professional Web, as I call it, and interlaced them together into a metaphorical rope.

Before I continue, let me define what I call the Professional Web.  As workers and professionals, many of us follow and contribute to the social web, interacting with it several times a day, if not every two minutes :-).  We leave implicit traces of our presence behind (e.g. Twitter streams, Quora answers, LinkedIn group postings) as well as explicitly build profiles across all these websites. I call this the Professional Web. The sum total of all this personal content is our personal brand on the Web.  

So why do I think it is important to proactively manage your presence on the Professional Web? Firstly, this is what other people see (probably through a Google or LinkedIn search) well before they meet you for the first time, as well as on an ongoing basis once they've gotten to know you.  First impressions are important, as we all know. Secondly, many of us in the knowledge economy make our living based on our reputations either directly from the Web (e.g. through Crowdflower or Turnhere) or indirectly influenced by the Web.  This applies to entrepreneurs, investors, proprietors, corporate employees, consultants or service providers.  So this directly hits our personal bottomlines.

So, I've listed below seven of my favorite Professional Web services and do's/don'ts for each of them. In a follow-up post, I will write about some new opportunities that I see in this area.

Seven Tools to Super-Charge your Professional Web Presence

Here're the slides - there is more context and discussion below the slides:

Seven tools to brand yourself on the professional web

View more presentations from Dev Khare.

I've been an active user of LinkedIn for several years and use it as my master work contact list. I hit 3000 connections recently.  I find this to be an invaluable tool for finding current email addresses, contacting CEOs of companies I'm interested in, doing competitive analysis, finding common contacts when meeting someone for the first time, and much more. I'm even paying LinkedIn because I'd feel guilty if I didn't pay them for all the value I'm getting out of the website!

• Do: Integrate Twitter and SlideShare into your LinkedIn profile.
• Do: Use LinkedIn to find and contact people who have just left the company you're interested in.
• Do: Create a group for the area you are most interested in - use this to post relevant content and drive deeper conversations with industry contacts.
• Don't: Accept connections with people you've never met.

I've been using Twitter for the past couple of years.  Invaluable for tracking experts in various fields as well as some vertically-focused news sites.  I follow a total of about 950 people, including 125 people in my main feed and the rest spread over 20 lists. 

• Do: Create lists where you track people in different segments that you're interested in. I track segments such as Payments, Local/SMB, Enterprise, Mobile and India-Tech-and-VC. The added benefit here is that you can offload people from your main Twitter feed.
• Do: Time-shift your posts (using a tool such as Hootsuite) so that they get posted during regular business hours, preferably early afternoon.  This will ensure the biggest audience.
• Do: Track conversations at conferences using their # hashtags to get an idea of the back-chatter.
• Don't: Connect with others just because you want to increase your follower count.

I've been using SlideShare since early 2008 (full disclosure: we are investors in SlideShare).  I find it really useful for tracking content from leading thinkers and opinion-drivers, including Dave McClure, Eric Ries, Jan Chipchase, Fred Wilson, Charlene Li, Steve Blank and Chris Messina.  I follow about 150 people.

• Do: Follow 50+ people by starting on the Slideshare profiles of perhaps some of the people above and clicking through to who they follow.
• Do: Follow your real-time newsfeed on Slideshare, once you've started following 50+ people. Incredibly rich content from your favorite experts. Very different from Twitter streams.
• Do: Upload a few pieces of your own content - drives great SEO back to you.
• Do: Embed a Slideshare widget on your blog - deep content gives your blog audience insight into what you are interested in.

  
Plancast is a great way to communicate future plans with your audience.  I use it to keep track of all the conferences I attend as well as see what public (or private) events other people are going to. I follow about 80 people.

• Do: Put up all your professional/public engagements so that other people know where to connect with you
• Do: Use the Google Calendar or Outlook integration feature to suck these events into your personal calendar.
• Don't: Give out addresses for private events!

Of course, blogging is a proven way of putting your opinions into the conversation stream in a much deeper way than Twitter allows. It's very very easy to get caught up posting to Twitter and to ignore your own blog.  However, I think you create social currency (earned media) by contributing deeper thought pieces to the conversation.  I use Typepad.

• Do: Reserve your own URL, rather than relying on a Typepad or Wordpress extension. Benefit is that you can move to other blogging platforms in the future and can drive more personal branding through the URL you choose.
• Do: Put widgets from your other Professional Web services here, such as your Twitter feed or favorite slideshows from Slideshare
• Don't: Leave your blog unattended for long periods of time (i.e. several weeks or months). I'm guilty of this myself, though!

I've started using Quora quite recently. I think it could be a great method for engaging on very specific topics with the wider opinion-maker community as well as with entrepreneurs.  I don't have a list of do's and don'ts here as I am very recent user. But I see a lot of promise here.

I also see promise in services such as About.me or Flavors.me which enable users to create a personal profile page on the web, outside of the confines of any specific professional networking service. We had an investment in this area called Nombray and I find the value proposition to be quite appealing. The value (to me) would be to provide a single-page to drive your professional contacts to (e.g. from your business card or email signature profile); a single place for analytics across all your professional profiles/websites; and a key way to drive up your ranking on Google searches of your name.

You may have noticed I haven't included Facebook in this list.  Personally-speaking, I try to separate out my business and personal/social contacts. It's a challenge for me but I feel there is some value in keeping them separate. For many people, the lines have blurred - and in that case, it's important to keep appearances up on Facebook.

So tell me one unconventional way that you use the Professional Web for your own benefit.

Stay tuned for a follow-up post on the challenges facing companies in this space and new opportunities that I see.

