Andrew Chen Archives

Hi everyone,

Hopefully all of you are enjoying the blog although I’ve only been writing about once a week now. We recently passed 7,000 subscribers, which is fantastic. The blog continues to grow and seems to be a useful reference for people.

Anyway, as some of you know, I’ve been working on some of my own startup projects – still quite early – and am now looking for recommendations for smart engineers.

In particular, I’m looking for advice on:

• Engineers that are interested in consumer internet startups
• Have experience with Ruby (or PHP/Python) including frameworks like Rails or Django
• Can work with me on customer-centered product development
• Have a BS/MS in Computer Science or equivalent experience

If you have recommendations for interesting people for me to talk to, please shoot me a note at voodoo [at] gmail.

I’d hugely appreciate it!

Thanks,
Andrew

Answer this question honestly…
When’s the last time you spoke to your target customer? Like really talked to them?

If it’s been more than a month, then shame on you!

Consumer internet companies are often overly dependent on quantitative data like Google Analytics, but without understanding the qualitative parts – the consumer psychology that actually goes into making purchase decisions. It’s a good idea to balance out the data aspects, particularly if you are not your target customer.

If you haven’t finished developing your product yet, that’s no excuse! After all, there are many methods of doing qualitative user research without writing a single line of code. In fact, in many ways talking to your customer and understanding them great detail is often much more powerful before you even go through the product development process.

How to recruit target customers to talk to, in 5 easy steps
It’s very very easy to talk to people on the internet. You really don’t have to do much work. Here’s what I will often do, in order to get some opinions about a particular set of products, or to deeply understand user behavior (like gifting! or decorating), or to get a better picture of what people do day to day.

Step 1: Write a recruiting survey
First off, go to Wufoo.com or a similar site (Surveymonkey.com works well too).

The most important part is to title the survey “Get a $20 Amazon gift certificate for 1 hour on the phone” or something similar.

Make a survey that includes the following questions:

• First name (text)
• Phone number (phone number)
• Email so we can send you a gift certificate (text)
• Best time to call, morning, afternoon, evening, weekend (multiple choice)
• Tell me about yourself! (textarea)

That is usually a good base, and you should make all the entries required. Then you also want to provide a couple questions that can help you screen or otherwise prioritize your questions. For example, for a Facebook app you might ask:

• What types of games do you like (multiple choice) 
• What kind of phone do you have? (multiple choice)
• Why do you like game X? (textarea)
• Have you ever spent money on a game? (multiple choice)
• etc.

Anyway, you get the point. I usually try to keep these pretty short.

Step 2: Recruit your participants
Now that you have a survey set up, then you can take the URL and start getting people to fill it out. There are a couple obvious areas to recruit people, and I typically do the following:

• Link the survey from your product (if it’s out there)
• Buy ads on Facebook and send traffic to your link
• Post your survey on Craigslist
• Buy ads on Google Adwords and send clicks to your survey

For the ad-based solutions, I will usually limit the buy to $50 per day, and spend $0.50 or so per click. I usually find that it costs about $1-2 per survey completion. After I recruit a couple dozen, then you can start moving forward with the call.

Step 3: Do your phone interviews and learn something!
This where you’ll learn the most – you can just pick up the phone and start talking. I usually structure the interviews into a couple distinct sections, depending on what I’m trying to learn.

The first section I usually try to learn about basic internet usage:

• Tell me about yourself
• What’s your typical day like?
• Tell me about your computer setup – what do you have? When do you use it?
• What are your favorite internet sites? What sites do you use every day?

Then depending on the topic, I’ll usually drill into 3 or 4 different areas with a couple questions each. The entire point is to ask open-ended questions without leading them too much. I will do as many of these calls as makes sense until I am hearing the same information over and over. Then I’ll start tweaking things and changing the interview to adjust.

Also, I will usually not show them a product unless the entire discussion is focused on that – the point of these conversations for me is usually qualitative understanding, not usability. Having them thoroughly test competitive products can be interesting also. You want to use this information to drive product strategy, and not be reactive.

I guarantee you’ll learn something!

Step 4: Buy your interviewees a gift card
When you’re done, don’t forget to send your interviewees a gift certificate – $20 card from Amazon is a good idea – to thank them for their time.

One of the best things is that once you get some relationships going with the best interviewees, you can go back to them for updates or to identify some of the most extreme cases.

Conclusion
The point is, it’s easy to talk to people, and it’s this type of detective work that separates customer-focused companies from technology-driven ones. There’s even a fun tool to suggest a bunch of other methodologies like this also – the IDEO method card deck.

If you have other additions to this, please suggest in the comments!

Want more?
If you liked this post, please subscribe or follow me on Twitter. You can also find more essays here. 

Here’s more people to follow on Twitter!

As a followup to my previous post Bay Area entrepreneurs I’m following on Twitter, here’s the version for investors (mostly focused on early stage angels).

Freemium pow-wow!
My friend Charles Hudson and I recently co-hosted a dinner conversation on the topic of Freemium business models. First, a quick blug: if you aren’t reading Charles’s blog, you should check it out! He runs BD at Serious Business up in San Francisco, and also has put on a number of great conferences like the Social Gaming Summit.

Anyway, we had a bunch of interesting people on hand, including folks who were working on monetization from a bunch of companies. The dinner was generously hosted by Bluerun Ventures, and we ate a lot of pizza. We had folks from places like:

• Xobni
• LogMeIn
• YouSendIt
• Puzzle Pirates
• Dropbox
• Imeem
• Dogster
• Crazy Egg
• etc.

There were a couple of key themes in the conversation, which I’ll outline below.

Key idea #1: There’s Consumer freemium, and there’s Enterprise freemium
First off, there was a strong distinction between the usage of freemium in the enterprise versus consumer. In many ways, it was as if there were two completely different conversations going on. In the consumer world, the focus is very much on topics like: payment methods, virtual items, subscription vs microtransactions, etc. In the enterprise, much of the focus is more on the IT infrastructure, departmental structure, expense reports, etc.

I think ultimately the distinction comes down to the fact that in the consumer world, people are spending their own money – as a result, they are much stingier, the demographics are more difficult, and you’re often an entertainment experience competing with other discretionary products. Compare this to enterprise, where the goals are more often utilitarian, and business users can more easily justify an ROI with the tools. Furthermore, because the users live in a broader business ecosystem, you have to deal with the IT organization, as well as the opportunity for people to simply expense their freemium costs.

Key idea #2: Freemium playbook has already been written
Another interesting discussion revolved around the fact that many of the basic tactics in the freemium world have already been documented and used by previous players.

In particular, there are tactics out of the playbook such as:

• 30-day free trial (with credit card upfront)
• Free service platform that upsells multiple premium products
• Freemium service that disrupts existing pay-only product category
• A/B testing pricing, purchase flows, etc.
• Achieving purchases by optimizing the new user experience
• Default to premium product, but allow the user to skip to Free
• Lifecycle-based discounts and upsells
• Start with a high price but A/B test coupons to price test
• etc.

(am I missing any? Please write me a comment! Will drill into these in more detail sometime)

UPDATE: Ted Rheingold from Dogster also added a couple ideas that came up – see the list below:

• The higher the price point, the less churn/drop-off amongst subscribers.
• Offer users a 30-day free version of the premium right next to offer to join free service. Put the two free offers side by side so people a) know they are making the choice for the premium version, b) less likely to be concerned about a ‘catch’
• Offer a money back guarantee period once payment starts.
• On the consumer side do not overlook the emotional motivation to subscribing. Whether it to feel a part of the club, to show your elevated commitment, or to keep moving up the kicking-ass ladder (see Kathy Sierra) subscribing to even utility services such as LinkedIn can have a very strong emotional component.

Many of these tactics have been used by successful players in the market – the most often used examples are ZoneAlarm, eFax, AVG, and others. In fact, here’s a longer Linkedin discussion with many of those brands and more. Sean Ellis in particular is an expert on this area.

Note, of course, that most of the above tactics and examples stem more from the enterprise world, whereas the consumer folks tend to use different examples – like Skype, Cyworld, etc.

Key idea #3: Freemium products face common design challenges
As a corollary to having a playbook of different tactics, you might also imagine that Freemium products must have similar design challenges as well. In particular, the biggest question of freemium is:

When does Free stop and Premium start?

On one hand, if you give away too much, then your conversion rate from free-to-paid ends up being too low. This means that people are too easily satisfied with your product, and have no reason to convert to being a paid user.

On the other hand, if you force the user to premium too early, then you lose out as well. They may not give your product a chance, and move on to something else, before they start down the path of converting to a premium user. Similarly, the free segment of your audience can help drive distribution and virality, and without that group, it becomes much harder to get meaningful amounts of traffic.

Charles Hudson has a great discussion of this design issue on a recent blog, where he writes:

It is very difficult to properly segment users and features such that you provide enough value to both paid and free audiences. For example, an email service that provided a 10 MB of storage for free and 1 GB for the paid version would have a hard time surviving – the basic offering isn’t sufficiently compelling to get people in the door. Conversely, a service that offered 2 GB for free and 10 GB for the premium service might be giving away too much value in the free product to expect a large audience of people to upgrade. And that’s just one product dimension. Adding more dimensions just makes it that much more difficult to figure out the features for which users would be willing to pay.

You can read more here. The longer blog goes into the discussion of the best way to segment free versus premium, and whether it’s better to go with a trial period with a full premium product, or if it’s better to go with a stripped-down Free product and a separately upgraded Premium product. This segmentation is a key design issue in the Freemium world.

Special thanks to Hiten Shah for helping me recall some of the bullet points!

Just finished “Stealing MySpace”
I recently got a Kindle, and one of the first books I ordered was Stealing MySpace by Julia Angwin, a journalist from the Wall Street Journal. There are lots of interesting reviews on the Amazon page, so I won’t rehash them here.

I added the book to my books list, which you can view here.

Instead, I’ll give a quick description of how I first ran into MySpace at my previous company, Revenue Science, and what I learned from it.

Why start a cost-per-click behavioral ad network?
Revenue Science is a behaviorally targeted ad network – this means that they tag users based on their browsing history, and then target ads to them based on their historical behavior. So ideally, if you knew that they were in market for a car, then you could target them with car ads and achieve better clickthrough rates and conversions. This technology has slowly permeated all corners of the ads world, and it’s a powerful story.

In 2004, we were in talks with Yahoo/Overture to experiment with behaviorally targeted ads within the Yahoo network. As with all behemoth companies, they had several internal teams already working on behavioral targeting, and there was some uneasiness due to classic NIH syndrome. As a result, some of our sponsors within the Yahoo organization encouraged us to take an XML feed of all of their ads, with some basic pricing data, and sign up publishers as a full-blown ad network. The arrangement was that we’d receive access to their thousands of cost-per-click (CPC) text ads, and we’d put together data showing that we could generate revenue using behavioral targeting in a direct response setting.

Chicken or the egg
Of course, with most ad networks, there is usually a chicken-and-egg problem of getting advertisers and publishers to the table. If you don’t have a ton of advertisers already buying, then you can’t deliver premium CPMs to the publishers. And of course the advertisers won’t buy without your network being able to deliver a significant number of ad impressions with publishers already signed up. Having this XML ad feed from Yahoo made it easy for us to bootstrap the advertiser side of our ad network quickly and easily.

As an aside, most ad networks get started because a top salesguy splinters off from an already effective network and brings a bunch of advertisers with them – that means they can do some buying quickly and easily to start the business. As with most marketplaces, the Golden Rule applies – “he who has the gold makes the rules” – and as a result, most networks tend to skew towards beyond advertiser-friendly rather than publisher-friendly.

Adventures on the open internet
Now that Revenue Science had access to a bunch of advertisers, it was time to sign up publishers. I started a small group within the company that consisted of a couple inside sales folks who could sign up publishers, fax them contracts, and quickly get ads flighted. This was truly a startup-within-a-startup experience.

One of the first thing we did was construct a big target list – essentially this meant buying the list of domains from Alexa, Nielsen, and others, and prioritizing them based on factors like:

• Is the content written in English?
• Do they already have ads placed?
• Are they selling something, or is it a content site?
• Is it a forums site? Or a social site? Or a communication site?
• Do they have at least 1 million impressions per day?

Using some of the basic criteria, we could have a team of fresh-out-of-college sales analysts go through the top 30,000 sites on Alexa and email each publisher.

The sales pitch was simple – “You know that your audience is worth a lot of money, but your ad revenues aren’t reflecting this.” This pitch was especially compelling, of course, to social networks and consumer internet sites which lacked context. That is, people would go there to “see what was going on” versus going there to buy something specific. And of course, we found that the social networks that accumulated massive amounts of profile data on their users were especially interested in doing something with that data.

We had also heard that LinkedIn and Friendster were making a ton of dough targeting ads to keywords that people had placed in their profiles. It seemed like a worthy experiment.

