Followup to Ning’s Viral Loop article
I was recently sent a copy of Viral Loop by Adam Penenberg, which just came out. I was first introduced to Adam in early 2008, when Marc Andreessen wrote us both while Adam was starting to write an article about Ning and their viral loops. That article was ultimately published in April 2008 as Ning’s Infinite Ambition, which you should read if you haven’t. After the article, Adam subsequently spent more time researching the topic, ultimately resulting in the book. I finished it and wanted to share a high-level summary and also talk through some points that the book brings up.

Summary
The book mostly covers a series of case studies from both offline and online companies. These include detailed dissections of viral companies from all stripes, including:

• Offline: Tupperware, Ponzi schemes
• Andreessen’s companies: Mosaic/Netscape, and Ning
• Bubble era companies: Hotmail, eBay, PayPal, HotOrNot
• Web 2.0 startups: Flickr, YouTube, MySpace, Bebo, Tribe, Tagged
• Widgets and apps, etc: Facebook, Slide, RockYou, Zynga

Some of the companies get pages and pages, and others just get a paragraph or two. But there’s a lot of stories that were new even to me, which is always a good sign, since I tend to love reading this kind of stuff.

The book also covers a bunch of high-level concepts about virality, such as the viral coefficient, viral loops, RockYou’s model for calculating virality, etc. All in all, a useful intro to all the major concepts in the field. It’s a great walkthrough of the history of viral companies since the late 90s when some of the formalizations started to happen.

Metrics-focused virality versus not?
One of the interesting distinctions that isn’t made in the book is the trend of startups who use quantitative techniques to optimize their virality versus products that went viral through other means. In particular, a lot of modern techniques are borrowed from the world of leadgen, ecommerce, and advertising, including:

• Formally defining landing pages (and using associated techniques)
• Creation and formal creation of funnels
• A/B testing
• Extensive use of analytics and targeting
• Deep understanding of email marketing, deliverability, and addressbook importing

From my personal experience, it seems like a lot of these ideas about virality ultimately originated from a few small teams here in the Bay Area who have now helped generations of viral companies succeed.

To me, the most important work in metrics-based viral marketing came from these companies below – I’ve listed the companies along with “descendent” startups who took the culture, playbook, and to build the next group of viral companies

• PayPal (Peter Thiel and Max Levchin)
  • PayPal mafia (Slide, Yelp, YouTube, Linkedin, Geni, etc.)
• Jumpstart (Greg Tseng and Johann Schleier-Smith)
  • Crushlink, Tagged, Hi5, others
• Plaxo (Sean Parker)
• Tickle (James Currier)
  • Ooga Labs (Medpedia, Wonderhill)
• BirthdayAlarm (Michael Birch)
  • Bebo, Flixster

There is lots and lots of overlap amongst this group above, and people cross-advise each others’ companies. Let me also caveat that the list isn’t exhaustive, and there are plenty of important VCs, advisors, and entrepreneurs that “get it” and help cross-pollinate between companies. In particular, I’ve found that the PayPal folks are involved in a tremendous number of companies in the Bay Area, and have been teaching their various companies to go viral for quite a while.

That said, I believe that the social relationships above have become less important over time to startups, as the knowledge around designing and optimizing viral loops has become more widespread. Certainly the Facebook economy has taught a wider generation of 20-something developers on how to build highly viral applications, with or without the help of the folks above. I’d note that some of them aren’t as numbers-oriented as copycat-oriented, but it’s still working for many people. As a result, I think the Bay Area is set up nicely to create the next generation of web companies as the bench in this area has gotten very deep indeed.

Who am I missing? Email me or let me know if I am in the comments. Or if someone on the above list would like to graciously identify who taught them the viral playbook, I can help trace the history further

“Viral Loop” stays high-level
One aspect, both good and bad, about the Viral Loop book is that it stays pretty high level. As mentioned above, even after you understand what a viral loop is, you have to understand the tools of the trade well enough to go execute one. Learning the ins-and-outs of direct marketing takes a long time, especially to become an expert.

Adam does a great job keeping the book high-level and relevant to people both inside and outside of the industry, but certainly it doesn’t go into any of the details that have to be mastered to do the actual execution part.

It is for this reason that the total supply of viral experts will always be relatively constrained. Anyone worth their salt would likely be working on an amazing project, early on in the team, rather than working for an established startup. Instead, what tends to happen is that the community operates on a “money + knowledge” type of relationship, in which successful viral experts advise new startups to provide both angel investment and advisory.

The limitations of viral loops as a force multiplier
Another thing that isn’t discussed much in the book, which I think is very important, is the limitations of viral loops. The quantitatively marketed companies that I mention above certainly have their successes, but similarly, many of them are plateauing and failing as well.

The reason is that there are some important factors that are not well-understood by the extended community.

First, I refer to this great presentation by Siqi Chen (of Serious Business) and David King (of Green Patch), called Metrics for Social Games:

The first slide contains a deep truth: Metrics are a force multiplier. If you don’t have a great product, then you won’t get anywhere. But if you have a great product, then it help you build a huge business.

I’ve written about a similar concept in a blog post called Creating value versus optimizing revenue.

Hitting saturation in viral networks
Another important limitation is that there’s a finite number of users out there, and after you churn through all of them, all you have to look forward to is the long plateau. I first wrote about this in my post Facebook Viral Marketing: When and why do apps “jump the shark.”

