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Successful media is entertaining. It has to be to get you to keep coming back day after day, many times a day.

Getting this right requires a mix of serendipity and high value content. Not enough high value content and I feel like I’m wasting my time, and I leave. Techcrunch had a post this weekend speculating that Twitter quitters are leaving because of a noisy feed. On the other hand, not enough serendipity and a service starts to feel stale, and I leave. I may as well be using an RSS reader. Useful, but not fun.

This is the core problem of discovery, balancing novelty with content that is interesting to me. It is a signal vs noise problem. I’m willing to see some things that are not interesting to me (noise) so long as there is enough interesting material to make it worth while (signal), and the “cost” of the noise is not too high.

Users always underestimate their desire for serendipity. They think they know what they want to see, but they don’t. Serendipity, in the form of breaking news, viral posts, “most popular” content, and randomness of all kinds always garners attention even though users will tell you that they don’t want to see it.

Most social media sites solve this problem with the feed. A “friend” or “follow” dynamic puts some initial filter onto the content of the feed. But as the site grows, the volume of content that can go into the feed quickly overwhelms what a user can consume. It has to put some filters on what makes it into the feed. This gets to a personalization problem. How does a site determine what may be interesting to me? And how coarse a filter does it apply? The tighter the filter (executed well), the more likely I am to get items that interest me, but the less room there is for serendipity.

By viewing this as a signal vs noise problem, you can see how different sites solve this problem differently. If you have a site where the cost of “noise” is high, you will want to keep a tight filter and make sure that most of the things in the feed are interesting. But if you have a site where the cost of “noise” is low, you can afford to keep a coarse filter and allow for more serendipity.

Longform video is the most expensive form of noise. When you watch a new TV show, it can take one or more full episodes before you know if you enjoy it. That can be an hour or two of time. So long form video tries to keep a tight filter and guide you towards shows it has high confidence you will like. This can take the form of suggesting different episodes of a show that you have watched a lot, or simply the most popular shows. But it never shows too many at a time.

Short form video is less expensive noise, but still noise. It can take a few minutes of watching a youtube video before you know if you like it or not; the average Youtube video is watched for four minutes. And once again, Youtube keeps a relatively tight filter, relying on subscribed channels and related videos for its recommendations

Long form text is the next most expensive noise. Examples would be most news, blogs, magazine and media sites. Read an article and you’re in at least 30 seconds before you know whether you’re enjoying it or not. Most such sites have 10s on links on their home page, the equivalent of their feed, and the editors spend a lot of time optimizing to determine which articles make it to the home page.

Short form text is pretty cheap noise. In the second or two that it takes to scan a tweet or Facebook status post to see if it is of interest to you, you’re already on to the next item in the feed. So it is no surprise that these are infinite scroll feeds within a single column.

Finally, if a picture is worth a thousand words, then it makes sense that picture based media is the cheapest noise of all. In a fraction of a second you can glance at a picture and know if it captures your interest. So it can have the coarsest filter of all. And that is why you see an endless scroll of photos in multiple columns in Pinterest, Wanelo, Whisper, Poshmark and so on.

The lower the “cost” of putting something potentially uninteresting into your “feed”, the more room there is for serendipity in content discovery. It allows the personalization filter to be coarser. That’s feature, not bug.

 

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Joe Lonsdale (co-founder of Palantir, Addepar and Formation8)  kicked off a meaningful conversation with us on building  multi-billion dollar businesses in big data at one of our recent Lightspeed Dinner Series events this week.

Lightspeed Dinner Series on Big Data with Joe Lonsdale from Formation 8

Lightspeed Dinner Series on Big Data with Joe Lonsdale from Formation 8

 

Drawing on examples from Palantir, Joe talked about his views on 1. Smart Enterprise, 2. Platform Effects and 3. Network Effects as related to big data.

 

1. Smart Enterprise is about data being at the center of your business. This is part of a series of shifts showing the value of data increasing over time in the context of the enterprise.

We share his view that the value and complexity of data is growing exponentially and most industries today all ill-equipped to handle this new reality and the rate and scale of enterprise data growth. Specifically, you need to get the data to the end-points in the organization as autonomously as possible so every person can make “smart” decisions in context. Organizations that do this effectively will gain advantages over ones that rely on a traditional workflows to manage information and productivity.

Joe elaborates on Smart Enterprise as an investment thesis for his new fund, Formation 8 (http://formation8.com/resources/the-smart-enterprise-wave/).

 

2. Building a Platform for solving complex problems gets exponentially harder. When the problem space is complex you arrive at solutions fastest through a mix of software and people engineering.  Algorithms alone don’t cut it.   Joe believes this is one of Palantir’s unique advantages and something they executed on well.

Palantir has been able to repeatedly pair  it’s core analytics platform with human investigators who  unlock business value across a variety of vertical domains. Their software and the residual know-how in the teams combine to form a powerful solution platform. The challenge ahead lies in translating this technology + human solution into a product that can be distributed through established sales channels for maximum scale. See Peter’s post related to this concept of repeatability  (http://blogs.wsj.com/accelerators/2013/10/01/peter-nieh-focus-on-repeatability). 

 

3. Network effects of knowledge are strong in the enterprise. Network effects, especially social, are a key strategy in the consumer space and at first glance, they don’t appear to apply to the world of Palantir.

However, on closer examination Joe demonstrates some great examples of how collaboration network effects can be even stronger when it comes to knowledge in the enterprise. For example, Government intelligence departments function as separate teams.  But when one team achieved an analysis breakthrough with Palantir, demand for their solution from the  adjacent intelligence groups followed very quickly. Often it was not just teams within the same agency but across multiple agencies and even across multiple allied governments.

When we tie  this back to the emergence of the Smart Enterprise it suggests that big data platforms and products a likely to have strong “knowledge network” effects that accrue to the market leading  players. As information and data insight become increasingly central to business effectiveness and competitive advantage it’s increasingly about ‘what you know’ (and how fast you know it).

Attendees at the Lightspeed Big Data dinner with Joe Lonsdale

Attendees at the Lightspeed Big Data dinner with Joe Lonsdale

 

We received a lot of great feedback on the dinner (thank you to all the attendees!).  Its clear  we need to have more of these types of conversation and we plan to do so frequently.  The guests for dinner included public and startup company executives, founders, investors and thought leaders in big data.

At Lightspeed we enjoy this type of discourse and appreciated the opportunity to hear  Joe’s views.  We  look forward seeing how these ideas play out in the future!

Thanks again,  Ravi Mhatre, Lightspeed Venture Partners

 

If you’re interested in learning more follow Ravi Mhatre at @rmtacct,   Lightspeed Ventures at @lightspeedvp and Lyon Wong at @lyonwong

 

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In the early days of a startup, every business component, from what to pay employees to new product features seems critical.  But what are the most important measures of success for a startup?

This week, I shared an article on the topic with the Wall Street Journal.  In my experience, I’ve have found that the most important aspect for success is relentless focus on establishing a repeatable customer acquisition process. This provides the essential foundation on top of which the team can scale the business in a highly efficient, rapid and predictable way.

You can read the full article – Focus on Repeatability –  on the Wall Street Journal by visiting this link: http://blogs.wsj.com/accelerators/2013/10/01/peter-nieh-focus-on-repeatability/

 

Follow Lightspeed on Twitter @lightspeedvp

 

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