Marissa Campise's Blog

Klout Dominance: Why We Believe

We are big believers in Klout! I was lucky enough to participate in the Series B financing. Since that time, through the hard work of Joe Fernandez and team, Klout has experienced explosive growth. With that backdrop, I am thrilled to announce Venrock’s participation in the Series C Financing. 

Klout measures consumer influence across social media. As social platforms continue to grow, it becomes increasingly important to have a standard system for identifying and measuring influence. Klout is this global standard.

The Social Media category continues to fragment with new platforms showing explosive growth. These platforms are quickly becoming real media channels with scale.  As with any media channel, businesses need to understand the nature of the channel, the mix and makeup of the audience, who matters in that audience, and how to reach that audience at scale.  In a broad sense, I like to think about Klout as the Nielsen of social media. Klout enables advertisers to determine where and whom to target to help gauge the efficacy of advertising. Any consumer-facing company that uses a CRM product will want Klout to enhance their customer outreach. Any application can use Klout to better understand their consumers by using influence scores and categories.

Klout uses the data it collects across different social media sites to identify influencers and segment them according to influence category.  Externally, consumers have a single Klout score that measures their general influence online, but behind the scenes these users are segmented according to an incredible array of categories.  Klout currently analyzes a variety of sites, including Facebook, Twitter, LinkedIn, FourSquare, YouTube, Instagram, Tumblr, Blogger,, Google+ and Flickr, with many more on the way. Advertisers and businesses can access influence data via an API to run targeted campaigns with consumers in different categories of interest. 

The imprimatur Klout has achieved with brands and agencies is remarkable. The company has achieved a high level of recognition and has emerged as the standard for influence. As the web is rebuilt around people rather than pages, Klout has become the next critical layer of the analytics and measurement stack.

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More thoughts on market sizing

Update: This is simply a market sizing exercise for people building a business in a market that doesn't exist. It does not reflect my actual thoughts on value. If you notice, I rewinded to 2009 and only explored one business model for clarity.

The New Market

Yesterday we talked about the established market but the market sizing exercise starts to get really interesting when you think about markets that don’t exist.  Let’s take a company like Twitter circa 2009, when there was still a lot of ambiguity around what the size of their market opportunity looked like (some might say this ambiguity still exists today).  The executive team probably had a vague sense that there was going to be some kind of advertising supported model to the business and I’m sure their investor decks contained the requisite ad-supported slide: “$300B in advertising spending in the US and only $25B of it is online!” 

Like most consumer internet companies, the key market sizing question for Twitter is very simple: what is their annual revenue per user at scale? 

Let’s start with a very simple model. Let’s suppose that Twitter will be purely ad supported. The basic market size equation that we’re going to start from is as follows:

Twitter Market Size = (Users) * (number of ads/user) * ($CPM of ads)

We can begin by decomposing the number of users. What does a typical Twitter user look like?  A simple assumption to make is that over the next 3-5 years, the typical Twitter user will be somewhere between 15-34, with a lower diffusion rate in the 35-49 year old category and a very limited diffusion rate above 50.  The Zynga case might make us question some of those assumptions around people over 50 but let’s play it safe.  To begin, let’s start with the following numbers, based on US Census Data from 2000:

·       15 - 34: (79MM people) * (100% possible diffusion) = 79MM users

·       35 - 49: (65MM people) * (50% possible diffusion) = 32.5MM users

·       50+: (76MM people) * (10% possible diffusion) = 7.6MM users

·       Total Potential Twitter Users = 119MM users

There are several factors that will influence this number, including what percentage of people have internet access, socio-economic factors, and general appetite for digital media.  Clearly, this number can be refined. 

From here, we need to think about the number of ads each user will see.  This is particularly tricky with Twitter given that a lot of users are on 3rd party clients where it may be difficult to track ad views and CTR’s and where Twitter may not even be able to serve ads into.  This is a whole other discussion but for purposes of this analysis, let’s assume that everyone goes directly to  This is where the wheels start to fall off the proverbial VC short bus. Twitter has no idea what the ad units will look like, what is the ideal number of ads served, how much time a user will spend on, or whether ads will work at all.  Oh well...  The analysis must go on.  Let’s assume that our thesis is that will be primarily a source of news distribution.  During the week, most users check a news website once in the morning and once in the afternoon, for an average number of daily visits of twice per day.  On the weekend, let’s assume that an average user won’t check Twitter at all since they’ve got a lot more time on their hands to read magazines, browse their favorite sites, and won’t need the quick-news-fix that Twitter provides.  Given the short format of Twitter, serving 1 ad per visit is not unreasonable.  Putting these assumptions together, let’s look at how many ads an average user will see in a year:

Number of Annual Ads Per User = (1 ad per visit) * (2 visits per day) * (20 visits per month) = 40 ads per month. 