I wrote that post back in March 2008, and a lot has happened on the Facebook platform since then. This includes an incredible growth rate of the underlying platform itself (now hitting 325 million monthly actives), the appearance of Social Gaming, and it turns out that the current model to beat on Facebook comes from Zynga. They get around the jumping the shark issue by releasing lots and lots of games – 17 on Facebook, 9 on MySpace, 8 on other networks, and 5 on iPhone. And more to come every day

Although it doesn’t seem like much of a problem for most companies to hit the saturation ceiling in the networks they are operating in, it is a huge problem for VC-backed startups because then the story stops being about growth. So for the entrepreneurs who are working on their startups, it becomes important to hit a product/market fit early, and scale then, rather than prematurely going viral without a long-term product direction.

Buy the book here
Hope you enjoyed the post, and you can buy Adam Penenberg’s Viral Loop here.

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Techcrunch on social gaming scams
As everyone knows, Techcrunch recently published a provocative article called Scamville: The Social Gaming Ecosystem of Hell. Most people will have already read this article, but just to summarize, Arrington argues the following:

• Social gaming companies (particularly Zynga) are making their revenues in a “completely unethical” way
• Users are getting scammed by the offers
• There’s harmful cycle where the scammiest companies earn more revenue, then buy more ads, then scam more people
• Similarly, some users opt in to offers and then cancel, lowering their value, driving out advertisers
• And finally, the industry is in total denial about this

It’s a compelling article, and I would encourage everyone to read it. There’s also another followup article on publishers who decided not to go the offers route, HotOrNot and PlentyOfFish.

Let’s dig in
I am very sympathetic to Arrington’s views, and investigated the issue over a year ago – here’s my blog post from August 2008 on the topic: Super Rewards and the leadgen side of Facebook virtual currency – can it last?

The more I dug into the issue, the more nuanced I decided it really was – things weren’t all bad, actually. In fact, I’ve come to believe that there are plenty of advertisers where this is working for them, plenty of consumers who are happy as well, though these offer guys are leaving a trail of unhappy users.

It’s clear that of all the issues, the user experience must be fixed. And after the user experience is fixed, I think we’ll still be left with a thriving industry, though people may be making less money than they are right now.

I want to drill into some of Techcrunch’s assertions and go one level deeper to look at the evidence.

It does makes the user experience suck
First off, I think everyone is clear that the way offers run right now, they are very confusing for users. If you search for “zynga sucks” on the Facebook.com domain, you get lots of angry complaints, most them about offers. In fact, I did an article a long time ago and got a bunch of random angry comments that clearly had just been searching for SuperRewards, completely unsolicited.

This was a long time ago, so I hope their service has changed a lot – but here’s a sample:

The post is in regards to Super Rewards. From a game user standpoint, I did the offers more when Super Rewards was not managing the offers. Super Rewards is slow to respond to problems from users, and requires proof of the completion of the offer in ways that the offer itself does not require you to do. For instance, to receive points a mini-game was played, many many offers were reviewed, then the game results were given. The offer states that the points would be awarded once the user reached the results page. If the points are not received, you are supposed to file a request for review. Well, Super Rewards would not take as proof of completion all the information from the results page. They even argued with what the results page displayed, even though it was cut and pasted directly from the site complete with the web address. Instead, they wanted two emails, one confirmation email and one confirmation of the confirmation email EVEN THOUGH DOING EMAILS WAS NOT REQUIRED BY THE OFFER.

Here’s more:

I got stung by them 10 days ago. 440 points for a Discover Card application. I applied, and I am holding the card in my hand RIGHT NOW. They say Discover has no record of recieving my info. Really? Well why did they send me a card then? A$holes.

As one of the ripped off customers of Super (assholes) Rewards on Facebook, I have to say that thier service is a total and utter crap to say the least. Of the offers I have spent time filliing in I have only recieved points for 2 out of the 20 or so offers that I have completed.
Any complaints either get a automated response or no response at all.
There are now groups being formed on Facebook complaining about this type of action. I hope the group action gets up and going, these crooks need to be shut down.

Clearly this is not what SuperRewards wants, nor their game publishers, nor Facebook. And like I said, I hope SuperRewards has cleaned up their service since then.

The folks over at Gambit have written a solid article addressing these issues head on, where they discuss 3 game ending user complaints:

1. “I did your offer but didn’t get my points.”
2. “I completed this offer even though it took forever and now I’m getting spammed.”
3. “I completed this free offer and now I’m being charged all this money.”

The article goes on to discuss why resolving these issues is an important part of the game developers job, and how they can’t just say “oh that’s monetization” and not care about it. These user feelings ultimately come back into the game, and create problems long-term.

I would like to see more of the offers companies directly discussing and addressing the user experience problem openly – I think that will ultimately be the positive result of all of this dialog.

Everyone should be in agreement that the offers experience sucks, but no one is willing to do much because making these changes would mean a short-term monetization hit. It’s a Prisoner’s Dilemma where as long as the big offers providers continue in their ways, everyone wants to match them for competitive advantage. Thus my argument that the only player that’s able to get everyone in line would be Facebook.

It does seem to be working for advertisers
Arrington also makes the argument that the offers industry isn’t working for advertisers, and will eventually cause the monetization to crater. After talking to a lot of people on the issue, I just don’t know that it’s true, to my surprise.

Here’s the quote from the Techcrunch article:

And some users aren’t dumb, either. For every user who gets tricked into some fake mobile subscription, there’s another who can beat the system. That’s where the legitimate advertisers, like Netflix and Blockbuster, get hit. Users sign up for a free trial with a credit card, get their game currency, then cancel the membership and start over.

