New Year’s Resolution

I’m not big on New Year’s resolutions–if you want to do something, just do it, why wait for that magic date–but here timing played a role…

My resolution is to use email better, both in terms of how I write/send emails and in terms of how I use my email system, which is Microsoft Outlook/Exchange.

It all started with me having to do a complete rebuild of my machine in late summer due to bit rot. That’s when I switched to Kaspersky anti-virus for better performance. Little did I know that Kaspersky had a bug (which I unluckily discovered) where some Outlook messages moved between folders have their received date changed to the current time. Many messages over a period of several months had their received dates quietly changed. I finally found out what was happening during a routine batch macro job which changed many thousands of messages this way, making the task of locating an email from that period almost impossible.

Let me back up. I spend much of my time in Outlook managing many separate threads of conversation that cut across multiple portfolio companies as well as many startups I’m engaged with. Folders simply don’t work (for me) for M-to-N relationships so I use categories (what Gmail calls labels) combined with search. I’ve extended Outlook with custom code to provide behavior similar to some of what Gmail can do with labels, except that Outlook isn’t designed for this and so there is the need to work around limitations in mailbox size as well as how many items can be in a folder, etc.

I spent many hours on the phone with Microsoft tech support right before Christmas to track down the root cause of my problem to Kaspersky and then use utilities custom-built by the MS support team (a very helpful bunch of guys) to get me back on track (we batch set the received time to the sent time as a close-enough approximation).

The entire ordeal has prompted me to rethink how I work with email. If you have efficient systems for handling lots and lots of email where messages belong to one or more logical groups, let me know.

Update: there are some good pointers in the comments worth reading.

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The Social Impact of HiTech

My friend Dominic Gallello, who used to be an EVP at Macromedia and is now running Graphisoft (part of Nemetschek) in Budapest, sent me this YouTube video about the work he and his wife are doing with orphanages in Romania. Impressive and a good example of the social impact hitech wealth can have, especially in less-advantaged countries.

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OpenSocial: The Lowest Common Denominator?

Google’s OpenSocial is becoming the lowest common denominator for application integration in the social media space. That’s actually not that great for Google as it is rarely the case that the most successful applications run on the lowest common denominator platform. Best implies leadership and innovation. You can’t get that just by being one of the gang. Some observations:

  • Facebook is the place to be in social apps right now and, as the leader in the category, they are not planning on supporting OpenSocial anytime soon.
  • On Monday, LinkedIn announced they are opening up and adding they own “set of REST APIs and widgets” and “have announced support for Google’s Open Social platform and will include other ways in the future as well”.
  • Friendster is also opening up and has said that “OpenSocial APIs will be integrated into the Friendster Developer Platform when the much-stalled OpenSocial is completed and secure.”
  • Not to be outdone, Bebo has launched an open application platform to complement its open media platform. Rather than joining up with OpenSocial, Bebo essentially cloned the Facebook APIs, e.g., they have SNQL (Social Network Query Language) for Facebook’s FQL and SNML (Social Network Markup Language) for Facebook’s FBML. 

In other words, OpenSocial is great but if you really want to work with us, use our native APIs. This situation complicates life for social application developers as the cost of experimentation and porting remains relatively high. Most application developers will choose to launch initially on Facebook to “test the market” and, if they see success, try others (Bebo because the port will be easy or OpenSocial, if its capabilities would support the application).

It would be interesting to see how much farther beyond OpenSocial MySpace goes. They may set the bar for OpenSocial. Since MySpace is not at heart a technology company, perhaps they will partner with Google to jointly figure this out.

Posted in Digital Media, Facebook, Google, MySpace, social media, startups, Web 2.0 | Tagged , , , , , , , , , , | 1 Comment

Ooyala wins

Ooyala won the Amazon Web Service Startup Challenge last night. The company has a video hosting platform integrated with an ad network and some interesting bells and whistles built around the notion of using computer vision to provide better ad targeting, interactive entertainment and commerce. The founding team  comes from AdSense.

The symbol of the prize was a gold-painted hammer signed by Jeff Bezos. Under the lenses of the eager photographers and videographers, the team banged the hammer on a 2U rack server to symbolize the end of servers deployed in the enterprise.

Posted in amazon web services, startups | Tagged , | 3 Comments

Amazon Web Services Startup Challenge

I’m heading to Seattle tomorrow for the AWS Startup Challenge finals. Many entered the competition with the hope of winning fame & glory, $50K in cash, $50K in AWS credit and an investment offer from Amazon (the details of which I’m fuzzy on).

The finalists are:

  • Brainscape
  • Commerce360
  • Justin.tv
  • Milemeter
  • Ooyala
  • UserTesting.com
  • WeoGeo

If you’ve any experience with the finalists, let me know. I’m one of the judges and Polaris is sponsoring the event. There is a cool dinner, which will bring together entrepreneurs and execs from the area. Unfortunately, it’s $100/seat. More importantly, Polaris is throwing together an after-dinner party. No, you won’t need to pay for that. If you can’t make dinner but want to come to the party, let me know.

Hope to see you in Seattle.

Posted in amazon web services, SaaS, startups, Web 2.0 | Tagged , , , , | 1 Comment

Bubble 2.0 as Art

Courtesy of my friend Paul Moorhead at Meridio/Autonomy.

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The Science of Viral Marketing

While it is fun to throw about theories about what works and what doesn’t in viral marketing, the fact is that there is precious little public analysis about online behavior in the face of social economic incentives.

The most detailed analysis I’ve been able to find is “The Dynamics of Viral Marketing“, which looked at real data from an e-commerce site that provided a simple incentive–recommend what you’ve bought and you + the first person who buys off your recommendation get 10% off.