Finding MySpace in 2004
Right around the end of 2004, we started getting in touch with some of the larger sites on our list. At the time, there was a entry called Intermix which was grouped as a network in the Nielsen top sites listing, but we couldn’t tell which sites were driving the traffic. And so we started emailing the properties we could find, and eventually reached an executive there via one of their eCard sites. As “Stealing MySpace” discusses, they had a number of eCard sites that were old school BlueMountain.com type clones. The business model for these was to harvest email addresses from people sending eCards back and forth, which would then be used to upsell consumers to offer-based monetization.

Anyway, the Intermix guy mentioned a bunch of different ways to work together, and near the end of the call, he asked us if we had ever heard of MySpace. And of course, this being late 2004, there was really no writing about the property, so we thought it was just another random site in the Intermix portfolio.

What really got us was that he said the site was adding 50,000 new registered users a day!

After the call, I ran to my desk and pulled up the site – and was immediately disappointed. It looked like a Geocities site, and a Friendster clone. But it was clear after clicking “Browse” that the site was incredibly active, and I just didn’t “get it” yet – there were tens of thousands of active users, and almost every profile had very recent comments and was completely pimped out.

Monetizing social networks is hard
After meeting with several of the execs there, we started thinking about a custom integration with them whereby they would pass us relevant keywords about user segments.

The founding team at MySpace was superb – we were impressed by almost all folks we met, and it was clear they were a scrappy, entrepreneurial group, not the staid media executives that roam the halls of most public internet companies. Many of the folks who are now no longer there, including Jason Feffer, Steve Pearman, and others, are now starting new companies of their own.

Anyway, the entire idea for behavioral targeting on MySpace was that we would take relevant keywords about audiences and then target ads to those users. While this is a fantastic idea in theory, there were a large number of difficulties exposed from this process:

• From a technology standpoint, the keywords in user profiles is extremely free-form. There’s a lot of formatting garbage, like people saying *~ dancing ~* as an interest. Similarly, people filled out the interests in complete sentences, jargon, and many other non-trivial text parsing problems
• Similarly, there’s a lack of purchase behavior on social networks – because people are there to hang out, they aren’t putting things like “looking for a new digital camera” in their profile, nor are they searching for it. Instead, they are saying “i <3 taking pics” which is different than saying you’re in the market for a great new digital SLR camera. The search traffic was similar.
• Also, there is a ton of noise in the clickthrough data we’re getting back – we found that MySpace traffic was very noisy, had a lot of accidental clicks, and thus created problems in the backend for advertisers
• Similarly, there was just SO MUCH traffic – when we started working with MySpace, they were at about a 1 billion ad impressions per month, but quickly got up to 1 billion ad impressions PER DAY. Pretty amazing growth, and we worked with them right through the inflection point. This really compelled us to get very serious about scaling our ad servers, and we heard repeatedly from other ad networks that they couldn’t take the volume MySpace tried to give them
• Another difficulty was the completely context-free ad impressions that exist on a social networking site – people are there to hang out, not to buy stuff, and so the clickthrough rates were very low. Anywhere from 0.05% to 0.2%, but never into the >10% CTRs that you’ll see on search pages.
• Visit lengths were another issue – as I’ve written about previously, more engagement doesn’t better monetization. The first 10 ad impressions might monetize extremely well, but once you’ve exhausted those premium campaigns, your super hardcore user that generates 200 pageviews is not substantially different than your engaged user that generates 30. Once you get past a certain point, then it’s all punch-the-monkey ads.

I’ve covered these monetization issues in more depth here and in these essays here.

That said, it was clear just looking from the data that MySpace was a very special property. In particular:

• The traffic was growing incredibly fast – as I said above, they went from 10-15M registered users when we started working with them to over 100M very quickly, in just a few years
• The users were highly engaged, and were definitely spending hours on the site – this is all obvious now, but at the time, MySpace was the only site we were seeing this type of engagement
• The geographies of the users were all across the US, but also 95%+ American traffic – which is a huge premium within the advertising industry

I don’t think it needs to be said, but for the folks who are ga-ga over the monetization potential of Twitter, I’d encourage you to think about the monetization shortcomings of social networks, blogs, and email, and present an argument about why it’ll be 100X better. (That said, I love using Twitter as a service, so I hope they figure it out!)

And in comes Google
Over time, we started to find things that worked well for us to monetize MySpace traffic. Taking in-market data from outside of MySpace and then targeting those same uniques was definitely effective. We found that certain ad units and sections monetized much better than others.

Very quickly though, Google came in and threw down their $900MM deal for MySpace’s traffic. The details of this deal are in Stealing MySpace, and while I got to hear about the aftermath of the deal, Julia Angwin’s book fills out a bunch of the details from the executive point of view.

The point is, while the MySpace traffic was certainly amazing, it was clear to me (and the other ad networks folks I was in touch with), that there was no way the ads would perform at the level to justify the deal. And ultimately, I think we were proven right. The deal seemed like a crazy auction with a “winner’s curse” and it always seemed like the big bucks were going to get attached to the brand deals the FIM/MySpace team were putting together rather than making the remnant text ad inventory perform 10X better.

Conclusion
Now it’s clear that MySpace’s dominance of the internet is waning, and to me, there’s no stronger indicator of this than the search term “myspace” plateau’ing recently. This means that folks who typically type it to get back to the site via navigational search are not as interested anymore. You can fix some aspects of retention through notifications, better SEO, etc., but if your users don’t want to search for you anymore, then something is wrong.

Graph of searches for “myspace”

Anyway, only time will tell if MySpace is able to recover their strength, or if they are stuck at where they are. Certainly their monetization is bound to improve, but turning the ship on growth is always very difficult.

Want more?
If you liked this post, please subscribe or follow me on Twitter. You can also find more essays here.

Another guest blog, today from Sachin Rekhi, who is currently an entrepreneur-in-residence at Trinity Ventures. You can find more of his writing at SachinRekhi.com, and follow his Twitter at @sachinrekhi. Most recently, he ran product efforts in Imeem’s monetization efforts, and before that started the company Anywhere.fm through the YCombinator program (acquired by Imeem). I asked him to write about some of his work monetizing Imeem’s ad inventory, and some of the issues he worked through – his post below is about those projects. One last important note, Sachin is engaged to marry my sister Ada next year! Congrats to the happy couple ;-) –Andrew

Designing and Testing an Ad Product: 5 Lessons Learned from imeem’s Audio Ads
By Sachin Rekhi

Introduction
In its search to find the most effective way to monetize user’s time spent listening to music, imeem has become one of the early innovators in the nascent online audio advertising space.

From the process of designing, testing, and iterating on imeem’s unique audio ad product, I wanted to highlight 5 key lessons learned that are applicable not only in developing imeem’s ad offering, but in general to designing any innovative ad product.

Lesson 1: Align the ad product with your site’s user experience
Lesson 2: The easy way is often not the best
Lesson 3: Pick the right metrics to optimize
Lesson 4: Make sure to look at qualitative feedback
Lesson 5: Iterate on the sell in addition to the ad product

Align the ad product with your site’s user experience

Yet we knew with our audio consumption experience, we were creating a new kind of available ad inventory which could be much more effective at reaching our users than display ads since audio-based advertising better aligned with the activity users were most engaged with on the site. With terrestrial radio ads still generating $21B in revenue, there was clearly an opportunity to shift some of those dollars online and provide a better experience for both users and advertisers.

The easy way is often not the best
Online audio ads are not a new concept. They have been used by a variety of major online streaming outlets, including AOL Radio, CBS Radio, Live 365, and Yahoo LaunchCast. However, the initial incarnation of audio ads took the easy way out. They typically ran 30 second audio ad spots which they obtained from ad agencies that re-purposed their terrestrial radio creative for online audio ads. This made it very easy for agencies to get their feet wet with online audio advertising with no additional creative costs. While this may work for traditional online streaming services, the new generation of music streamers like imeem, Last.FM, and Pandora would not be willing to run such long audio ads out of fear of losing their user base.

However, this was far from easy, as it required imeem to develop in-house production capabilities for the 8 second audio creative, as agencies never had existing creative and were rarely willing to develop another set of creative themselves. While this was an undertaking, it is often necessary to bear the cost of innovation to deliver the right ad product to your audience.

Pick the right metrics to optimize
In order to understand the effectiveness of any ad unit, it’s important to systematically test it. The first step in designing a successful experiment was determining what were the metrics that we were testing. We knew that we were trying to satisfy two customer segments with this ad product: advertisers and users. For advertisers, there were a variety of ad-related performance metrics that we could measure. However, we decided to start by measuring the advertiser metrics that ad agencies had classically been most interested in. We wanted to determine whether we could make advertisers happy through the performance of these classic metrics, since trying to educate ad agencies on the importance of new metrics is an uphill battle that would significantly decrease your ability to sell the unit. Thus the initial advertiser metrics we tracked were click-through rate of the tethered medium rectangle banner as well as aided and un-aided brand recall as measured through quantitative surveys administered by our research partner Dynamic Logic.

For users, what we wanted to understand was whether introducing audio ads onto our site would decrease the amount they used the site. While we tracked page views, visits, session length, etc, we focused on number of songs played per user during the life of the experiment as the most important proxy for site usage.

Make sure to look at qualitative feedback
In addition to measuring quantitative metrics, it’s equally important to collect qualitative feedback from real users. The iModerate online focus groups we conducted ended up being very enlightening and allowed us to derive interesting insights of consumer motivations and behaviors that looking at the quantitative data alone wouldn’t provide.

For example, though initially we were significantly worried that the introduction of audio ads would cause users to flock to our ad-free competitors, we learned through interviews that many of our young users had developed a strong affinity with imeem, understood the need for imeem to monetize, and were eager to suggest ad verticals they would be most interested in hearing to improve the product.

Iterate on the sell in addition to the ad product
An area that’s as important to iterate on as the ad product itself is how you sell or position this offering in the marketplace. Selling innovative ad products is actually the greatest challenge in the process. Anytime you introduce a new ad unit, significant education is required for brand marketers and agencies to help them to understand the importance, effectiveness, and promise of this new medium.

Our sales planning team iterated many times on the pitch to advertisers for the audio ad product as well as how we reported on ad unit performance at the end of each campaign. This was regularly refined based on feedback we elicited from our advertising partners.

Conclusion
While many have claimed the death of online advertising in light of the recession, its important to remind ourselves that ad dollars are still being spent online. Now is an opportunity to innovate on the ad products that we offer advertisers to show greater value, brand awareness, and performance. We must keep in mind that ad agencies are eager to find better ways to spend ad dollars, as they are equally interested in showing results to their brand clients to hold on to their ad budgets. We should partner with our advertisers and users to find the most efficient way to leverage online advertising to monetize our sites.

My friend Jay Weintraub writes an amazing blog about the leadgen industry at JayWeintraub.com, which I’d recommend you check out. He also runs the LeadsCon conference series. Since Jay is such an expert in the leads space, I recently asked him to comment on the incentivized social offer platforms that have recently seen much success, and assess the pros and cons of the business. His thorough response is below! –Andrew

Conversionomics: Analyzing social cash/alternative payment platform market longevity
by Jay Weintraub

(Advanced/Ad industry readers: Scroll to “Conversion Economics” Section)

A Not So Brief Background
Every once in a while, the world of higher brow internet advertising concepts – social media, engagement, Web 2.0, etc. – intersect with the oft-perceived lower brow world of performance-based advertising. Calling performance-based advertising lower brow does some injustice to one of the more dynamic sectors, but this is the same sector that has figured out how to convince tens of thousands of users daily to sign-up for “free” trials of acai berry and colon cleanse products via the flog. It is also the same sector that despite the legal and regulatory hurdles (including millions in fines) thrown at it for its marketing of ringtones continues to generate well north of one million new cell phone pin submits (monetary transactions) monthly for other mobile subscription services. If it doesn’t seem intuitive that this almost anything goes wild west of arbitrage would intersect with the heavily funded world of higher brow online advertising, you’re not alone. It was purely accidental but perhaps not unexpected at some level, especially when we consider the underlying trend that tends to attract performance marketers – massive amounts of inventory that very few have a grasp on how to monetize. That description fits social media to a tee.

In the display world, surplus inventory tends to get lumped into the bucket of remnant ads. In this case, the surplus is only remnant because few outside of performance marketers have started to figure it out. The social media inventory being referenced here doesn’t include the MySpace login screen or other areas that a) have enough scale and b) have won over the trust of agencies. We are talking about inventory that to some doesn’t differ that differently from user generated content, application traffic. We could write a whole piece on how the dominant ad networks missed the boat on monetizing app banner traffic. Each app has made room for standard ad units, and taken together, resemble any other collection of publishers. The networks would even have greater data available for optimization but alas no cookies, on which most of their optimization platforms are reliant. Had these networks become the dominant players, we would still see the same result, though – a limited revenue stream for the app owner. None of which is new, especially to this audience. It does, however, set the stage for the unexpected rise of the more dominant player in app monetization, a group that taken together makes almost as much money monetizing app traffic as Facebook does monetizing their internally controlled impressions.