I specifically asked Jay Weintraub to look into this problem earlier in the year, and was genuinely surprised by the results. I figured that it was all a house of cards, but Jay came back to me with the idea that in fact it’s probably working (at least somewhat).

This is definitely required reading for anyone thinking about these issues. Jay did a great job breaking down the issues.

To summarize his analysis:

• The offers ecosystem on Facebook shares some surface similarities with the “Free iPods” incentivized offers industry that ultimately imploded (just read about Adteractive, Gratis, and similar companies for background)
• The volume of leads being produced by Facebook apps is so large that it’s unlikely that the crappy performance is just being hidden in the volume
• However, the pricing on Facebook will likely go down, and companies will make less money in the long run
• The offers may actually be performing, with the working hypothesis being that users actually choose the offer to fill out, versus the “Free iPods” case where they are run through a forced set of offers
• Also, long-term gameplay encourages accountability and repeat purchase

I think all the above points are surprising, and probably right.

Advertisers may reprice, rather than leaving Facebook
Arrington’s also argues that the bad leads will ultimately drive out all the advertisers. He writes:

Netflix has a policy of only paying for a user once. But game developers use a complex set of partner chains to launder these leads and try to get them through for payment. Netflix sees an overall lowering of quality and pays less for leads. Game developers, desperate to monetize, then search for ever more questionable offers to make up the difference. In the end, the decent advertisers are out, and only the worst of the worst remain.

My question is, why they won’t simply be repriced?

If an advertiser is buying leads at $3, but half the users cancel their orders, then why not just reprice down to $1.50? In fact, the best advertisers probably have the best products, and you might argue that their danger of cancelation is actually less than companies selling niche crappy stuff. Similarly, the Facebook leadgen infrastructure is now a big enough animal that advertisers may want to participate just to drive up volume. Even if an advertiser ends up with an additional $10M with no margin, they might do it anyway just to get more heft into their business.

So I agree with Jay’s argument that in the long-run, these leads just all get repriced, and the same set of advertisers (plus or minus) will remain.

Part of my positivity here is my direct experience buying Facebook advertising, which has actually been high-quality and high-conversion, for the most part. I think that the fundamental traffic is good, and thus the offer advertisers can see the same results, if they aren’t obnoxious about it.

It does create value, through product bundling
The other question is whether or not there is actually any underlying value to offers. And as I wrote in a post yesterday, offers theoretically should be good for everyone, the same way that Amazon and Netflix recommendations are good for consumers. The problem has been the execution, due to user experience issues.

Arrington seems to think, however, that getting users to pay more for the offer to subsidize the virtual good is a bad thing. He writes:

[...] Most of these offers are bad for consumers because it confusingly gets them to pay far more for in-game currency than if they just paid cash (there are notable exceptions, but the scammy stuff tends to crowd out the legitimate offers). And it’s also bad for legitimate advertisers.

I think the above statement doesn’t correctly describe how and why offers can add value overall. I won’t repeat the entire post here, except to give the outline of the argument:

• Amazon recommendations is good, and product bundling as a whole is good
• How do you define a “good bundle” versus bad? How will we target offers in the future?
• How does offer + virtual goods bundling actually work?
• Only 1% of people buy at an ecommerce site

If you haven’t read the article, check it out here.

What will happen next?
My working hypothesis is that the following things will happen – and it might take less than a year:

• The offers industry will continue to grow, the # of players will continue multiplying
• This will mean that the competition for doing leads will be cut throat, and no one will think long-term
• Ultimately, Facebook will intervene to preserve the user experience and make users feel safe in the checkout line
• If they decide not to do it themselves, they will heavily regulate the situation
• Otherwise, they will just make their own “clean” version, potentially by building out the Facebook ads into having landing pages, transaction forms, and redirects, rather than just sending clicks

Either way, I predict it will not end well for most of the leadgen players, unless they clean up fast.

If Facebook regulates, I would like to see them do something like this. Think of it as the FDA food packaging guidelines, but instead of calories it’ll talk about total cost to the consumer.

More reading
Here are some of the related articles that I would recommend anyone interested – they are from the view of the monetization gurus, and looking at advertiser-related performance, rather than user experience.

• Will social payment platforms really work long term? (guest by Jay Weintraub, leadgen expert)
• F*** your game offers (by Noah and friends at Gambit)
• Incentive Promotion 2.0 (also by Jay Weintraub)
• The impending doom of Facebook apps (by Niki Scevak, Jupiter Research)
• The economics of app monetization (with Noah Kagan, Gambit)

and two recent posts I just did related to the same Techcrunch article:

• How social gaming offers create value for everyone
• How Facebook could clean up the offers industry

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As a quick follow-on of my last post on How social gaming offers create value for everyone, it strikes me that what the industry needs to survive for the long-term is for one of the big players to break out of the stalemate of zero information sharing, and start advocating for sustainability.

Why all the advertising and leadgen companies hide their information
One of the big problems for the advertising and leadgen industries is the massive lack of information sharing between different parties. The reason is that ultimately, there are really just two parties involved:

• The paying customer
• The company providing the end product or service

But then lots and lots of middlemen get involved, including:

• Agencies / SEMs
• Ad networks
• Publishers
• Infrastructure providers
• Data providers
• etc.

Everyone in that extended chain are just middlemen, and their job is that for every $1 of profit, they want to outmaneuver everyone else in the stack to get as much of that dollar as possible. So if an ad campaign is doing really well, the agency doesn’t want to tell the ad network, for fear that the ad network will raise their rates. On the other hand, the ad network can’t figure out which of the publishers in their ad network actually deliver good performance.