As a sanity check, this feels a bit low. Just browse the web for 20 minutes and count how many banner ads you see.  We’ll want to revise this number upwards later on.

Lastly, what is the average $CPM that Twitter will be able to charge?  At scale, let’s assume that Twitter directly sells out 75% of their inventory at a $5CPM and uses 3rd party ad networks to fill the remainder at a $1CPM after revenue-share to Twitter.  These are reasonable numbers based on average CPM rates across all categories for banner ads, but there is a huge open question of whether Twitter ads will behave like banner ads in terms of branding value, CTR’s, and other metrics.  The ad effectiveness profile could be wildly different, in which case our $5/$1 assumptions could be materially off. 

Now, let’s put all of this together to see what the potential ad-supported annual market size is for Twitter in the US:

Twitter Market Size = (119MM Users) * (40 ads / month * 12 months) * ($5 CPM * 75% of ads) / 1000 (necessary to calculate CPM) + (119MM Users) * (40 ads/month * 12 months) * ($1CPM * 25% of ads) / 1000 (necessary to calculate CPM) = $228,480,000 revenue/year.

As a sanity check, is it reasonable to expect Twitter to capture $228MM of advertising revenue, given that online advertising revenue in the US will hit $50B or so in the next 3-5 years?  Probably. As I noted, there are a number of areas where the analysis can be refined and it is likely that our core thesis of Twitter as a distribution medium of news is too limited.  Beyond that, we haven’t explored a variety of other business models that Twitter could pursue, including subscription, commerce generation, data sales, and so forth.  This is where the really interesting discussion points begin. 

Hope this was helpful!



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Some thoughts on market sizing

Over the last few weeks I’ve had some meetings where the topic of market size could have been a bit more rigorously addressed. It’s a hard issue to tackle – particularly when you’re creating a new market – but the topic is critical in every pitch.  There are some occasions where the market size is fairly straightforward.  For example, I’ve looked at a few female focused online fashion companies recently and while I know this is a huge market, it’s still helpful to dive into the issue of sizing for a couple reasons:

1.     Most companies are going after a slice of the market.  The fashion market for 15-44 year old females with household income of $40,000-$80,000 dollars is quite different than the market of all female fashion.  This is an obvious point but I’d say that about 1/3rd of the pitches I see contain market size estimates that include sectors that are outside of the focus of the business.

2.     The market sizing discussion is incredibly helpful in getting to know how you think. Most of the time the intro pitch is the first meaningful interaction between entrepreneur and VC.  I like to think of the market sizing discussion as almost an intellectual discourse between professor (entrepreneur) and student (vc).

With that in mind, I’d like to share a couple different ways that I like to think about market sizing in the consumer internet space.  There are a variety of other ways to think about sizing and a lot has been written on the topic. I’d encourage everyone to spend some time on Google and read up on other opinions.

Established Market, New Product 

Continuing on with the example above, let’s say I’m starting a women’s fashion company that aims to sell scarves online.  Those of you that know me are probably chuckling right now since I’ve been wearing the same 3 scarves for the last few years now and am thoroughly unqualified to run such a business. 

Let’s take a first cut here.

Market Size = (number of females in the US in the target market) * (average number of scarves purchased by females) * (average price point)

The types of scarves that I’m selling will appeal to 15-44 year old females with a household income (HHI) between $40,000-$80,000/year.  The US census data groups people into segments of under 15, 15-24, 25-34, and 35-44.  The data tells me there are 8.7MM females in this category. Not a bad start.  Unfortunately, my instincts tell me that scarf consumption varies dramatically by geography. I’m going to make a simplifying assumption and segment consumers into two groups: California People (which also include people from Arizona, New Mexico, Texas, and other states where scarf consumption is de minimis) and Everyone Else.  Note that I’m a New Yorker, though, I must admit that some of my favorite people are from California! After looking through a map of the US and segmenting different states into warm and cold climates , I’ve decided that 20% of the US population are California People and 80% are Everyone Else. 

Next, I need to determine how many scarves are purchased by the average 15-44 year old female in both of these segments. To do this, I got on the phone and called up 20 friends in each of these groups.  Those of you that did not major in History like I did will likely groan that this is not a statistically valid sample.  I agree.  It’s a start. If you want a statistically valid sample, there are a number of online survey companies that can get you this data fairly cheaply.  My very un-rigorous survey reveals that the California People buy 0.3 scarves/year and Everyone Else buys 1 scarf/year.  Moreover, I got the sense that Everyone Else takes the quality and fabric of their scarves seriously and probably pays more on average per scarf than the California People.  More on this later.