If we are honest with ourselves, it is fair to say that no one would have guessed just how lucrative monetizing app traffic would be. The data might now tell us this, but ask someone if they’d sign-up for Dish TV in order to earn points to dress their baby or buy their friend, and the rational person would say no. Sure, they might do some things in order to earn more points – send invites to their friends, come back daily, etc., but jump through enough hoops for a mob-themed fantasy game enough to earn its founders more than one million monthly? No one saw that coming. Plenty of companies are profiting from this discovery – be it app makers themselves like Zynga or especially the new breed of companies that enable this commerce, the alternative payment platforms which include the likes of OfferPal Media, Super Rewards, Peanut Labs, Gambit, etc. Only now are those not directly tied to this ecosystem starting to grasp the scale that some of the companies have reached (>$100mm annual run rates). They are profitable, enjoying hockey stick like growth curves and great valuations by the venture community. They are also, for the time being, indelibly tied to the performance-marketing space, as that is from where the vast majority of their ads come. Why? Because ultimately, the alternative payment platforms are a form of incentivized ads.

History Repeated?
Among almost all forms of performance-based online advertising, not many have enjoyed the fame and notoriety that incentivized marketing has. Its initial rise to prominence took place in a similar setting – a media recession with a surplus of untargeted inventory available. The incentive promotion ads, think “Free iPod” offers, did what other types of online advertising could not. They took someone who hasn’t expressed any intent about a product or service and turned them into customers for a variety of products and services. Those who mastered this made large sums of money, so much so that they can afford to purchase other companies with a better public image, e.g. Intreprid Investments buying Q Interactive. Those who mastered it even better, found themselves in the cross-hairs of the FTC. Distilled to its core, though, the incentive promotion companies operated on a simple principle – users want something, e.g., an iPod, and they will jump through hoops (complete offers) in order to get it. At that level, it doesn’t seem conceptually different than the alternative payment platforms. This newer generation of companies uses the same ads, the same tracking methodology; they just get their users through a different way – app traffic instead of banner/email/search traffic.

Given the boom and bust that the original innovators of incentivized marketing have undergone, the big question for many tracking the unexpected popularity of the real Incentivized Marketing 2.0 is what does the future hold in store for them. It is a topic that entrepreneur Niki Scevak, and the most widely quoted blogger (from Andrew’s blog to Venturebeat) for someone with supposedly fewer than 200 RSS readers, discussed in his fantastic post, “The Impending Doom of Facebook Apps.” At the heart of the issue – quality. The incentive promotion space gained a reputation for burning through advertisers due to its horrible quality. Users who signed up for offers didn’t really want the offer. They just wanted their now not so free good. And a whole cottage industry even sprung up on how to game the system to get your iPod or other electronic for as little real money as possible. Is the same fate to beset the alternative payment platform space? Says, Niki,

“The consumer behavior has changed only subtly from five years ago: instead of completing a laundry list of offers to qualify for a $150-250 value product, each consumer is completing a small number of offers for a smaller economic value to be used in the game or application.

But just like credit card companies and Netflix were happy to give the free ipod guys a shot, they are also happy to completely shut down the channel as well once it proves it doesn’t work. As the category of leads/customers grows, the more important it will be for more senior marketing folks to take a real look at the quality of leads provided through Facebook games and apps. And they’ll find the same result of quality they did back in 2004-5: a whole heap of shit.”

Types of Offers
Front and center in the ecosystem are the ads that these “Managed Offer Platform” companies (a term coined by Offerpal Media) run. At the end of the day, they pay the bills. In the world of online advertising, ads tend to fall along one of the following payment metrics – per thousand (CPM), per click (CPC), or per action (CPA). Those in the offer platform space focus on the last, per action. But, per action is a broad term encompassing the following subcategories – per sale (CPS) and per lead (CPL), the chief distinction being that the former requires data plus credit card whereas the latter requires just a user’s data. To confuse the situation further, we could create a whole new category of CPA ads based around subscription services (a form of cost per sale) – one that includes payment with credit cards, e.g., Netflix or the (potentially) more sinister free trials (acai berries for example.) This subscription category also includes services where the transaction occurs using the mobile phone (ringtones, quiz, crush).

In case you haven’t installed an app that uses one of the offer platforms, here are some sample ads that you would see. This example looks at ads as a user tries to earn Lunch Money, a virtual currency managed and tracked by alternative payment platform company OfferPal Media. You will see examples for per lead (auto insurance), mobile subscription (IQ Challenge), credit-card subscription (Netflix), per sale (Zone Alarm), and even one incentive promotion 1.0 offer (free laptop). Here there are:

Take the IQ Challenge!
Quiz Ad – Todays high score is 125. See if you can beat it? Compare your score to others in the IQ Challenge community!

No credit card needed to receive L$ within minutes.

L$ awarded after submission of a valid mobile number and PIN confirmation.

Price – 7.05

Netflix DVD Delivery Service – Free Trial & GET L$!
Try Netflix DVD home delivery for FREE and get some L$. No coupon codes allowed! If you use a coupon code, your L$ will not be awarded.

Purchase required to receive L$ within minutes.

L$ awarded upon registration for home DVD delivery. New members must order and receive initial movie order or L$ will be reversed.
Price – 15.00

Get Free Auto Insurance Quotes and easy L$!
Direct Insure Online offers exciting new insurance quotes fast, free and easy. Just fill out a simple form and get up to FIVE quotes which can help you save money on your Auto Insurance.

Free! No purchase required to receive L$ within minutes.

L$ awarded upon Complete insurance quote request. Fraudulent information will lead to expulsion from application.

Price – 3.78

Share your thoughts on Laptops, and get one for FREE!
Answer a short survey on laptops and receive one for FREE! Participation required.

Free! No purchase required to receive L$ within an hour.

L$ awarded after submission of a valid email address, personal information, and navigation to the final offers page where you must click on an offer.
Price – 3.00

Make your PC Safe and FASTThe Most Complete Internet Security. Need home protection for up to 3 PCs? Get the ZoneAlarm Internet Security Suite 3 User-Family Pack for just $10 more!

Purchase required to receive L$ within minutes.

L$ awarded when you buy now.
Price – 19.80

The “Price” reflects what the user earns as a result of their efforts. In this case, Lunch Money (L$) gets awarded in the millions, but we rounded down to the nearest million. And, for those who do not want earn their lunch money, they can purchase them. The rate is $9.99 per 100 million L$. It’s a data point that might not seem like a lot but provides us with a very important starting point for assessing quality.

Conversion Economics
In the world of performance-based advertising, the price per action correlates directly with quality. The higher the quality, the greater the payout. Let’s look at two examples, one from the lead generation world and one from the subscription world. In the lead generation space, e.g., auto insurance, the data purchased by a lead buyer has no real value to them. It only matters if the leads turn into policies. The greater the lead to policy ratio, the greater the value the buyer can and will pay for that lead. In the subscription world, the signup has some value, for users must enter valid credit card data, but more often than not, the advertiser must pay an initial bounty that exceeds what they earn from that initial charge. The longer the average user stays subscribed, the more the advertiser can afford to pay.

We can now at two examples from the subscription world that appeared as choices for the user wanting to earn their Lunch money – Netflix, which requires a credit card, and IQ Challenge, which requires a mobile subcriptions. Netflix pays the user 15mm L$. (As an aside, that L$ doesn’t have a corollary with real dollars is very wise.) That price is after the payment platform makes its money. In the default situation, this means a 50/50 split between the app owner and offer platform. Assuming that to be the case with Lunch Money apps, the rate to the platform is 30mm. But, what is 30mm L$ in actual dollars. One way to figure it out is to use the hard currency L$ ratio for a clue – $9.99 for 100mm L$. That implies the Netflix offer has a value of roughly 1/3 or $3.00. As a user, you’d be much better off paying $9.99 for 100mm L$ than converting on Netflix for 15mm where you will receive a charge of at least 9.95 on your credit card. The challenge with this math is that the dollar/point ratio doesn’t always give us a good sense for the actual economics. If anything it shows us the propensity for users to select an offer over paying hard dollars. If the system were truly aligned (ad dollars and offer dollars), the user would probably receive at least 100mm because Netflix pays Offerpal at least $20 for that user. At $20, the publisher receives $10, with $10 being roughly equaly to 100mm L$ according to the exchange rate. Right now, though, users don’t understand the offer economics the way someone in the performance marketing space would, so they wouldn’t naturally look to question the point spread.

The second example, mobile subscription offer IQ Challenge, pays 7mm L$. By knowing the market rates, we can back into a dollar per point value. In the affiliate space, this offer would pay between $6 and $10. Given this is incentive traffic we will assume the low end, $6 of which the publisher would see $3. The publisher received $3 and the user earns 7mm, showing an exchange rate of just greater than 2 to 1. It is still not commiserate with the 10 to 1 ratio when directly purchasing L$. But, it is enlightening when we use this price point to try and estimate what Netflix might pay. If the mobile offer pays $6 for for 7mm to the user, then Netflix could pay as little as $13 for 15mm to the user. The wrinkle in the analysis is that that many of the offers do not come direct from the advertiser.

Traffic Blend
Despite their growth, the jury is still out on the offer platform companies’ traffic quality. We know that they are better than the free iPod offers, but the $64,000(000) question is how much better. Is it Scenario A, where they are closer to the incentive promotion space or Scenario B where they are either somewhere in between, perhaps even closer to true intent than they are incentive promotion.

Scenario A:

Scenario B:

Regarding overall quality (and thus, viability), the jury is still out because, not surprisingly, the results are all over the map. There are specific examples from those praising it and those lambasting it. Adding to the confusion, not all advertisers who receive traffic from the platforms realize that they do. This does not mean deliberate deceit, especially on the part of the offer platforms. But, it doesn’t imply complete ignorance either. The CPA Networks (aggregators of CPA offers) that supply them the offers also don’t know the traffic quality yet either, so they hedge their bets. They make sure that the traffic from the “apps” (both the applications and the alternative payment platforms) doesn’t comprise too great a percentage of the total traffic to a given advertiser. That way if the traffic isn’t as good as they estimate, it won’t hurt their relationship with the advertiser. They do this too because many networks know their advertises wouldn’t necessarily give permission to run on the apps, so they take the ask forgiveness route. While it might seem better to operate on full transparency, there are some quirks which prevent a fully transparent system from being the best solution.

When we say the platforms aren’t completely ignorant, it is because they know they have more traffic for a given advertiser/campaign than a given cpa network gives them. As a result, for some campaigns, they end up getting the campaign from multiple providers. Unlike Trialpay, who largely doesn’t play in the app space, those in the app space still rely on third-party providers. That is to say that while they do have direct relationships, it’s not unrealistic to assume that more than 50% of their revenue still comes from third-party providers. Complicating matters further, the platforms might have Netflix (as an example) direct but also run it from a third-party company. This happens all too commonly in the performance marketing world where one company has a lower limit than what they can deliver and another a greater. Plenty of offline parallels exist for that, but whether it is a good or sustainable practice online remains to be seen.

I find it hard to believe that the quality could be entirely bad or that the traffic round-robin (taking ad from one network x%, same offer from another y%) that effective to cover truly bad quality. Another option is that it could be that bad, and that there is enough overall budget spread among networks to cover-up the quality here. Again, with an industry contributing north of $200mm per year among what is ultimately a narrow set of advertisers, the cover-up doesn’t explain things fully. Is it bad but too small to get noticed? Good and built on a solid foundation? Good but still up-in-the air (because of this segment’s youth and the continuity advertisers receiving the traffic having multi-month periods for determining quality)? I’d say somewhere between the last two points.