This all sucks, and requires a central party to think long-term. That player might ultimately just be Facebook, but could be a publisher like Zynga (though I doubt it).

What information could be worth exposing
In general, I believe the key to thinking long-term on the offers industry would be to expose all sorts of feedback information, out in the open, at a granular level.

Users would also be able to get information like:

• What are they actually signing up for?
• A standardized view of every offer, like a checklist, similar to FDA mandated food packaging guidelines:
  • What is the 12-month cost of this offer?
  • What is the $ value of this offer to the advertiser?
  • Is this a subscription, yes or no?
  • Am I going to get emails?
  • Am I going to get a phone call?
  • Is my information getting shared with any other parties?
  • How can I cancel? (and this should be standardized too)
  • How do other users feel about this offer?
  • What is the cancelation rate?
  • How do I get customer support if I opt in to this offer
• Every offer should link to an “advertiser profile” on Facebook, with comments, ratings, etc.
• Facebook should be able to instantly ban specific advertisers and offers from ever coming up across all of Facebook

For advertisers and everyone else, they would get to see information like:

• Where are my offers showing up? (by app)
• What kinds of users are filling out my leads? (demographics, geo, etc.)
• What is the $ incentive for users? (by app, by $ amount)

Similarly, there is soft information like:

• How are users rating the app?
• How do they feel about the particular offer?
• How often engaged are users? How much churn is in the app?
• How often do they repurchase virtual currency?

For all of the above, I think a lot of companies would hate it in the short run, and a lot of dollars might be banned, but long-term, this would be better for the overall ecosystem.

Let’s hope that something like this happens!

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Ultimately, offers are about “product bundling” and it adds value to the economy the same way that any product bundling adds value – by giving people more of what they want, often for less. And naturally, some configurations of different bundles are more effective than others, as we’ll see below.

This post will touch on a couple topics:

• Amazon and “relevant” bundling
• How to define good product bundles
• What’s actually happening with offers and bundling
• Solving the 1% ecommerce problem at the Point of Sale

Let’s get started:

Amazon.com and product bundling
When you are shopping at Amazon.com, and you’re in the process of buying a book, and different book is recommended, how do you feel about that? And even more, if you happen to decide you like both books and want to buy them, and Amazon is willing to give you an aggregate discount, how do you feel?

I think that intuitively, the cross-sell and bundling that happens on Amazon is great for the customer experience, and exemplifies the good side of product bundling.

Here’s some additional information about it from Wikipedia:

Product bundling is a marketing strategy that involves offering several products for sale as one combined product. This strategy is very common in the software business (for example: bundle a word processor, a spreadsheet, and a database into a single office suite), in the cable television industry (for example, basic cable in the United States generally offers many channels at one price), and in the fast food industry in which multiple items are combined into a complete meal. A bundle of products is sometimes referred to as a package deal or a compilation or an anthology.

The article goes on to say that the strategy is most successful when:

• there are economies of scale in production,
• there are economies of scope in distribution,
• marginal costs of bundling are low.
• production set-up costs are high,
• customer acquisition costs are high.
• consumers appreciate the resulting simplification of the purchase decision and benefit from the joint performance of the combined product.

Note also there’s a darker cousin to the above, called Product Tying, in which the consumer is forced to buy the whole set and not just one. This can lead to crappier products becoming more successful, and is the kind of thing you can read about in DOJ monopoly cases.

When bundling is helpful
As mentioned in the list form Wikipedia, there are many situations when bundling is helpful to both the consumer and the business. The bundling is extra helpful when:

• The product being bundled “makes sense” to the consumer
  • “Makes sense” often means a complementary good (drink+burger)
  • Or, it might share the same context (2 of product X are better than 1)
  • Clearly targets the same audience (people who like A also like B)
  • etc.
• Also it can be a great bundle if it was something you were going to buy anyway – like if you put two items in your cart, hesitated and took one out, but were then offered the bundle together

Just as in advertising, you need to “target” your bundles and make sure they are as relevant as possible. If the industry continues to deliver irrelevant offers to consumers, then it’s no surprise that ultimately the whole thing will be written off.

I’m sure I am missing many other examples from above – please write in the comments if you have additional thoughts.

Product bundling in the offers and leadgen world
With the above points in mind, you can imagine what is happening behind the scenes in the leadgen/offers world for social gaming.

The product bundle ends up:

• X dollars worth of virtual currency
• Y dollars worth of bundled product (plus Z dollars of built-in marketing expense)

We can look at this from a couple points of view.

For the product seller, if you’re selling a product for $20, and it costs you $5 to make the item, then you have $15 worth of margin to spend on marketing and still break even. Thus as the product creator, you would be excited about buying up to $15 of virtual currency for the user, if it gets them to buy your product. And if you can buy even less currency for them, that generates profit for you and the leadgen networks and publishers between you and the user.

From the user’s perspective, the above deal can work well if the bundled product “makes sense.” If you were already going to buy a Netflix subscription, and you are being offered the same price and you get some virtual currency to your favorite social game, then that’s great.

So when Michael Arrington of Techcrunch writes that it’s bad for users to pay more for in-game currency than if they paid cash, I think that’s just misunderstanding how offers actually work in the aggregate economy:

In short, these games try to get people to pay cash for in game currency so they can level up faster and have a better overall experience. Which is fine. But for users who won’t pay cash, a wide variety of “offers” are available where they can get in-game currency in exchange for lead gen-type offers. Most of these offers are bad for consumers because it confusingly gets them to pay far more for in-game currency than if they just paid cash (there are notable exceptions, but the scammy stuff tends to crowd out the legitimate offers). And it’s also bad for legitimate advertisers.