10 Characteristics of Successful Distribution Networks

(Photo by Nick Saltmarsh)

Over the past few months, I've looked at many startups that are focused on distribution of content in the digital media & mobile space.  "Content", in this context, could be videos, music, ads, games, payments, virtual goods, powerpoints, incentivized offers, coupons, airtime, whatever.  Surprisingly, very few Silicon Valley-style companies are actually producing original content (many investors actually shy away from original content production businesses, leaving that up to Hollywood or Madison Avenue). 

Classic examples of businesses with distribution characteristics include YouTube, Amazon, Flickr, iPhone's App Store, Google's Android Market, Motricity etc.  A number of such ventures that have captured our collective imaginations in the past few months include: Groupon, Foursquare, Gilt Groupe, CloudMade and Blippy.  Many platforms have turned into quasi-distribution businesses, including Twitter, Facebook and SalesForce.com.  In our own portfolio here at Venrock, we have several ventures with distribution characteristics, including INRIX, SlideShare, Aha Mobile, Media6Degrees, Adify and others.

Sometimes, these businesses are camouflaged in clever ways, such as having a branded destination site or native mobile application (in addition to B2B or B2B2C distribution).   Sometimes,these businesses don't have a consumer brand at all and are selling data.

You may say "So what?"  Well, the 'so what' is that certain economics principles apply to distribution networks and the sooner you figure what principles apply to your business, the sooner you can take these principles into account to build your business to your advantage.  And based on the principles outlined below, you may be able to find some additional ways of building value in your startup.

Apart from general startup success factors (such as team, product-market fit, user experience etc.), here are some of the things I look for in distribution ventures:

1. Increasing marginal return to increasing transaction volume:  You have to find positive network effects, high switching costs, effective machine-learning algorithms or other blocking IP to convince yourself that your distribution network has a chance to become a highly valuable quasi-monopoly.  In the absence of this, even if there is a diffuse set of suppliers and buyers, there may end up being a highly fragmented set of distribution networks (a la the current state of the ad network market) – this is not necessarily bad if the market is very very large.  With some of these effects in play, you get the likes of Google.

2. Clear value-add of your distribution network:  Related to the point above, be clear about the exact value being added by your distribution network.  Some examples that I find repeated across distribution networks are:

• Data step-up: You could collect data from multiple sources or generate data from user activity on your own destination site. You could then algorithmically generate value-added data to enhance your own service or sell to other players in the value-chain.
• One-stop shop / scale: You could be the largest or most focused provider of content/data in a certain category. Examples would be Motricity in the mobile apps space or Yodlee/CashEdge in the consumer transaction data space.
• Value-added ancillary services: You could provide additional services, such as payments, discovery, search, APIs etc. to really lower the barriers to buyers to source from you.

3. A business model that fits your industry:  Your distribution network has to adopt the business model that the suppliers and buyers are already using. Trying to invent a new business model is (most times) a no-go.  Time and again, I see startups that have a business model orthogonally different from the market they are going after when they could really get faster to product-market fit by adopting existing pricing models.

4. Non-commodity content:   Like in any market, an oversupply depresses prices.  This is what happened in the casual games market where there was essentially infinite supply of games.  However, low (or free) prices are not necessarily a bad thing – low/free prices could help you get to greater user adoption and could then be monetized through lead generation and advertising, presuming that this content has other substantive ways of making money.  If you can get exclusive access to all (or some of this) content or be the only platform on which this content can monetize effectively, you have built a defensive moat around your business.

5. Diffuse suppliers:  In general, a high concentration on the supplier side of your network is not conducive to the your success.  If you are distributing, say, mobile applications or social games or banner ads or videos or music, if a small set of suppliers (say 5 or less) own 80% of the market, then these suppliers will tend to go direct to the buyers or buyer aggregators. If you can source relevant or hard-to-get data or content exclusively, that can of course play to your advantage.

6. Diffuse buyers:   A high concentration on the buyer side of your network is also not conducive to your success.  In this scenario, buyers will tend to go direct to the suppliers or supplier aggregators, bypassing you.

7. Aggregators that you could co-opt:  If there are a set of supplier or buyer aggregators on either side of your network, then that reduces the margins for your distribution network.  In some cases, these aggregators can be competitors to you or could be distributors or supplies.  An example would be yellow pages companies which concentrate the buying power of local merchants.

8. Understanding of platform build-out cost:  In some large-scale distribution ventures, money is made pennies at a time. Visa or Mastercard are examples.  To get to, say $100 million in revenue, such distribution networks have to do billions of transactions a year.  Platforms are hard to scale to this volume and almost always require a pretty large amount of capital.  Understand how much it will take you to build out this platform.  Key features should include supplier ingestion tools (ideally self-service), buyer ingestion tools, metering/billing, social lubrication etc.

9. First caveat: Early-mover advantage:  Notwithstanding everything mentioned above, there may still be an early-mover advantage if you can get to scale fast enough (e.g. Admob), where you get big enough to be taken out by a larger incumbent in an adjacent space as a line extension.  Sometimes the large incumbents don’t move fast enough and pay a premium for size/reach/traction.   Of course, this is conventional wisdom but doesn't necessarily always apply.

10. Second caveat: Disruptive technology or business model:  In some cases, either the buyers or the suppliers are currently garnering most of the market and the market seems locked up.  However, you may have a new technology or business model that opens up the market to many more buyers or suppliers, disrupting the cosy oligopoly on the supplier or buyer side. 

Let me know what you think of this.  I'm sure I've left out some important gotchas.