Standing Hypothesis
First, the bad news. Pricing will probably go down. The “app” ecosystem makes more than it probably should on per unit basis. The good news is that the traffic quality in my opinion more closely resembles Scenario B than Scenario A. In other words, I do not foresee the impending doom of Facebook apps or the companies that help them monetize the traffic. The not so eloquent answer as to why they won’t die comes from the world the world they mirror at some level – the good ol’ incentive promotion space. It is an industry that just won’t die, and it continues to morph as the audience and traffic sources change. If it can survive, how is it that the offer platforms, with their much higher intent, would not? Assuming you want a real answer and not an “if they can so can you” response, here are two other reasons:

1. One to one – while app users are still participating in offers in order to earn soft-money, which by its definition is not only incentivized but also lower intent than a consumer choosing to seek out the service, there is a big difference between how users engage with these offers versus those that are part of the free iPod offers. Users engaging in the app process pick a specific offer from a list of offers. While still somewhat limited, it is still a choice. In addition, users aren’t being asked to jump through major hoops. Contrast that with the free iPod approach where much of the disconnect comes from users being funneled through a flow in which they see more than a hundred opt-ins, none of which actually help them achieve their end goal. Plus, once they finally get to the final page, instead of being a simple process, it is quite cumbersome (number of offers, additional people, etc.) and designed for breakage. They make more if you don’t finish; not so with the offer platforms. They only make money if you do complete it.
2. Accountability – in the incentive promotion space, users enter skeptically and/or with false expectations. Additionally, the incentive promotion path doesn’t have a connection nor does it really try to build a connection with its usres. The exact opposite is the case in the app environment. The user has a vested interest in the app. It is tied to their personality, their profile. The action that they take or don’t take reflects directly on them. Want to cheat the system? You will get caught. But, that is not always a strong detterent because people don’t realize that. So, the offer platforms have become very explicit and obvious regarding the requirements for credit. Take Netflix for example, “Purchase required to receive L$ within minutes.L$ awarded upon registration for home DVD delivery. New members must order and receive initial movie order or L$ will be reversed.” Very clear. So too is the one for Zone Alarm, “Purchase required to receive L$ within minutes. L$ awarded when you buy now.” Perhaps my favorite are seeing the incentive promotion guys’ language, “Free! No purchase required to receive L$ within an hour. L$ awarded after submission of a valid email address, personal information, and navigation to the final offers page where you must click on an offer.” Good luck. Where things get tricky is with lead generation (my area of specific focus). Here, the users have a better chance at gaming the system, and it partially explains why few true lead generation offers exist. The one from above, has the following disclaimer, “Free! No purchase required to receive L$ within minutes. L$ awarded upon Complete insurance quote request. Fraudulent information will lead to expulsion from application.” But, no matter how strict the language, it doesn’t require much sophistication for valid look fraudulent data to be entered.

Saving Grace
Whether the current model employed by the alternative payment platforms stays this course almost doesn’t matter. At some point, it’s expected that users should wise-up and realize that they can play better games for free elsewhere. Not all will, but the enthusiasm and take rates we see now won’t hold. But again, that won’t impact the longevity or long-run success of these payment platforms. App developers and offer platformas can help slow any decline by not being – creating a balance between what they ask users to do with what the users get in return. One could argue that users must do a little too much, and that relatively speaking, they get so little in return. Convering on two higher value offers would get a person a full game on XBox 360, but luckily for this ecosystem, users aren’t rational and thinking about alternatives when in the midst of impulse and ego driven actions.

Ultimately, the platforms have an incredibly valuable asset that assures them a role in the barely developed world of social media monetization – access. They own prime real estate, and if there is one thing that the ad networks have shown, it truly is location, location, location. It is why an ad network with poor tech but great inventory will outperform one with amazing optimization but lesser traffic. Whether they intended to be or not, as opposed to the banner ad networks that work with app publishers, the offer platforms are the real gate keepers, and they can transform themselves much the way the old incentive guys have as the traffic and advertise base changes. And luckily for the platforms, they will have no shortage of mediums with which to play – social media, iPhone today, game consoles tomorrow. While intent may still always be questioned, their relevancy will not be.

About the author: Jay Weintraub works in and writes about the performance-marketing ad ecosystem. He runs the largest conference servicing the online customer acquisition industry – LeadsCon. His next event, LeadsCon East takes place August 17 – 18, 2009 in New York City at the Marriott Marquis Times Square. You can also find a collection of his writings on his personal blog, JayWeintraub.com.

Flash Games Summit
Yesterday I attended the first ever Flash Games Summit which has a bunch of informative sessions and interesting people involved. Thanks to Mochi Media for inviting me!

Monetization panel
I moderated a panel called “Monetization and Business Models for Flash Games” with:

• Adam Caplan from Super Rewards
• Kate Connally, AddictingGames
• Jameson Hsu, Mochi Media
• Kenny Rosenblatt, Arkadium Games

This was a nice mix of people because it represented a virtual goods-focused payments platform (Super Rewards), an ad network (Mochi Media), a content portal (AddictingGames), and a developer (Arkadium), so there was a variety of interesting viewpoints.

Panel topics
We covered a variety of topics, including:

• How do the different players (ad network, portal, etc.) make money?
• What are the biggest factors in driving monetization?
• What are the differences in monetizing demographics and geographical areas?
• What kinds of games are more successful at monetization?
• How are social gaming folks different than flash gaming creators?
• What are the key metrics they look at?
• How has the recession changes their business, and how will it affect the industry overall?
• .. and many more!

Hope you enjoy watching the discussion.

Here’s the video – enjoy!
(click here if you don’t see the embed)

Online TV Shows by Ustream

BFF means “best friends forever” for those of you who are wondering why there’s a monkey and banana at the top of this blog post

Examining the power of one-way friending AKA “follow”
When I first joined Twitter, I found the one-way following mechanic pretty weird – but now, it’s clear that it’s very powerful and provides a richness that you can’t get from two-way friend requests. Initially though, I was confused. After all, hadn’t all social networks standardized around two-way friend requests that both parties have to accept? Why try to fix it? It seemed like it’d just be confusing, and potentially freak some people out that they were being followed by random people they didn’t know.

This post examines the strengths of the one-way “follow” design, in particular, the ability for this paradigm to support 4-tiers of relationships rather than the simple 2-tiered model in the classic friends case.

First, let’s discuss the social groupings issue.

Social groupings and friend segmentation
At the same time as Twitter was just getting started, the rapid explosion of users on Facebook, MySpace, and other social networks raised a bunch of really core and important questions about these social applications. Among these issues:

• Will one social network rule them all?
• Or alternatively, will you use one social network for work, one for your personal life, and possibly others for other vertical interests?
• If it’ll be one, how will you group your work friends in one, and your personal friends in another?
• How will this work at a design level? How about at a technical level? (aka Data Portability?)

These are all great questions, and point out a number of potentially fundamental weaknesses to the all-in-one social networking model. If you look at many other communication channels, like phone, email, etc., you’ll often see people segment their identities. Their work voicemail will be boring, and their personal voicemail will be funny, and they’ll use different phone numbers for each.

Of course, the initial petri dish that social networks grow – high school and college students – don’t really have to deal with this. Their social groupings are more or less homogenous, because they only have personal friends. But after you’ve worked a couple places, moved around, and have your friends’ careers diverge into lawyers and slackers, then your social network becomes more complex and segmented.

The approach that many social networks have taken to solve this is to group people into networks and friend lists. Either through self-assignment or you assigning them, people go into different lists. Of course this hurdle is basically a type of boring security configuration that consumers have historically had trouble with.

Twitter’s “follow” model
The amazing thing about Twitter’s model of allowing one-way following is that it adds depth and a couple simple segmentations to your friend list, without needing to do any configuration beyond hitting a button.

With the one-way follow design, you have:

1. People who follow you, but you don’t follow back
2. People who don’t follow you, but you follow them
3. You both follow each other (Friends!)
4. Neither of you follow each other

Having these 4-tiers of relationships on Twitter is nice – combined with Protected Updates, it creates a nuanced set of definitions, executed with just one button: Follow.

The advantages are numerous: First, it’s easier to get started by opting into a number of feeds that pre-exist, and you can populate your timeline without anyone accepting your friend requests. Second, it makes it possible to have interactions with lots of people (@replies), but your timeline only has information you care about, as you don’t have to follow folks you’re not interested in. Third, some profiles are inherently appealing to a cross-section of users – these include celebrities, companies, media content, etc. – and it the one-way follow design supports all of these nicely whereas two-way friending makes things complex. 

Two-way friending with public profiles?
Compare the above to the traditional two-way friending case, supported by social networks

1. You’re friends
2. You’re not friends

So how do you deal with Sean Combs aka P. Diddy (aka @iamdiddy)? If you were to friend him (and he friends you back), all of a sudden, you are exposed to the random people (like you) who are interacting with him, which creates a lot of low-value information on your newsfeed.

As a result, it only makes sense to separate Diddy’s profile into two separate ones, a public and private profile, where the private is the “real” friends and the public one is everyone else. For MySpace, they opted to differentiate these public profiles as “Artist Profiles” whereas Facebook decided to call them “Pages.” I imagine that they treat information flowing in and out of these pages specially, so that they know not to public crazy amounts of information from random people, and they can segment those interactions out.

Note that MySpace was very early in having these celebrity profiles, which has led to the right of so-called MySpace celebs like Tila Tequila, Forbidden, etc. whereas I’m not aware of any Facebook celebs emerging ;-)

Maybe this two-way friends with public/private profiles works, but it’s much less elegant than a single “follow” button. In the dual profile version, you end up needing either lots of configuration (what photos to publish, which friends belong in which), or you end up with two distinct pieces of content. This would mean multiple photos, multiple profile content, and two places to do everything. Not attractive, in my opinion.

Conclusion
Ultimately, both approaches have their advantages – the two-way friending model is better at supporting strictly real-life relationships. That ability has obviously led MySpace and Facebook to conquer a lot of real estate and build eyeballs. At the same time, this model requires them to design around the complexity introduced by celebrities, brands, and companies, which are all important folks to have in your ecosystem for long-term monetization as well as mass appeal.

As always, leave a comment with your thoughts! See any other friending models that have advantages?

Want more?
If you liked this post, please subscribe or follow me on Twitter. You can also find more essays here.

Here are some links I’ve posted to my twitter account over the last week or two. You can follow me on Twitter if you like these! Many are work unrelated.

• Spot Runner Is Running On Fumes: Another 60 To Lose Their Jobs http://tinyurl.com/dj89n2
• rt @mkapor If you read anything about what’s happening to newspapers, read this essay by @cshirky (Clay Shirky) http://bit.ly/18tDhy
• NYT article, In Italy, a Vending Machine Even Makes the Pizza: http://tinyurl.com/cwuy5q
• Candy-Maker Tries To Ignore Kids Pretending To Smoke On YouTube http://tinyurl.com/casw5j
• Awesome Peter Funch Photo Series of Manhattan http://cli.gs/z67bZu
• Don’t launch http://tinyurl.com/bz5vop
• Struggling in-game ad firm IGA Worldwide seeks investments or possible sale http://tinyurl.com/c8ujol
• Yahoo Search Streak Snapped, Loses Share In Feb (Google Gains) http://tinyurl.com/b3v6dz
• demographic changes in myspace->FB movement: http://tinyurl.com/cb53or
• Introduction to the ad auction http://tinyurl.com/bmsjnb
• Pivots for change http://tinyurl.com/bu4tdr
• Sam Spenser’s Umbrella Bloom http://tinyurl.com/d95zbq
• i thought google thought behavioral targeting was evil? Now they just unveiled their new internet-wide cookie tracking: http://bit.ly/X5xWM
• slate article explaining icelandic elf detection: http://www.slate.com/id/2213353
• How Google Will Invade Your Privacy While ‘Protecting’ It http://tinyurl.com/d5nqwf
• graph of microlending vs venture capital: http://tinyurl.com/dza5r9
• Not Enough Work http://xkcd.com/554/
• can’t believe msft surface is a real product you can buy – i always thought it was a demo: http://tinyurl.com/brlalo
• Graham on Boston vs Silicon Valley, why YC left Boston (his opinion: Silicon Valley wins hands down) http://bit.ly/gAh6
• Some zone design lessons http://tinyurl.com/cjbuuc
• Cellphones vs Women (or Men) [Comic] http://tinyurl.com/bb5z32
• massive tetris flash game. Fun? http://sovietrussia.org/f/src/tetoris.swf
• 27 Huge Publishers Join To Replace The Bannerhttp://tinyurl.com/bpdv7r
• More Details & Stats on User Communication Patterns from Facebook Data Team http://tinyurl.com/bbvhnl
• 8 Ways the Changing Facebook Home Page Will Affect Application Virality http://tinyurl.com/dzzrkz
• Dilbert and The Smart Talk Trap: The Dangers of Skilled Bullshitting http://tinyurl.com/bt742t
• Branded Virtual Goods Boost Purchase Intent By 20% Or More On Social Networks http://tinyurl.com/boq7mo
• Unauthroized viewing of TV and movie content expected to grow http://tinyurl.com/aeet87
• The Last Days of the Oligarchs? http://tinyurl.com/cdluh5
• What is your game design style? http://tinyurl.com/br8sh7
• Facebook Extends Lead Over MySpace in US Traffic in February http://snipurl.com/d9qld
• Lessons Learned from imeem http://snipurl.com/d9w4s
• Opinion: Designing For Free Takes More Than ‘Just’ Game Design http://tinyurl.com/bqu6j6
• # of iphone apps in the appstore is accelerating!http://apple20.blogs.fortune.cnn.com/2009/03/05/apples-app-store-25000-apps-and-counting
• yahoo mail still 5X the size of Gmail: http://tinyurl.com/cezzco
• Don’t Throw Out the Subscription Model http://tinyurl.com/d92fah
• wikipedia article on the large % of icelandic people who believe in elves: http://en.wikipedia.org/wiki/Huldufólk
• nyt article on icelandic elves:http://www.nytimes.com/2005/07/13/international/europe/13elves.html
• Watch this! Great speaker, and he’s 10 years old: http://tinyurl.com/a9rdhm
• rt @adachen Wow, YouTube hits 100 million *US* viewers http://bit.ly/8CWQH
• Icelandic logic behind the meltdown http://tinyurl.com/aloche

New monetization option for app developers launches
Today, my friend Noah Kagan launched a new payments service called Gambit which you can check out here. The focus is on virtual currency-based Facebook/OpenSocial applications, and supports credit cards, mobile payments, and offers-based monetization. He also has a blog and twitter account.