How offers solve the 1% problem at Point of Sale
Ultimately, the biggest problem that offers solve for advertisers is the 1% problem of e-commerce. That is, at any given time, the number of people “in market” for anything is actually quite small, and the percentage chance that they will actually purchase something is also very small. As a result, if you are at a “Point of Sale” and they have their credit card out, you might as well try to cross-sell and bundle as much related stuff as possible.

The real skill and value created in all of this, of course, is in actually creating useful product bundles rather than the asinine ones I keep seeing. Social gaming and life insurance don’t mix, the same way that Free iPods and life insurance didn’t mix for incentivized leadgen.

This doesn’t mean that offers companies aren’t totally slimy and the industry isn’t broken
I want to make it clear that all of the above isn’t a judgement on whether the offers industry is working or not working. Frankly, it’s probably pretty broken (I’ll leave that discussion for another post). But I do believe that there is some fundamental value being generated, in the long-run, and someone will build a great company around dynamically creating and targeting product bundles at Point of Sale, wherever you are across the internet.

Whoever does figure that out will make a lot of money, and we’ll forget about all of this social gaming stuff.

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Small profitable companies versus VC-backed startups
I recently had an interesting conversation with a friend centered around a key question that’s come up a couple times before:

How transferable are the skills you learn from building a small, profitable company versus doing a VC-backable startup?

This question came up because part of his life plan was that he wanted to do a “real” shoot-the-moon type startup at some point in his career, but before doing that, he wanted to work on a small profitable company so that he could learn more about the process. We had a discussion around the key assumptions around a plan like that, which centered around the question above.

In general, it’s my belief that most of the knowledge isn’t that transferable, and you are better off just trying to do the VC-backable startup from scratch, rather than deferring that experience. In the worst case, if you fail, you still learn a lot about VC-backable startups and what it takes to succeed. Compare this to building a small, profitable company, where even if you succeed or fail, you may not learn what you wanted to learn.

And of course, it’s a perfectly healthy thing to NOT to want to build a VC-backable company, ever. That is a great idea too But for those who want to have that experience but are deferring it, I would encourage you to try sooner, not later.

VC-backable startups have weird constraints
Ultimately, the core of my beliefs stem from the fact that VC-backable startups have to deal with a number of weird constraints:

• they should grow really fast – people sometimes say ideally hitting $50M in revenue in <5 years
• they should be defensible – ideally having real technology that isn’t easily duplicated
• obviously, you want a great, experienced team – ideally experienced operators or cutting edge technologists
• it’s very centered on SF Bay area and less so on a few other areas (Boston/Seattle/NY/SoCal/Austin)
• early stage is focused on proving things out to get each new round of funding, not on profitability (which is a nice to have)
• etc.

Again, most of the above are nice to haves and they are always on some investor checklist somewhere, and are followed loosely/casually in most cases. Similarly, to get in the game, there are significant “community” effects that kick in too – it’s good to have the right angel investors, because they can help connect you with the right VCs. But angel investors are just random people (albeit random successful people), and they sometimes don’t like to give money to strange people from other cities. So they like to invest locally, and only through people they already know.

So the point on all of the above is, VC-backable companies have all sorts of weird constraints on what you have to be able to do.

Understanding these constraints, and working with them, requires a different mindset than if you are just targeting for profitability.

There’s different constraints on Lifestyle companies, aka Small/profitable companies, aka Passive income companies, aka whatever you want to call them
I think most of the constraints above are pretty silly if the only goal is to build a self-sustaining company that can get profitable and kick off passive income. In those cases, you really don’t need all the constraints above, which really take you down a different path.

In those cases, you could really execute your company anywhere – you don’t have to be in the Bay Area. Rapid growth is both unnecessary, and possibly not desired if new users are creating costs! Instead, you might prefer to charge users upfront, so that you can be sure that you can stay cashflow positive. Similarly, it’s fine to just work with your buddies, or family, or whatever you want – there’s less of a need for them to scale the business quickly, nor will their experience level play a role in whether investors fund the company.

What both the two styles of company do share, however, is that you still need to be able to build a product, and build a business for cheap, even if you are going after different goals.

But even with product development, when you are going for a smaller, self-sustaining company, it’s more OK to target niche markets or build high-quality products for slow-growth businesses. You probably don’t want to build for a new market, since that can take a lot of time and capital to get right.

How much do you really learn?
To net this discussion out, my point is that the two styles of companies are different in as many ways as they are similar. Instead of “walk before you run” it’s more like “learn to sail versus learn to bike.” Learning to sail does not increase your chances of success at cycling, and vice versa, as well.

So for all the engineers out there who are thinking about doing small web projects before trying to take over the world – go for the latter

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My experience in venture capital
As I’ve blogged about before (though quite a while ago), I spent some time at Mohr Davidow Ventures as Entrepreneur-in-Residence – for more about what that job is, read here and here. A couple years before that, I had spent some time at MDV in their Seattle office, towards the end of the dot com bubble, as an analyst/intern. Both experiences were a ton of fun, and I justified the ~3 years in venture capital where I could have been starting companies as an education that would help me later on.