Now we’re on to the final stretch.  What is the average price point of a scarf?  In my informal survey above, I asked my friends for their average price point but most of them didn’t remember and those that did seemed to give me inflated numbers (so snobby!).  To get insights into this question, I went on Amazon, navigated to the women’s clothing section and searched for the keyword “scarf.” Here is the breakdown:

·       Under $25: 3,758 (50%)

·       $25-$50: 1,728 (23%)

·       $50-$100: 1,348 (18%)

·       $100-$200: 524 (7%)

·       $200+: 107 (1%)

·       Total: 7,465

Using this informal technique, let’s assume that the average price point is $25 for Everyone Else and a slightly lower $20 for the California People.  These are a decent ballpark approximation but can obviously be refined further.

Taking the data we’ve gathered, our first cut at a market size for my new company is:

(8.7MM females * 80% Everyone Else) * (1 scarf/year) * ($25 average price point) + (8.7MM females * 20% California People) * (0.3 scarves/year) * ($20 average price point) = $187,050,000

Does this number seem reasonable or is it out of line with reality? As a quick sanity check, the next thing I did was go to the Consumer Expenditure Survey maintained by the Bureau of Labor & Statistics and look at the total amount spent on clothing by US households and the look at what percentage of the total clothing market the $187,050,000 represents. 

Lastly, I asked myself, what is a reasonable amount of the market that I could capture? Fashion is a particularly fragmented market and even if I become the category killer in scarves, it’s unlikely I’d get more than a few percent of the overall market.  Let’s say I can capture 5% of the overall scarf market – an extraordinary number in any fashion related category, then I’d be making around $9.3MM/year given the numbers above. 

Note that this is a quick-and-dirty analysis. An actual analysis should be significantly more rigorous in terms of data quality and layer in more refined assumptions.  For example, my segmentation of California People and Everyone Else, while entertaining, is too simplistic to withstand real scrutiny.  The same goes for my methods of data collection. Tommorow I'll post some thoughts on how to approach a new market.




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‪Venrock’s Campise Doesn’t See `Bubble’ in Technology Yet‬‏

I was in San Francisco last week and had fun meeting with lots of companies. I also did a short interview with Bloomberg. Here it is..

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Joining Venrock

It’s a very natural thing to do on your first day of work: fire up your computer, check a bit of email, and read up on the upcoming events of the week.  On this particular day, reading up on the events of the week meant looking through the materials for our upcoming Limited Partnership meeting.  Contrary to popular belief, the “LP meeting” is not one of those shrouded-in-mystery type events; it’s fairly straightforward.  The team presents to the investors on the state of the portfolio, fields questions, and gets to hear from a few select speakers. 

It struck me that the job description of the keynote speaker at the Venrock LP meeting was unusually sizable: “Aneesh Chopra's job will be to promote technological innovation to help the country meet its goals such as job creation, reducing health care costs, and protecting the homeland.”  Wow.  Kind of puts things in perspective.  Aneesh is the Chief Technology Officer of the United States and was appointed by President Obama in 2009.  Even more importantly, he is the first person to hold this title (and what a great title it is).  Just thinking about this…the ENIAC, which is widely regarded as the first computer ever invented, was built in 1946.  Fast forward 63 years and we now have our first CTO.  There’s a definite thoughtfulness in the selection approach for this role. 

Aneesh covered a wide range of topics in his discussion, but underscoring all the themes that he touched on was the issue of data analysis.  The US government generates unbelievable amounts of data across every category you can imagine: packaged food composition, mining production, reservoir water levels, medical facility ratings, and my personal favorite, the American Time Use Survey. All of this data has been coming online over the past few years and there is still a tremendous amount of data that is not yet accessible.  A number of interesting companies have already begun to use these datasets to gain a powerful advantage.  As @jonathanmendez pointed out, one great example of this is Urban Mapping. I am confident that many more will emerge.

The conversation with Aneesh was inspiring because it brings into focus the reason that I got into venture capital in the first place: to find and invest in great entrepreneurs that are tackling problems of vast importance.  Venrock has a long and rich history of executing on this goal and I’m proud to be working with such an accomplished group of investors.  With such an exciting entrepreneurial community bubbling here in New York ($2.2B of venture money in NY in 2011!), I’m enthusiastic about what this next year will bring.

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