Given the proliferation of these services, I wanted to spend a couple minutes talking through the new monetization funnel for apps and some of the metrics that are being thrown around.

Spreadsheet model
First off, I wanted to quickly share a very simple spreadsheet model which you can download here.

Funnel steps
At a high level, the key issues to track for an offers-driven monetization looks something like this:

• total installs / registered users
• monthly active uniques
• daily active uniques
• daily paypage uniques – how many users go page where you can pay or fill out offers?
• daily lead clickthroughs
• daily net lead completions (lead completions – chargebacks)
• daily revenue
• monthly revenue

From top to bottom, you can see that the focus is around uniques, and how many of them translate to completed offers and ultimately revenue. Of course, many of these transactions will actually end up as direct payment via credit card or mobile, and that is trivial to add to this model – I won’t cover those just for simplicity.

One quick note on leadgen though, for those who are unfamiliar – essentially, leads are opt-in forms that users can fill out in order to generate virtual currency. This might be subscribing to a Netflix offer, for example, or giving out a real address to get a direct mailing from a university. More about the leadgen industry here. As a result of this construct, users may not have to use credit cards in get money to the publisher, which can be great.

As a result of this offer-based monetization, it becomes important to track not only how many people click through to begin filling out a lead, but also how many folks complete leads, and ultimately how much revenue each lead is worth. Different demographics, geographical areas, and lead types generate different kinds of revenue. There’s also chargebacks that happen when the leads are rejected for being complete or incorrect.

Example numbers
If you plug this into a monetization table, then you can see the flow.

Here are some example numbers derived from games that Noah’s company Kickflip had previously developed and operated – he was comfortable sharing the data that came out of his own apps. The numbers below would represent a large and successful app with millions of actives per month:

and of course these numbers are driven by all the percentages of how much dropoff there is at each step. For quick reference, the percentages are listed below and drive the numbers in the table above.

Now that we have these metrics, we can start to calculate to other metrics.

Let’s define a new term, “ACPM” which stands for “App CPM”
As someone from the online ad industry, I was saddened to hear that the term “CPM” had been co-opted by these app monetization companies to mean something entirely different and weird.

In the app monetization world, this is the definition:

App CPM = (daily revenue / daily uniques to the paypage) * 1000

Recall that this is very different than the standard definition:

Online ad CPM = (daily revenue / daily ad impressions) * 1000

They are certainly not apples-to-apples, even though they are represented in some literature as such. And unfortunately, now that some of the market leaders are using these misguided terms, everyone is following suit. Yuck!

So as you can see, the “app CPM” (which I’ll refer as ACPM) is defined by uniques to the payments/offer page, rather than by pageviews or impressions. Using the above numbers, we’d get:

ACPM = ($5,670 / 45,000) * 1000 = $126

I’ve been told by multiple people that numbers from $50-$200 are all possible here.

Measuring ARPU
You’ll notice that the ACPM has no relation to the overall usage of the product – in fact, you might have $300-$400 app ACPMs but still have low revenue, since the ACPM is only defined once the users hit the payments page. Maybe you have a small number of users who do so, or maybe you only have a small % of users who are active at any given time.

To measure how the numbers fit together from top to bottom, instead we’ll have to calculate the ARPU:

ARPU = revenue / total actives

This means that this would include any and all actives, regardless of whether or not they visited the payments page. For the numbers above, they’d translate to:

Monthly ARPU = $172,935 / 3,000,000 = $0.058

On Facebook, I’ve been told from multiple sources that numbers from $0.01 to $0.25 are all reasonable, and that off of the social platforms you’ll find more niche destinations that generate closer to $1 ARPU.

Conclusion
Ultimately, it’s very exciting that multiple monetization platforms are getting created in the industry, and that competition will be great for everyone. Gambit is certainly one of many new creative companies that  will come out with great stuff.

At the same time, it’ll be up to publishers to figure out how to squeeze as much monetization they can from their properties, but without compromising the user experience. By looking at the numbers above, it’s clear how you can increase both ARPU and ACPM in very systematic ways, but it’s potentially at the cost of retention or engagement within the product.

Ideas or questions? Leave me a comment.

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UPDATE: thanks to Jared Fliesler for correcting a silly mistake in my arithmetic ;-)

Today we have a fantastic guest blog from Ranjith Kumaran, on his adventure going from an ad-supported free service to a subscription-based freemium model. Ranjith is the Co-Founder & CTO of YouSendIt.com, a Silicon Valley company that allows businesses and individuals send, receive and track digital content securely and easily. Enjoy! -Andrew

Free to Freemium: 5 Lessons Learned
by Ranjith Kumaran

Introduction
A tech reporter recently asked me if YouSendIt.com had made the switch from a free ad-based business model to a subscription-based freemium model “just in the nick of time”. After all, with death chasing every ad-revenue-fueled startup these days, surely we must have been scrambling over the last few months!

The truth is that we got our first paid subscriber at YouSendIt on the night of February 28th, 2006, over three years ago. The company recently passed the 100,000 paid subscriber mark but that first customer was where it all started: the transition from free to freemium. As startup pundits we expect business models to iterate but this particular switch was a thrill-a-minute ride.

So if you’re ready to take the plunge or are still on the fence between free vs. freemium then read on. I’ll highlight five key lessons learned over the last three years as we went from a 100% free model to freemium:

Lesson 1: It’s all about DNA
Lesson 2: Funnels come in all shapes and sizes
Lesson 3: Compound growth is a double-edged sword
Lesson 4: Don’t let pricing psyche you out
Lesson 5: “Boring” things can give you lots of conversion lift

It’s all about DNA
It doesn’t matter how smart your team is or how hard you work, everyone has to want to make the switch from free to freemium. The thesis of our first venture round of investment was to test both models to see how they scaled. But the reality was we already had a significant free business (advertising revenue helped my co-founders and I keep the lights on, sound familiar?) so the lion’s share of the first six months were spent building a team that could keep the viral, ad-impression-generating parts of the business growing. When the subscription service launched and showed great promise (we collected our first payment within 4 minutes of pushing the code live), the business model was changed but many of the team’s mindsets were not. Reconciling these differences was exhausting but we got there.

Do yourself a favor and pull the band-aid off quickly. If you re-channel all effort into improving conversion and building your brand your subscription business will get out of the blocks much faster. A change in DNA is the hardest thing a company can endure and some don’t; get through it early.

Funnels come in all shapes and sizes
Once you’ve made the switch a number of things will happen:

• People who don’t believe in paying for web-based services will call you a sell-out. Unsurprisingly, these folks aren’t in your target market. If you provide a valuable service the majority of your users will stay with you (most for free and some percentage will subscribe right away). YouSendIt.com’s traffic took a 30% haircut in traffic during this process. If we didn’t have anything further down the funnel this would have been devastating.
• Expect to see a drastic change in the mix of users you serve going forward. YouSendIt’s business is international (everyone sends files), including geographies that any startup will struggle to effectively monetize showing ads; in general the same geographies also yield weaker subscriber numbers. This pruning of who you serve and how much (by, say, asking for payment) is very, very, common and often more deliberate; it’s a cost-to-serve discussion every web business that thinks beyond customer acquisition will eventually have. Over time we found that the users who were willing to pay for our services attracted similar users to the site.
• Plan to change the metrics by which you measure your business. Our dashboard went from plotting CPMs, impressions and make-goods to conversion rates, churn, and ARPU. Acquisition cost, cost-to-serve, and lifetime value start to rear their ugly heads. If you want to fully understand your freemium business, learn to love them.

Compound growth is a double-edged sword
Once the freemium engine has run for a while you’ll see that, unlike fluctuations in ad-impressions and CPMs, subscription revenue is very predictable; your shareholders will appreciate this. Step functions in revenue are seen when new products are launched (including up-selling to the current base and convincing more users to subscribe) and new channels into the top of the funnel are created (making our way onto the desktop was a big one). Compounded subscriber growth is very powerful: convert more users in January and you’ll have a chunk of the year’s revenue in the bag, provided you’ve got churn under control; fall behind and revenue shortfall amplifies just as quickly over time.

Don’t let pricing psyche you out
Balancing market penetration and the fear of leaving money on the table is no fun and more than one startup has failed to even launch a paid product because of the pricing hurdle. Here’s a quick and dirty way to put a stake in the ground:

1. Make a list of your competitors or find adjacent markets / potential substitutes with similar users and use cases. You should already have this list.
2. Plot the spectrum of all the price-points of their offerings.
3. Plan to release at least two paid tiers: one at the bottom end of the spectrum that is driven by volume and one at the top that is clearly differentiated by value.

By doing this you can accomplish the following: fill in any market share vs. revenue maximization discussion rat-holes (now you can test both); give customers a way to compare between three offerings (free, a little more, and lot more; being able to compare is an important part of any purchase decision); feel good that you’ve done some diligence on pricing without prematurely shelling out a lot of cash on market research.

If the above exercise seems unscientific that’s because it is. Your pricing work has just begun: constantly observe the rates at which users move through the funnel at different price-points, use promotions to get buyers off the fence, and re-price as you get more price elasticity data. At YouSendIt we raised prices (yes, it can be done) successfully several times in the early days as we learned more and more about buying behavior.

“Boring” things can give you lots of conversion lift
Conversion lift doesn’t always come from groundbreaking changes in product, offers, or funnel analysis. These days I will look for ten 1% lifts in conversion before one 10% magic bullet; in reality there probably aren’t a lot of 10% lifts left after the first handful. Get into the groove of turning knobs a little at a time, learning, and iterating; you never did this further down the funnel when you were selling ads and you are likely out of practice. Other mundane things that you haven’t invested in start to get a lot of play: customer service SLAs, quality of service, and even the right terms of service are all areas which can drive conversion. Look for a 1% lift in conversion right now, it’s in there somewhere; then do it again a million times.

Conclusion
With any luck there are enough examples above to convince you that switching from free to a freemium business model can be done with a little perseverance and a lot of belief. I’ve experienced the rush of going from 0 loyal users, to thousands, hundreds of thousands, and millions a few times in my career. But there is a different kind of satisfaction you and your team will get when your business starts to amass paid subscribers: users who believe the things your company has worked so hard to create are good enough to pay for. This is the ultimate validation of your efforts.

These topics will be covered in more detail at a couple of panels (one on Understanding Freemium Business Models, another on Customer Acquisition in a Down Economy) I’m helping to organize in the coming weeks. If you’d like to participate as an attendee, panelist, or moderator or if you’re simply interested in hearing about more lessons learned (the hard way), please follow the Twitter feed I’ve set up.

Here are some links I’ve posted to my twitter account over the last week or two. You can follow me on Twitter if you like these! Many are work unrelated.