Now, a couple years later, I thought I would reflect a little bit on where the VC experience helped and hurt me relative to actually trying to build a startup. The net of it is that the time was mostly helpful, and a big chunk of knowledge transferred over, but it was mostly high-level stuff. A lot of running a startup involves mastering nitty gritty details, and the VC experience did nothing to help there

For the lazy/impatient, here are some key things I’d say where it can help:

• It helps with traditional investor/entrepreneur information asymmetries
• Lots of tactical holes still exist
• Investors can often oversimplify startup issues, or overmatch on patterns
• Helps with understanding of investor motivations, which can otherwise appear mysterious

Let’s dive into each of these issues below.

It helps with traditional investor/entrepreneur information asymmetries
Some of the stickiest situation for entrepreneurs are cases where they infrequently encounter a situation, which generates information asymmetries where an investor often knows much more. These asymmetries often involve events like fundraising, selling a company, recruiting executives, etc. In the positive case, investors can be helpful and coach startups through these times, which is great. In the negative case, it provides an opportunity for investors to engage take advantage of naive entrepreneurs, which is not so great. This is why sites likes VentureHacks and TheFunded are useful, because they help even the playing field.

Part of the problem for me, however, is that only the General Partners at VC firms end up actually doing deals. All the associates, EIRs, etc often participate, and you see the final deal terms, but rarely get to see all the back-and-forths that end up with the deal getting done. This creates familiarity with the process, but not the battle-tested experience of having gone through lots of nitty gritty negotiations. But even then, you hear about, and know what the levers are, so everything is less mysterious.

(But like I said, VentureHacks and TheFunded are great, and I only wish there were sites with that level of candor about this obscure industry)

Lots of tactical holes still exist
One area where a venture capital background didn’t help at all was dealing with all the tactical details of getting a company off the ground. In particular, the biggest hole by far is hiring and managing people, which gets abstracted at the financial level. Someone in VC-land can talk abstractly about strong teams, but you don’t have to go through the process of interviewing dozens of people to find the right person.

I’ve written up some of my thoughts here on this topic, in a post called “Building the initial team for seed stage startups” where I talk about a couple points I’ve come to believe:

• Hiring T-shaped people versus specialists
• Try to get doers
• More candidate flow solves a lot of problems
• Interview for the actual work you’ll be doing, not skillset trivia
• Raw intelligence is just one factor – don’t overestimate it

There are also some even deeper questions that are unanswered by VC experience, such as how you actually build out a suitable recruiting pipeline? Or how do you interview people where you don’t have the skillset to comment about their competence one way or the other?

I would say hiring is probably one of the most difficult areas to master, and although there are other block and tackle issues – accounting, leasing an office, operations, etc – getting the right people is just a very hard topic. It’s not a surprise that so many startups struggle with it.

Investors can often oversimplify startup issues, or overmatch on patterns
Venture investors often spread their time across a whole number of industries – you look at their websites, and they’ll say they invest in everything from consumer internet to clean tech to life sciences. MDV was no different, and we were responsive to companies across a large number of markets. One VC explained to me early on that you have to respond to what entrepreneurs are producing, and if you get too “top-down” about a particular industry, it gets easy to overinvest in a bunch of mediocre companies rather than trying intently to just focus on finding the best single team and opportunity you can.

Mike Moritz has talked about this before:

Moritz waxed philosophical by comparing venture capital investing to bird spotting. “I rarely think about big themes. The business is like bird spotting. I don’t try to pick out the flock. Each one is different and I try to find an interestingly complected bird in a flock rather than try to make an observation about an entire flock.” For that reason, while other firms may avoid companies because they perceive a certain investment sector as being overplayed or already mature, Moritz said Sequoia is “careful not to redline neighborhoods”.

Continuing with the ornithological analogy, Moritz pointed to Cisco and said, “There’s a lot to be said for investing in the ugly duckling.” When Don Valentine led Sequoia Capital’s investment in Cisco, many others had passed on the husband and wife founding team of Len Bosack and Sandy Lerner.

One of the difficulties for me personally in seeing a wide variety of companies all the time was that it was impossible to not start to pattern match and draw conclusions about the companies that were probably false. You end up in the proverbial “mile wide, inch deep” level of knowledge about that industry, which makes it all too easy to make generalizations. Similarly, there is a drive to simplify your understanding of a company, since you have to socialize it and talk to other venture partners about particular spaces and companies, which also causes oversimplfication.

Contrast this to startup life, where you end up devoting yourself to one company (which may encapsulate many ideas, as you iterate) for the period of years. You end up diving very deep into situations, and learning about all the different details tradeoffs that cause products to be successful versus not.

Helps with understanding of investor motivations, which can otherwise appear mysterious
Finally, one area where having a venture background helped a lot was understanding investor motivations in general. Entrepreneurs ask a lot of great questions, like, “Why don’t investors want to invest in my idea X which will be highly profitable?” or “Why does hot consumer internet startup X lose tons of money but is valued so much?” The answers to these questions drive a lot of investor behavior, which can be mysterious if you don’t know what’s going on.

The interesting part is understanding why VCs are structured the way they do, why they have a 1 in 10 portfolio strategy, and how they think about their Limited Partners. They have a boss too, of course

The major point here is that building medium-sized, profitable companies that aren’t growing quickly is not really part of the venture capital model. Knowing that can help with all sorts of things, such as massaging your business plan into something “sexy” that investors will respond to. Similarly, it will help get everyone aligned on major decisions, such as financing events, exits, exec team building, etc.