We present an analysis of a person-to-person recommendation network, consisting of 4 million people who made 16 million recommendations on half a
million products. We observe the propagation of recommendations and the cascade sizes, which we explain by a simple stochastic model. We analyze how user behavior varies within user communities defined by a recommendation network. Product purchases follow a ’long tail’ where a significant share of purchases belongs to rarely sold items. We establish how the recommendation network grows over time and how effective it is from the viewpoint of the sender and receiver of the recommendations. While on average recommendations are not very effective at inducing purchases and do not spread very far, we present a
model that successfully identifies communities, product and pricing categories for which viral marketing seems to be very effective.

The great thing about the analysis is that the data set extends to three major categories (books, CDs, DVDs and videos) with lots of buyers over two years. The researchers draw some interesting conclusions, including the observation that “purchases that resulted from recommendations are just a drop in the bucket of sales that occur through the website”.

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Reading the analysis it becomes clear that the etailer did a poor job of setting up a successful social incentive system and of providing the tools to let consumers take advantage of it. For example:

  • By not imposing limits on the number of recommendations that could be sent out they allowed some people to become spammers, which negatively affected conversion for the recipients, even as far as other senders were concerned. No surprise here–by analogy, email spam has reduced the effectiveness of email as a marketing medium for everyone.
  • By having a one-size-fits-all incentive system, the etailer did not take advantage of the fact that the dynamics of viral distribution vary by product category and price range. Imagine picking a simple marketing strategy and putting it in place for two years without change and without any A/B testing. No, that’s not how things should be done.
  • The etailer provided no tools to help viral spreading beyond the ability to email people with a recommendation. No profiles, recommendation lists, interest groups, checkout notifications, recommendation-based cross-selling, etc.

To make effective use millions of distribution channels, e-commerce sites need to think systematically about social commerce. They need a strategy first. Then they need to realize that implementing a successful social commerce system will require investment not unlike that required to build any major part of their site.

Posted in Social Commerce | Tagged | 6 Comments

Millions of Consumers, Millions of Channels: The Shift to Social Advertising and Social Commerce

I did a guest post at InternetEvolution titled “Viral Distribution’s Coming of Age“. The key observation is that the consumer has become the distribution channel. Millions of consumers = millions of channels. Successfully leveraging social advertising and social commerce will require a shift in how brands think about distribution.

Posted in Social Advertising, Social Commerce, Web 2.0 | Tagged , , , | 4 Comments

Shifting The Cost/Benefit Conversation

Had a great dinner last night in the Valley with a group of founder/CEOs. At one point in the evening the conversation shifted to a topic that’s near and dear to my heart as I continually find myself challenging entrepreneurs to rethink their sales models and look for ways to reach initial scale with less cost and risk. Around the table there was general agreement that one of the simple strategies to consider is shifting the way a business engages its customers.

The typical sales pitch is a cost/benefit conversation that focuses on the benefit. Millions of sales people have been trained to talk benefits and make the customer realize the necessity of the costs. This is the only way to go when the product or service being sold has either a high up-front cost (money and/or time) or has non-trivial ongoing costs. Examples would be a car or a ERP deployment or a Salesforce.com subscription.

The biggest problem with this sales model is that it is slow. It requires research and, more often than not for more expensive products, lots of conversations. It is also susceptible to interference, particularly in the case where the discussion in category has already been framed by a competitor. Both of these are bad news for startups that need to suffer the core burn + grow a sales team to see whether the dogs will eat the dog food.

With the advent of ad-supported business models online and SaaS in the enterprise, a number of businesses have taken a different approach. Rather than asking a customer “how much are you willing to pay for these benefits?” the conversation has shifted to “if the cost is $0, how much benefit must we deliver for you to adopt?” Can we call this a cost-oriented selling approach? I guess so.

Flipping the value proposition from pay now, benefit later to don’t pay but still get some benefit is a no-brainer for consumer plays but it also can work miracles in the enterprise as demonstrated by the scalability of, say, LogMeIn‘s freemium model. (LMI, a Polaris portfolio company, offers consumers remote access to their PCs for free. They make money through upgrades for more functionality and enterprise deals.) It is a particularly good approach for entering areas with established competition where a benefit-oriented as opposed to a cost-oriented sales approach will bog down selling.

The challenge under this model is to find a monetization strategy. Online, for ad-supported businesses, the first step in the rule book is plugging in an ad network. The next step typically involves hiring an ad sales team–a difficult, expensive and slow undertaking, which is why many startups choose to sell to big players with good ad sales teams at this point. In the enterprise it typically has to do with value-added services such as manageability, SLAs; pulling through additional products; and building data businesses that leverage network effects in the deployment footprint (most commonly seen in security with anti-virus/spam/phishing).

Posted in SaaS, startups, Web 2.0 | Tagged , , | 2 Comments

Walt Mossberg and Verizon’s Move to Open Its Network

I’m listening to Walt Mossberg speak at the Dow Jones Consumer Innovations conference. He began by pointing out that Verizon didn’t give him any credit when they announced the opening of the Verizon network. This is a huge deal, by analogy equivalent to the fall of communism. Hey, maybe Walt can change the world. Here is Om’s analysis and a pat on the back from the FCC. I’m with Om. I bet there will still be lots of hurdles to jump over and “Verizon-open” won’t mean “Internet-open” but the chance to stance is still very significant.

BTW, it will be difficult for Apple to maintain its semi-closed iPhone/iTunes environment with GOOG’s Android positioning + Verizon moving towards openness.

Posted in Apple, Google, Mobile | Tagged , , , , | 2 Comments