• TiVo Loses 125,000 Subscribers In January ‘09 Quarter http://tinyurl.com/c9gxvu
• Chinese Students Want To Know: How Do I Get Rich? – Forbes.com http://cli.gs/LXUBSp
• Crazy Numbers: $300 Billion in Annual Revenue from Pachinko & Pachislots in Jap.. http://tinyurl.com/c52zvu
• Why Amazon Is Bucking the Trend http://tinyurl.com/dzcf7y
• Facebook’s Thiel Explains Failed Twitter Takeover http://tinyurl.com/cp6d64
• YouTube TV News Upload And Viewing Numbers, Week ending February 27 http://tinyurl.com/asug59
• Today’s Threat To Broadcast TV Networks http://tinyurl.com/aqlck9
• Berkshire: Worst Year Ever, plus Credit Default Swaps http://tinyurl.com/ddf8c5
• Max Levchin Is Bored With Silicon Valley Startups http://tinyurl.com/d735ez
• The ugly truth about your favorite social networks http://tinyurl.com/bgtdda
• As first-big-domino Rocky Mountain News folds, Google begins to place ads on Google News. http://bit.ly/S37ew
• THIS IS SO COOL — Oregon Trail ONLINE: http://www.virtualapple.org/oregontraildisk.html
• Golden Parachute http://tinyurl.com/b48ry9
• Surprise! Yahoo Outperforming Google http://tinyurl.com/b9onk5
• The Shakeout Begins! Video Startups in Play http://tinyurl.com/d78zyd
• termsheets still getting done, or at least VCs still talking about them on twitter ;-) http://tinyurl.com/dk97bw
• challenges/opportunities in brand online ad market (via @joshk) http://tinyurl.com/aa2z2z
• Apartment Buyers Abandoning 6-Figure Deposits http://tinyurl.com/dyyamd
• what happened to avg time per visit on MySpace in July? This graph looks like it’s bad data to me: http://bit.ly/4My6o
• Launch: Pitching Hacks, The Book http://tinyurl.com/by9bm6
• Building Quake Live: Carmack Speaks http://tinyurl.com/br6h4r
• Moral Hazard http://tinyurl.com/aoesd3
• What do fourth generation ad networks look like? http://tinyurl.com/ahh9of
• Bailout Hearings http://tinyurl.com/bmp4wu
• State of the Computer Book Market 2008, part 4 — The Languages http://tinyurl.com/dauyzk
• Eliminating Channel Conflict Between Publishers, Ad Networks http://bit.ly/D3Ra7
• Reboot: How to Reinvent a Technology Startup http://tinyurl.com/aodcbm
• How IMVU learned its way to $10M a year http://tinyurl.com/aqjnb3
• 5 Reasons Chernin’s Exit Puts Hulu In Danger http://tinyurl.com/alwbnr
• Leonardo di Vinci was a big time procrastinator: http://tinyurl.com/dmj74r
• Pic of Vegas, 1954: http://tinyurl.com/cj8579
• Online Video Viewing Up, Impact on TV “Negligible” http://tinyurl.com/c3naft
• Exploring a ‘Deep Web’ That Google Can’t Grasp http://tinyurl.com/dgd2fy
• Why the Click Is the Wrong Metric for Online Ads http://bit.ly/Gm0SZ
• Foxtrot comic, Jason figures how to make money in a down economy: http://bit.ly/11iwtm
• 4Chan.org: 300m page views/mo; about $6k in revenue http://tinyurl.com/czeppu
• Education Fail http://tinyurl.com/cb32u2

Lots of Bay Area startup chatter on Twitter
As I’ve written about many times, I’m of the often-repeated notion that the SF Bay area is a one-of-a-kind place for entrepreneurs. There’s lots of advantages, but one great one is the constant casual chatter of other entrepreneurs and CEOs here.

I quickly combed my tweets for today and made a list of active users of Twitter who also happen to be founder/CEOs in the companies around here. Almost all of them are running VC-backed startups, and they are among the best folks to learn from. I’m purposely leaving out obvious choices like @ev and conference types that get enough visibility as it is ;-)

The quick list of folks you might follow on Twitter
Here we go:

If you’re not on the list, then I didn’t see you use Twitter in the last day ;-)

And last but not least, you can follow me on Twitter here.

Am I missing anyone? Just comment if you have recommendations.

Above: Hollywood sequels follow from risk-averse design decisions, like the widely panned Godfather Part 3

The dangers of the metrics-driven design process
Many readers of this blog are expert practitioners of metrics-driven product development, and with this audience in mind, my post today is on the dangers of going overboard with analytics.

I think that this is an important topic because the metrics-driven philosophy has come to dominate the Facebook/OpenSocial ecosystem, with negative consequences. App developers have pursued short-term goals and easy money – leading to many copycat and uninspired products.

At the same time, it’s clear that A/B testing and metrics culture serves only to generate more data-points, and what you do with that data is up to you. Smart decisions made by entrepreneurs must still be employed to reach successful outcomes. (Thus, my answer to the title question is that no, A/B testing does NOT lead to crappy products, but poor decision-making around data can absolutely lead to it)

So let’s talk about the dangers of being overly metrics-driven – here are a couple of the key issues that can come up:

1. Risk-averse design
2. Lack of cohesion
3. Quitting too early
4. Customer hitchhiking
5. Metrics doesn’t replace strategy 

Let’s dive in deeper…

#1 Risk-averse design
The first big issue is that when you design for metrics, it’s easy to become risk-averse. Why try to create an innovative interaction when something proven like status/blogging/friends/profiles/forums/mafia/etc already exists? By copying something, you’re more likely to quickly converge to a mediocre outcome quickly, rather than spending a ton of effort potentially creating something bad – but of course this also eliminates amazing, ecstatic design outcomes as well. 

This risk-averse product design can lead to watered down experiences that combine a mish-mash of features that your audience has already seen elsewhere, and done better too. So while it’s an efficient use of effort, it’s unlikely that your experience will ever be a great one. It’s a recipe for mediocrity. 

Risk aversion is responsible for a whole bunch of bad product decisions outside of the Internet industry as well: Why do Hollywood sequels get made, even though they are usually much worse than the original? Why do companies continually do “brand extensions” that dilute the value of their brand position? The reason is that it’s an efficient thing to do, and it’s pretty easy to make some money even if the end product is not that great. But it’ll hurt in the long run, since these products will inherently be mediocre rather than great.

In my opinion, the only way to avoid this is to never get lazy about design, and to always take the time to create innovative product experiences. Of course you’ll always have parts of your product which will borrow from the tried-and-true, yet I think it’s always important that the core of the experience is differentiated and compelling.

#2 Lack of cohesion
As hinted above, the next issue is that A/B tested designs often create severe inconsistency within an experience. The bottoms-up design process that results from lots of split testing is likely to come up with many local effects, which may rule global design principles.

Here’s a thought experiment to demonstrate this: Let’s say you tested every form input on your website, with different labels, fonts, sizes, buttons, etc. You’re likely, if you picked the best-performing candidate, to have wildly different looking forms across the site. While it may perform better, it also makes the experience inconsistent and confusing.

Ultimately, I think resolving this has to do with striking a balance between global design principles and local effects. One great way to do this is to split out the extremely critical parts of your product funnel to be locally optimized, and keep the rest of the experience the same. For a social gaming site, the viral loop and the transaction funnel should be optimized separately, whereas the core of the game experience should be very internally consistent.

#3 Quitting too early
Another way to get to uninspired products is to quit too early while iterating an experience because of early test data. When metrics are easy to collect on a new product feature, it’s often very tempting to launch a very rough feature and use the initial metrics to judge the success of the overall project. And unfortunately, when the numbers are negative, there can be a huge urge to quit early – this is a very human reaction to wanting to not waste a bunch of time on something that’s perceived to fail.

Sometimes a product requires features A, B, and C to work right, and if you’ve only done A, it’s hard to figure out how the entire experience will work out. Maybe the overall data is negative? Or maybe it inspire dynamics that go away once all the features are bundled? Interim data is often just that – interim. But people are great at extrapolating data, but sometimes the right approach is just to play out your hand, see where things go, and evaluate once the entire design process has completed.

#4 Customer hitchhiking
A colleague of mine once used the term “customer hitchhiking” to describe how it’s easy to follow the customer on whatever they want to do, rather than having an internal vision of where YOU want to go. This can happen whenever the data overrules internal discussion and resists interpretation, because it’s so uncompromising as hard evidence. The important thing to remember, of course, is that the analysis is only as good as the analyst, and it’s up to the entrepreneur to put the data into the context of strategy, design, team, and all the other perspectives that occur within a business.

Today, I think you see a lot of this customer hitchhiking whenever companies string together a bunch of unrelated features just to please a target audience. This reminds me of what is often called the “portal strategy” of the late-90s. Just combine a bunch of stuff in one place, and off you go. The danger of that, of course, is that that it leads to incoherent user experience, company direction, and numerous other sources of confusion.

In the Facebook/OpenSocial ecosystem, of course, this manifests itself as companies that have many unrelated apps. You can dress this up as a “portfolio” or a “platform” but at the same time, it can be a recipe for crappy product experiences.

#5 Metrics doesn’t replace strategy
What do you think it would be like to write a novel, one sentence at a time, without thinking about the broader plot? I’m sure it’d be a terrible novel, and similarly, I bet that testing one feature at a time is likely to lead to a crappy product.

Ultimately, every startup needs to decide what they want to do when they grow up – this is a combination of entrepreneurial judgement, instinct, and strategy.

Every startup has to figure out how big the market is, they have to deliver a compelling product, and they need a powerful marketing strategy to get their services in front of millions of people. Without a long-term vision of how these things will happen, an excessive amount of A/B testing will surely lead to a tiny business.

To use a mountaineering analogy: Metrics can be very helpful in helping you scale the mountain once you’re on top of the right one – but how do you figure out whether you’re scaling the right peak? Analytics are unlikely to help you there.

Conclusions
My point on this – nothing is ever a silver-bullet, and as much as I am an evangelist for metrics-driven approaches to startup building, I’m also very aware of the shortcomings. In general, these tools are great for optimizing specific, local outcomes, but they need to be combined with a larger framework to reach big successes.

Ultimately, quantitative metrics are just another piece of data that can be used to guide decision-making for product design – you have to combine this with all the other bits of information to get it right.

Agree or disagree? Have more examples? Leave me a comment! 

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The Silicon Valley machine is still going, for now

Here in Palo Alto, the Silicon Valley machine is still going strong – entrepreneurs are still starting companies, angels and VCs are still investing, and engineers are still coding. In the last 3 months, I’ve had half a dozen friends get their companies financed, which is great. Certainly things are more difficult, but deals are still happening, and there’s still a lot of companies growing.

But I’ll say that I’m still quote worried, because of my belief that the worst has yet to come. There is a large group of 2004-2007 self-described Web 2.0 companies which haven’t hit bottom yet, and I’d like to discuss this possibility in this post. I hope this blog will spawn off useful discussions for entrepreneurs thinking about where we are in the boom-bust cycle.

So first, some thoughts about Web 2.0 and how that category has played out:

Web 2.0 isn’t cool anymore
In the 2004-2007 era, many companies in the “Web 2.0” space received a tremendous amount of funding. You can debate what the term means, but generally I would classify them as companies having some of the following qualities:

• Consumer internet destinations (or widgets!)
• User generated content and activities
• Advertising-based revenue models
• Appealed strongly to the early adopter audience

Yet ultimately, it turned out that most of these startups didn’t work out as real businesses. The reasons hinged primarily on the difficulties of monetizing user-generated content based on ads that I’ve written about. As a result, to be a VC-backable business, you either need to be a top 50 internet property (good luck on that!) or have a well-defined monetization backend that probably wasn’t advertising.

My guess is that the # of companies describing themselves as Web 2.0 has dramatically decreased over the last year, as these business model problems have been rapidly discovered and popularized.

And yet, now the difficulty of course, is that there are dozens of Web 2.0 startups funded in the 2004-2007 timeframe that have a meaningful amount of cost, and not enough revenue. It’s these startups that I’m worried about.

Venture financings as a lagging indicator for the economy
The problem is, VC financings tend to be a lagging indicator for the economy. We haven’t seen the established startups who are trying to raise Series B or C rounds get turned down by the market. The reason is that it’s too early, and these companies failures will lag the downturn in the economy by a year or possibly more.

Lots of smart companies and entrepreneurs did a great job of getting their financings done last year before the economy really fell apart. As a result, these lucky ones have cash in the bank right now and can continue iterating on their model to hopefully figure things out. But if they haven’t figured things out and the economy is still bad, then ruh-roh, that’s no good. But we’re unlikely to see the effects of these sick companies with dysfunctional business models until later this year.

Who are these startups that might be in trouble? Let’s discuss:

Characteristics of startups in danger
I’m not going to call anyone out ;-) But I think that there are several startups out there which are now in the precarious position of either finding their model ASAP, or collapsing.

These companies might include the following characteristics:

• Started in 2004-2007, and self-described as Web 2.0 startups
• Have grown to lots of headcount, let’s say >40 people, which can burn through a $5M Series A in under a year
• Substantial traffic, let’s say >5 million uniques per month, which drives up the cost structure
• Ad-based business models, which rely on big sales teams calling up agencies (whose pockets are now reduced, if not closed)
• Low-context advertising inventory, with low CPM in sectors like communication and entertainment
• Mature internet sectors, where the upside is now established, and acquirers are less likely to pay up as a result
• Not a leader in their category, where they may be #5 or higher, and investors may be unlikely to keep supporting their growth
• Media content hosting, where they allow users to upload, host, and stream content without charging a dime, which also drives down the cost structure

I will leave it as an exercise to the reader to pick out companies that might fit the bill.

The point is, I think this cycle is going to get a lot worse, and a downslide will likely be caused by one or a number of 2004-2007 vintage classically Web 2.0 companies hitting the skids. I am hoping that the slope down will be gentle.

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I wanted to pass this along – I will be moderating a panel at the conference, agenda and conference details below.

Click here for a 10% off of registration for readers of this blog.