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Ignore Cougars, Follow the Money: 3 social gaming tips for monetizing younger users
by Susan Su, Gambit Payments

Lately, we’ve all heard a lot about the middle-aged housewife. She’s an adult, she’s got disposable income and a couple of credit cards, probably even a PayPal account. In her leisure time, she sits at home and plays social games with her Facebook friends, possibly instead of going out to a movie. She buys virtual goods with real money while you fill your Olympic-sized swimming pool with gold coins.

This is a great bedtime story to fall asleep to, but would you feel so relaxed if you knew you were leaving millions of dollars on the table?

Age is only one factor
In a previous post, we looked at user age as a factor in a game’s overall revenue. We took a bird’s eye view of average revenue per paying user (ARPPU) by age and transaction volume, and saw that older users – the middle-aged housewife (or husband) – brought in ARPPUs that were 2 or 3 times as much as ARPPUs for younger users. Because of low transaction volumes, however, older users represented little more than a tiny speck of total revenues across the developers in the dataset. It became clear that age data – and even ARPPUs – meant little without the context of volume.

On the other end of the ARPPU spectrum, younger users delivered ARPPUs that were fairly unsexy and unvarying, ranging from $2.58 for 16 and 17 year olds to $3.07 for users aged 20 to 29. However, users aged 14 to 29 together make up 91.5% of the total userbase for the virtual goods industry.

$2.58x…millions
A $2.58 ARPPU doesn’t look so bad when you’re selling to millions of users. The massive transaction volumes associated with teens and 20-somethings aligns directly with this group’s share of total revenue across a sample of nearly 2 million virtual goods users. How massive? Over 93%. That is, users in their teens and twenties bring in over 93% of all revenue seen across all games, for all developers in the sample. Even though ARPPUs are consistently modest, transaction volume – and with it, total revenues – are jaw-dropping.

Younger users are cheap, plentiful, and worth your attention.

Given these figures, what’s a developer to do?

For starters, don’t ignore your younger users. It’s true that there will always be transaction and ARPPU variation based on the game you’re building, what you’re interested in, and what resources you have available, but it’s clear that younger users are still the major players across the board. There are hordes of them, and they’re eager to engage in lots of transactions.

Get Them Young: 3 tips to monetize younger users

1. Think volume. Look for the users who are transacting the most, and then make sure you understand exactly who they are (and how they might be changing). For example, today your revenue may be driven by a massive group of teenagers, but what will happen when those teens become 20-somethings? In this series, we explored this question by age, but you’ll also want to think about geography, language, and gender. ‘Think volume’ means:

• Mind your game. If your product is subpar, you shouldn’t expect amazing volumes or revenues, no matter how much you…
• Focus on growing traffic through virality. How can you make your game even more social, more addictive, and more spreadable?
• Get users to complete. Users are 3 times as likely to make additonal payments if they’ve completed at least one offer.

2. Hold on to your users. People of all ages get tired of games easily. The last thing you need is a poor user experience to push users over the edge and straight into the database of a competitor. Do certain offers just rankle your userbase (leading to poor conversions, bountiful complaints, and churn)? While your payments solution’s algorithms will help you find the best offers for your users, there are always going to be a couple that just don’t perform. ‘Hold on to your users’ means:

• Pick out and remove underperforming offers, either individually or by offer category, and address customer complaints. For example, ‘adult’ offers may not work well if your game’s users are primarily 13-17 year olds.
• Diversify your product(s). How can you enrich a single game to be more complex and engaging? How can you offer more complementary games so when a user defects, she defects to another game in your suite?

3. Keep your eye on empty spaces. Yes, Facebook is huge. Yes, Zynga is dominating. But, growth potential is everywhere still. As more users of all ages sign up for their first Facebook accounts, more people pour into the virtual economy. As Facebook grows in locales outside the U.S., so do the games and apps that inhabit its ecosystem. As users get tired of specific games, they’ll start looking for other places to spend their time and money. They’ll probably invite their friends, too. ‘Keep your eye on empty spaces’ means:

• Don’t make a play just because someone else is making bank off of it (for now). Today’s leaders got there because they kept their eyes on empty spaces and filled them, quickly.
• Look for under-monetized user groups. How well is your game doing with young males? Can you work in a way for more of these users to complete their first offer (and open the door to additional payments)?

These should be your main considerations:

Growth
What does the growth trajectory look like for young users? How many of these users are already playing games, and how many more aren’t? The online casual games industry is still young and has plenty of room for growth.

Facebook boasts 300 million active users, with almost a third of these in the U.S. Since the entire population of the United States is just over 300 million, that means approximately half of all U.S. internet users, or a third of the entire U.S. population, are on Facebook.* Facebook counts 70% of users as having ‘engaged with a Platform application,’ meaning that most users have loaded an app of some sort at some point in their Facebook time. Judging by the impressive monthly active uniques the biggest developers are enjoying (51MM for Zynga’s Farmville alone), it seems that games have already taken off on the network. With all this, is there still room to grow?

Yes. Here’s why:

• Facebook has saturated the U.S. market, but that doesn’t mean every Facebook user is playing a game. Yet.
• The U.S. isn’t the only country in the world, either. In terms of Facebook traffic growth rates, the U.S. doesn’t even make it into the top 10. As other economies (real and virtual) catch up, markets around the world should start looking more and more promising for developers looking to monetize.
• People get tired of games. One developer’s churn is another developer’s new user.

As mentioned above, younger users contribute the lion’s share of total revenue for virtual transactions – for now. However, Facebook reports that the 35 and up group is their fastest growing demographic, so will we see this shift reflected in game usage and monetization too? Probably. But until the older users reach critical mass on the network, would you rather be competing hard for the same handful of housewives or slyly going for the many younger users at lower ARPPUs and massively higher transaction volumes?