More detail:

Conference description

Flash Gaming Summit is a one day conference dedicated to fostering the growth and success of the Flash games community. The conference will bring together leaders in the Flash game space to share industry insights and strategies for monetization, distribution and successful game development. The Mochis, an Flash games award show, will take place at the conference to recognize the best games of 2008.  Flash Gaming Summit sponsors include Adobe, Kongregate and Nonoba and is organized by Mochi Media.

Agenda

Time	Session
8:45	Doors Open – Registration & Breakfast
9:45	Opening Keynote
10:00	Session 1 – Designing and Building Successful Multiplayer Games    Flash game developers are increasingly building more immersive and engaging multiplayer experiences for their users. What makes a multiplayer game successful? Our panel will share their experiences and best practices on designing successful multiplayer games that engage gamers and keep them coming back. Moderator: Ranah Edelin, Raptr Chris Benjaminsen, Nonoba Daniel James, Three Rings Jim Greer, Kongregate Paul Preece, Casual Collective
11:00	Session 2 – Getting Eyeballs – Marketing and Distributing Flash Games    How important is it (or not) to get distributed on game portals? Where are the game plays coming from? Hear from a panel of portals and game developers on the best strategies and tactics to market, distribute and publicize your game to reach gamers. Moderator: Jeremy Liew, Lightspeed Venture Partners Chris Hughes, FlashGameLicense.com John Cooney, Armor Games Richard Fields, MindJolt Games Matt Spall, Gimme5games
12:00	Lunch
1:30	Session 3 – The Future of Flash    Hear from Adobe about the latest developments in Flash 10, CS4, AIR, Flex, Catalyst and more. Adobe Evangelist Ryan Stewart will discuss and answer questions about how these improvements will impact the game development community. Ryan Stewart, Adobe
2:30	Session 4 – Monetization and Business Models for Flash Games    There are multiple models emerging in the industry for monetizing games. How should developers choose which business models to pursue? Our expert panel will share their experiences and thoughts on various business models and their potential. Moderator: Andrew Chen, Futuristic Play Adam Caplan, Super Rewards Kate Connally, AddictingGames Jameson Hsu, Mochi Media Kenny Rosenblatt, Arkadium Games
3:30	The Mochis Flash Game Awards    Recognize the best Flash games of 2008 with The Mochis Award Show!
4:30	Session 5 – What Makes a Flash Game a Hit?    Game developers share insights on how to design and build hit, popular games that appeal to a mass audience. Panelists will discuss topics including game design, game testing/tuning, the effectiveness of creating sequels, and success metrics on succeeding in games. Moderator: Brian Robbins, Fuel Industries Edmund McMillen, creator of Coil (IGF Finalist) Joel Breton, AddictingGames Sean Cooper, SeanTCooper.com Stephen Harris, Ninja Kiwi
5:30 – 6:00	Session 6 – Social Game Design 101: How To Make Flash Games That Social Networkers Want to Play    What are the key elements to designing successful Flash games for reaching gamers on social networks? This talk includes a deep dive into game design and viral mechanisms that you must use to succeed on social networks such as Facebook and MySpace. Bret Terrill, Zynga Games
6:00	Join us for drinks at the Official Flash Gaming Summit After Party: License to Play

 

The Snowball by Alice Schroeder

I <3 Warren Buffett
One of the recent books I read during my blogging vacation was The Snowball, a comprehensive biography of Warren Buffett. I’ve always enjoyed reading the shareholder letters, and have read other books about him, but at nearly 1,000 pages, this book is particularly detailed and goes into a lot of unique stories.

In many ways, Buffett’s world is diametrically opposed to the startup world. He specializes in boring industries, doesn’t worry much about products, and has extremely long timeframes. Yet I took a lot out of reading about his experiences, and thought I’d share some thoughts about the startup world:

1. Enduring businesses take a long time to build
2. Who cares what other people think? Boring businesses can win big 
3. Access to money can be a huge competitive advantage
4. Success begets success

Let’s talk through a couple of these ideas:

1. Enduring businesses take a long time to build
I live in the heart of Silicon Valley, in Palo Alto, and there is an inescapable pull of “get rich quick” in the culture here. Every time there is a new trend, I quickly get calls and emails from people telling me about a new “gold rush” forming, and how I don’t want to miss out. In many ways, getting visibility on these trends is why I’m here, yet at the same time, it demonstrates a real breakdown in the business culture of Silicon Valley to be focused on quick flips.

Right now, the current “gold rush” trend is probably around mobile and social gaming, particularly the amount of revenue that small developer teams are generating on Mob Wars clones. Or iPhone apps. And before that, there was a huge gold rush around Facebook apps, and before that, Web 2.0 anything. The real pinnacle of this is an exit like YouTube, which generated over a billion dollars before it became profitable, and in less than 2 years.

Yet at the same time that this froth exists, there’s an undeniable fact that enduring, profitable, standalone businesses have still taken 5-8 years to build. Yes, it’s very cheap to get started and run some experiments, but to scale into a huge business, it takes real time and capital. Take a look at Facebook, for example, which clearly still has many years on it before it cracks the code on its business model and scales into something huge. Even Google took from 1996/7 to 2004 to get big – can you really do it faster?

I think the Warren Buffett view of the world is in a decade-or-more type timeframes, not in months. It’d probably be worthwhile for everyone to think about their startups and their eventual businesses that way, rather than just trying to get rich as soon as possible.

2. Who cares what other people think? Boring businesses can win big 
The other striking thing about the Buffett model of investing is that he puts money into a ton of companies which ultimately sell pretty boring things – bricks, chocolates, jewelry, etc. These are evergreen industries that will be around 5 years from now or 50 years from now.

From my personal observation, the startup world doesn’t think like this, and the focus is on the hot new thing. There’s always a lot of excitement about technologies or products, regardless of whether or not they address mainstream consumer needs, and thus a lot of market risk gets injected into every company. From the last couple years, trends like podcasting or RSS or P2P/BitTorrent got white hot, but ultimately didn’t go anywhere. Right now these hot trends might be new platforms (like mobile, SNs, etc.) or data portability, or status messages, or lots of other candidates. Maybe these will all go somewhere, but maybe they won’t.

And woe to the startup which is not working on a sexy problem, but instead working on something boring ;-)

I think there’s a lot of great opportunities in consumer internet that are now considered boring or forgotten. For example, doing things like domaining, ad arbitrage, forum sites, SEO sites, etc. Or similarly, there are now a number of proven consumer interactions, like video sites and toolbar companies, which are not likely to ever get funded now. All of these boring sectors, given the right twist or the right team, might actually lead to big successes, since a significant portion of the model has been proven out, but not as many smart people are working on them. Certainly you won’t have Techcrunch and the geek press banging on your door, but for some, that’s part of the appeal.

Instead of asking “what are the hot areas right now??” instead, the question to ask might be, “what are the overlooked areas right now?” 

3. Access to money can be a huge competitive advantage
One of big competitive advantages of Warren Buffett’s investment model is that he discovered that he could take money out of his cash-rich businesses, particularly in the insurance world, and then use that to invest in more cash-rich companies. This allows him to have a lot more flexibility in reacting to the market, and only jump in when he thinks companies are cheap. (Interestingly enough, the first chapter of the book starts out with everyone writing him off in the late 90s for not investing in tech companies, saying that he’s lost his touch, etc.)

For startups, my interpretation is pretty simple – ultimately, startups get money via two methods:

• Customers give them money
• Or, investors/bankers/VCs give them money

And these two methods are constantly in flux, depending on whether it’s more fashionable to sell stories to investors or whether the focus is more on revenue. And especially right now, the latter holds true. Having access to this capital is huge, for obvious reasons – there’s been a ton of discussion about why it’s a great time to start a company, because it’s easier to hire, office space is cheaper, etc. so I won’t go and repeat all of this.

The important part, I think, is to gear your company into to maximize for bringing cash into the door – you need to make sure that you’re either in a super hot space where investors are willing to throw money at you, and ideally also you’re making a ton of money. Oftentimes it seems like companies end up going either in one direction or the other – either they are going big, growing traffic, and raising money at crazy valuations, or they are boring and make money slowly but surely ;-) Not sure why it seems so mutually exclusive, but either way, have a strategy ;-)

4. Success begets success
Last thought on something that occurred to me while reading The Snowball – in the early years, you could really see that Warren Buffett succeeded just by pure skill alone. He invested in undervalued companies, and made money on them over time. But as his fame grew, it also became clear that there was a secondary impact from him investing – not only did his capital help, but the brand of Warren Buffett investing was enough to drive up the value of his shares. Pretty nice.

In the Silicon Valley startup world, I think it’s unavoidable that many of the successes here also hinge on luck as much as skill. But there’s also some secondary effects of having the right people involved, whether it’s founders or investors or other – you end up seeing that the social proof of having successful people involved has a self-perpetuating angle to it. Success begets success.

No wonder that the best venture capital firms experience serial persistence – the best ones stay at the top, year after year, even though this is typically not the pattern in other investment categories. As an entrepreneur, this tells me that it’s important to get the best people involved with your company, at all levels of the business, from investors to employees – and the more you are kicking ass, the easier it gets.

Your thoughts?
If you guys have thoughts on the above and/or on Warren Buffet or the book, please leave me a comment! Thanks. 

Want more?
If you liked this post, please subscribe or follow me on Twitter. 

Blogging vacation was fun
After a good couple months not blogging, I’m going to try to get back in the groove of things ;-) It’ll be hard since the format of writing long essays tends to suck up a lot of time, but I’m going to try to get back to writing something substantive 1-2 times a week. Ideas and suggestions for topics are welcome! Please comments or write me an email

Twitter was a “good enough” replacement for blogging
Of course, even while I was off on blogging vacation, I was still tweeting away on links and other short comments. It was a great way to still publish content but without the constraints of doing something serious in the long-form blog format. If you haven’t followed me on Twitter, please click here to do so! And be sure to email me at voodoo [at] gmail and let me know what you’re working on these days.

Twitter links (lots of them)
I haven’t posted a link dump from Twitter lately, so I’ll do that now. Enjoy!

If you want to stay current, follow me at Twitter here.

• For Obama Girls, There Is No Avoiding Chores http://tinyurl.com/bnbjsx 
• Oscar Halo: Academy Awards and the Matthew Effect http://tinyurl.com/aw7m3z 
• Founder Dilution – How Much Is “Normal”? http://tinyurl.com/basumn 
• Work in small batches http://tinyurl.com/aoflly
• one of my favorite new blogs – TV by the numbers. Lots of data and analysis for old school teevee: http://tvbythenumbers.com
• Japan: No Longer a Miracle http://tinyurl.com/bqplts 
• Allocating Harvard’s Resources http://tinyurl.com/dlbc5e 
• Actively Avoid Insights: 4 Useful KPI Measurement Techniques http://tinyurl.com/cq44bj 
• Jeff Dean keynote at WSDM 2009 http://tinyurl.com/awxbf8 
• Google May Dump MySpace Search Deal http://tinyurl.com/cqv9r3 
• Washington Post profile of 4chan’s moot http://tinyurl.com/aq9qzj 
• Research shows MMOG players mostly play with ppl they know: http://bit.ly/U8gm 
• Lively Languished With 10k Users In Week Before Cancellation http://tinyurl.com/bx3n7x 
• Stop Looking for a WoW Killer http://tinyurl.com/a9p9sr 
• NYT says, don’t blow your nose when you have a cold: http://tinyurl.com/asjrhf 
• michael lewis on stats-driven approaches to basketball: http://tinyurl.com/cagke4 
• Why eBay Should Consider Breaking Itself Up http://tinyurl.com/bepvha 
• The Death Of “Web 2.0″ http://tinyurl.com/cm5vgm
• MySpace Ad Revenues Closing In On AOL’s http://tinyurl.com/cjch88 
• How To Break Up In 64 Easy Steps http://tinyurl.com/czn29c 
• Slate.com on graphs and discussion for Facebook’s 25 random things: http://tinyurl.com/bqz3qx 
• pew internet report on twitter and status updating: http://tinyurl.com/cmlvjb 
• has facebook reached saturation in specific markets? lots of numbers: http://tinyurl.com/ba3y4n 
• rock it out to music composed only of sounds from windows XP and 98: http://tinyurl.com/2m9fwb 
• From wikipedia: A group of crows is called a “murder” http://en.wikipedia.org/wiki/Crow
• another guilty pleasure blog to read… dating a banker anonymous: http://dabagirls.wordpress.com
• kittens riding a roombah! http://tinyurl.com/56v872 
• amazing visualization of openings of Target over the last 50+ years: http://projects.flowingdata.com/target
• pretty great. And totally random. Hamburger bed!! http://tinyurl.com/ckuvkj 
• more on freemium from eric ries of kleiner perkins/IMVU: http://tinyurl.com/aennvv 
• going from freemium to ‘mium: http://tinyurl.com/c4hw5z 
• all good things must come to an end, even search marketing growth: http://bit.ly/5oJs 

This was so good I had to repost it (via pinchmedia).

Here’s some of the great graphs included in the presentation:

• Downloads per unit time versus price
• # of downloads needed to hit Top 25 and Top 100 list
• Retention curves for free apps
• Retention curves for paid apps
• Retention curves per app category
• Engagement for free vs paid and by category
• Advertising revenue tradeoffs for free versus paid
• Cumulative application runs since first use, by decile

Slides below:

How to get the spreadsheet
First, here’s the spreadsheet:
Click to download Freemium spreadsheet

You can open it in Excel and fiddle around the numbers. The rest of this essay is a discussion on this!

Background on this discussion
Last year, the stupendous Daniel James co-hosted a talk with me on Lifetime Value metrics for subscription and virtual goods-based items. You can see the video/outline for the talk, Daniel’s commentary, and a mindmap of the talk (scroll to the bottom of the post).

As part of the talk, we worked on a spreadsheet model for freemium businesses that we didn’t get enough time to work on – so I’m going to cover it in this post! If you haven’t gotten the spreadsheet yet, here’s another link to it.

Here are the questions this post (and the spreadsheet) is meant to answer:

• What are the key factors that drive freemium profitability?
• How do freemium businesses acquire customers?
• What are the drivers of customer lifetime value?
• How do all these variables interact?

If these questions interest you, keep reading :-)

Article summary (for people with attention deficit!)
To become profitable using a freemium business model, this simple equation must hold true:

Lifetime value > Cost per acquisition + Cost of service (paying & free)

Said in plain english, the lifetime value of your paying customers needs to be greater than the cost it took to acquire them, plus, the cost servicing all users (free or paying).

There are lots of different factors that influence profitability, including:

• Cost per acquisition
  • Efficiency of media (traffic sources, CTR, impressions)
  • Signup funnel conversion %
  • Average viral invites sent out
• Lifetime value
  • Retention metrics
  • Revenue mix

By understanding these subcomponents, you can tweak your model and figure out what metrics need to be hit in order to reach profitability.

Now for all the gory details… 

User acquisition
The first tab in the spreadsheet covers the issue of paid user acquisition – many subscription businesses mostly rely on AdWords and ad network buys in order to acquire users. For freemium businesses, particularly ones that are social apps, there’s often a word of mouth or viral component, which we’ll cover in a second.

I’ve written extensively on paid user acquisition in the past, particularly the blog post: How to calculate cost-per-acquisition for startups relying on freemium, subscription, or virtual items biz models.

At a high level, here are some of the things you’ll want to track:

• How are you paying for traffic? (CPM/CPA/CPC)
• What do the intermediate metrics look like? (impressions/CTR/etc)
• How does your signup funnel perform?
• How much are you spending for the users you end up registering?

Basically, you end up with a media buying matrix that looks something like this:

and these are some factors worth thinking about, in terms of increasing or decreasing the cost per acquisition (CPA):

As previously mentioned, lots more detail here.

Funnel
Once you get your users registered onto the site, then there’s the question of how convert to paying customers, and whether there are any viral effects. The model covered in the spreadsheet has a separate tab, called “Funnel” which covers these issues.

At a high level, there’s what is happening:

• Each time period, a bunch of newly registered users come in (both acquired through ads or through viral marketing)
• Some % of these users convert into paying users
• Some % of these users then send off viral invites
• Revenue is generated by building up a base of paying users
• Cost is generated through building up a base of active users (paying or not!)

To me, this tab captures the “art” side of building a freemium business. Persuading peopleto pay for your service and invite their friends requires creativity, product design, and lots of metrics. Josh Kopelman of First Round Capital had a great tweet recently on this topic where he says:

@joshk: Too many freemium models have too much free and not enough mium

As Josh notes, the key is to create the right mix of features to segment out the people who are willing to pay, but without alienating the users who make up your free audience. Do it right, and your conversion rates might be as high as 20%. Do it wrong, and your LTV gets very close to zero. This is why premium features have to be built into the core of a freemium business, rather than added in at the end. You want to be right at the balance between free and ‘mium!

Just remember that during the time period that it takes you to figure out your funnel, viral loop, and everything else, all the free users you’re building up create cost in your system.

Businesses that aren’t eyeball businesses shouldn’t act like eyeball businesses :-)

Anyway, the product design issue (and resultant conversion rates) are a a deep topic, and here are some other related posts (by others and myself):

• Thoughts on free powered business models (Charles Hudson)
• Casual MMOs get between 10-25% of users to pay (Nabeel Hyatt)
• Successful MMOGs can see $1-$2 in monthly ARPU (Jeremy Liew)
• Bridging your traffic engine with your revenue engine
• What’s your viral loop? Understanding your engine of adoption

User retention
Of course, it’s not enough to just acquire paying users, you need to retain them. If you have a super high churn rate, then at best you’ll be stuck at a revenue treadmill (doing lots of work but flat revenue and no profitability). At worse, it’s easy to lose a ton of money, if the CPA exceeds the LTV. I wrote about this topic earlier in my essay When and why do Facebook apps jump the shark (which also has a spreadsheet).

How sensitive are retention numbers on lifetime value? Here’s a quick thought experiment: Lifetime value is the sum of the revenue that a user might generate from their first time period to when they quit the service. Think of it as an infinite sum that looks like:

where rev is the revenue that a user produces during a time period, and R is the retention rate between time periods.

You can simplify this, based on the magic of infinite series:

So let’s say that you make $1 per time period, and you have 1000 paying users. Let’s compare the difference between a 50% retention rate and a 75% retention rate:

This means that in this case, by increasing your retention rate by half (relatively speaking), you actually DOUBLE your revenue. And even more when you reach “killer app” status and attain retention rates around 90%. This is a big lever.

Note that retention rates are generally not fixed numbers – they usually get better the longer a cohort of users stays with you! I’m using a fixed retention number to set a lower bound, and for mathematical simplicity.

OK, so the biggest factors affecting retention boil down to three things:

• Product design
• Notifications (optimize them, of course)
• In success cases, saturation effects

For more reading on product design, I’d recommend Designing Interactions from IDEO. For notifications, there’s been a lot of great work in the database and catalog marketing world, for example Strategic Database Marketing. Tesco, Harrah’s, and Amazon are all companies well-known for their strategic use of personalization and customer interaction. For saturation effects, as previously mentioned, my old-ish article When and why do Facebook apps jump the shark.

Cashflow (and ad-reinvestment)
The tab “cashflow” in the spreadsheet captures a couple different issues:

• Paid user acquisition is usually an upfront expense, whereas the revenue comes in over time
• Your revenue per paying user depends on a mix of revenue sources
• You pay a “cost of service” across all users, whether they are paying or not – be careful that this cost of service is not too high!!

Some more detail on the above:

In a model with paid user acquisition, it takes time to break even. You pay for a user upfront, but then the revenue stream trickles in over several time periods. As a result, you tend to be cashflow negative for some number of time periods, and which then goes positive later. This effect is compounded further if your model specifically depends on viral acquisition, because you don’t get significant users in virally until your userbase becomes large.

This is why you get a graph like this, where you’re unprofitable for a while, then break even:

Note that it’s also VERY possible that they never cross, and the entire business is unprofitable. Just play around with the numbers in the spreadsheet and you can see how easy it is to happen!

In terms of average revenue per paying customer, what you typically find is that your customer base is made up of multiple segments. You can price them differently through different tiers of subscription (Free versus Pro versus Business) or with Pay-as-you-go or with many other models.

Ultimately you can roll this all up into a single number, which is referred to in the spreadsheet as revenue per paying customer. You can also divide the revenue by the number of total users (paying or not) in order to get the average revenue per user (ARPU).

As for the cost of service, your mileage will vary. The main thing is, try not to do anything too expensive for free users! After all, given that typical conversion rates are <10%, and subscription services are typically <$20/month, the following thought experiment is insightful:

Plus then you have to factor in the acquisition cost! (Probably a couple bucks per user, so thousands of bucks per 1000 users).

Lifetime value
And finally, the last tab on the spreadsheet calculates lifetime value. Basically you figure out the number of payments that a paying user will generate over their lifetime, referred to in the model as “user periods.” (I arbitrarily took this out to 20 time periods, but you can do something different) This is then multiplied by revenue per paying user, to get the total dollar figure generated.

More important for the paid acquisition model is to do the LTV calculation not for paying users, but for all registered users (paying or free). Doing this then lets you figure out if you can profitably arbitrage traffic via ad buying. This is done using the same method detailed in the above paragraph, but using total user numbers rather than just paying users. Then you compare this LTV number with the effective LTV that you get from buying users and then factoring in their viral effects (as shown in the Funnel tab).

Model improvements
Of course there are tons of things in this model of freemium businesses that ought to be improved!

In particular, a couple ideas:

• Benchmarks of real world data for comparison
• More granularity for user acquisition for affiliate versus ad buys versus other
• Saturation rates in the viral model
• Better model for retention rate other than one fixed number
• More sophisticated accounting of cost per user (infrastructure/employees/etc.)
• Model in multiple revenue sources including transaction fees, for Paypal versus Offerpal versus In-store cards versus mobile
• Better intelligence around ad-buying, including ramping up when profitable, slowing down when unprofitable
• etc.

More on funnels, retention, viral, etc.
If you liked this article, please subscribe to my RSS feed! I will be writing more when I’m officially off my blog break ;-)

You can also see my other essays, check out some book recommendations, or follow me on Twitter.

Here are some links I’ve posted to my twitter account over the last week or two. You can follow me on Twitter if you like these! Many are work unrelated.

• lol, lots of ex-softies (located in kirkland goog office) complaining: http://tinyurl.com/a35rm5 
• this article on pitching game publishers reminds me of articles on pitching VCs: http://www.icopartners.com/blog/archives/33
• i’m a big ARG skeptic, good to see this article: everything you know about ARGs is wrong – http://bit.ly/BM2A
• zynga’s new MMOG – highest production facebook game so far? http://bit.ly/WZMP 
• interview with an engineer who wrote adware – lots of interesting tidbits in there: http://bit.ly/g8v9 
• neat (but oldish) – detailed analysis of second life economics: http://tinyurl.com/5z5j3u 
• great blog post on why NYT landing pages suck. Guys, this is pretty basic, get it together!… http://bit.ly/10sVq 
• generation Y research – this stuff is usually too fluffy for me, but sometimes a good nugget here or there: http://bit.ly/89Ub 
• great article on the lookery blog about behavioral targeting:http://tinyurl.com/82vmtk 
• very readable fraud confession from CEO of satyam:http://tinyurl.com/8myeob
• wow, actually found a good blog on freemium: http://www.freemium.eu/ 
• wow did you know google chrome automatically updates itself, whether you’re running it or not? http://bit.ly/1hkqeQ 

The 2009 conference season begins
Happy new year! Recently, Jason Oberfest of MySpace put together a great resource of a bunch of digital media tech conferences. I added a couple ad-related conferences, and he agreed to graciously share.

Is this list complete?
Of course this list isn’t complete at all. If there’s anything missing in the list, please leave a link in the comments!

The digital media conference list (so far):

Demos and General Technology

• SXSW
• Startonomics
• Under the Radar
• The Crunchies
• Foocamp (invite only)
• AlwaysOn
• TechCrunch 50
• Web 2.0 Expo
• DEMO
• Le Web (Europe)
• The Next Web Conference (Europe)
• Lift (Europe)

Platforms, apps, and widgets

• Graphing Social Patterns
• Google Campfire
• Widget Summit
• SNAP Summit

Online Advertising events

• Advertising Week
• OMMA
• MIXX
• DMA
• Ad:Tech
• Search Engine Strategies
• Search Marketing Expo
• Pubcon
• Affiliate Summit
• eMetrics
• Leadscon

Gaming

• Game Developers Conference
• Virtual Goods Summit
• Social Gaming Summit
• iGames Summit (iPhone games)
• Interplay
• Casual Connect
• Flash Gaming Summit
• LOGIN
• Engage! Expo

Business and analysts

• Web 2.0 Summit
• All Things D
• EconSM
• MIT Emerging Technologies Conference
• Goldman Sachs Internet Conference
• Fortune Brainstorm Conf

UPDATED: added a couple games and search ones, courtesy reminders from Jameson Hsu, Joe Ludwig, Lee Clancy, Wallen’s, Niki Scevak Charles Hudson, Andrew Parker, and Jonathan Mendez. Thanks!

Still on blog vacation – hopefully not for long
Sorry for the lack of posts guys – still semi on blog vacation ;-)

Anyway, I will be in Seattle on the 8th and 9th of January – if anyone wants to meet up while I’m there, here’s where I’m going to be:

• 8th: Downtown Seattle (free after 5:30pm)
• 9th: Downtown Bellevue (free after 5:30pm)

If I get any interest, I’ll set up drinks or meet people for coffee or something. Let me know, just shoot me an email at voodoo [at] gmail.

See you there!