Changing ARPPUs
Do ARPPUs change as users get older? Will your 15 year old user be worth more after she turns 18, gets a better job, and starts opting for direct payment over offers? We know that the typical 18 year old makes you more money than the typical 15 year old, so from this we might guess that it will pay off to hold onto that user as she ages.

Older users
Should you try to grow your older userbase? As just mentioned, Facebook’s fastest-growing demographic is the 35 and up set. While actively trying to acquire these users (over others) may divert your resources in ways you can’t afford, it’s likely that your game will indirectly absorb the benefits of Facebook’s demographic growth anyway. If everyone else is focusing on winning the middle-aged housewife segment, would you be better off stealthily (and expertly) acquiring the forgotten younger users? Try it. Measure it. Report back.

Conclusions
In parting, don’t buy into a ‘must do’ (eg. housewives) just because it’s popular today. Popularity doesn’t mean it’s wrong, but it does probably mean that lots of other developers are out there thinking the same thing as you. Instead, look at the data and focus your work where the greatest opportunity currently blossoms. Right now, that’s users who are in their teens and mid-20s.

If you’ve been targeting and you’re seeing interesting results, please share in the comments. What’s worked for you, and what would you do if you were a new developer just entering the marketing today?

For specific questions on data or resources, you can contact Susan here or follow her on Twitter at @susanfsu.

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Designing a viral loop has multiple stages
Viral loops have been featured in mainstream media and there’s even a book coming out on it – but the step-by-step design of creating a new loop remains obscure, and for good reason. I’ve come to believe that creating viral loops is akin to building a software project – at best, it still comes down to a great team, a strong understanding of the tools available, and relentless iteration. There’s no recipe at the heart of it which guarantees a viral process every time, the same way that you can’t guarantee that any software project will result in market success.

There are no silver bullets in viral marketing
In fact, the core of virality ensures that there will never be a dominant “recipe.” If everyone knows how to build a viral loop around social network invites, then everyone will do it, resulting in consumers will become desensitized, which finally leads to lower response rates. Thus this causes the viral loop to unwind, which leads to long-term disaster.

The only way to combat this is to build a viral loop around the core of your product – something that no one will seek to duplicate, unless they are a direct competitor. These viral loops are incredibly effective because they are lasting and sustainable.

I wanted to jot down a couple thoughts on the different stages that viral loop design go through, so that the entreprenurs reading through this can imagine deeply ingrained, user-aligned ways for their products to gain distribution.

Strategize: Stage 1
The first stage of a viral loop is developing the core strategy around the loop. This requires the viral loop designer to think through, step-by-step, how a user will come to find their product and how they will ultimately pass it along to their friends. If you’re lazy, there are lots of recipes to follow from the Facebook ecosystem like quizzes, “find your friends,” and gifts. As discussed above, these opportunities are already becoming less effective every day.

Even if you decide to use an existing recipe, here are some higher-level strategy questions that should be answered before proceeding:

• How does this viral loop fit into your core product?
• What is the fundamental value proposition you are presenting to your users?
• If your loop is successful, will users transition to your core product or will they bounce when reaching the switchover point?

As you might imagine, most of the discussion here is qualitative and there’s very little A/B testing involved.

Implement: Stage 2
The next stage is the rapid development of the core viral loop. This part should hopefully take days or weeks, not months. It will also certainly be wrong. The best advice I can give here is to follow agile development models and to build the smallest number of features and pages to create the initial flow of pages.

As mentioned before, the best implementations are strongly tied to the core product – as a result, if you’re a video site, it’s best if you can somehow involve videos. If you’re a dating site, you probably want to involve dating.

The other implementation advice I’ll give is to treat the viral loop code as an iterative, protoyping process. So copy and paste all you need, keep it in a separate codebase, and make it easy to refactor. You’ll need to do a lot of messy stuff like changing the order of pages or page elements later, and once you develop your own recipe, it’s easy to rewrite it in the “right way.”

Launch: Stage 3
The next step is to beg, borrow, or steal traffic The easiest way is often to pay for it, $50/day or so, just so you have a trickle of traffic coming in.

Optimize: Stage 4
As you get a flow of incoming traffic, this allows you to deeply optimize the experience. This will involve building out some basic infrastructure to do A/B testing, or using Google Web Optimizer, and otherwise. The key thing here, of course, is to measure whether or not the $50/day you’re spending results in traffic above and beyond what you’re paying for – the more the better, and eventually you’ll cross the threshold where traffic scales infinitely.

In this stage, there are a lot of common fixes that you’ll want to consider:

• Shortening the flow of pages (can you shrink a 5 page funnel down to 2?)
• Rearranging UI elements to emphasize next steps
• Testing different value propositions for going through the flow
• Increasing the # of people invited

This optimization stage creates great conflict for product and customer-oriented people. Oftentimes, to get a number to move from 10% to 30%, there’s temptation to do things that users may not be happy with. This might include things like asking for invites multiple times throughout the initial session, presenting an opt-out process for selecting friends, etc. These are all bad and need to be fixed in order to create a long-term sustainable viral loop.

This optimization step can take a very long time (months is not uncommon) as you zero in on the dozens of small and large changes needed to create a viral loop.

After months of work, two outcomes can result:

• You don’t reach your goal, and you’re stuck on traffic
• You reach your goal, and your traffic is going bananas!

If you don’t reach your goal, then it’s time to stop your optimization process. Often the changes that result are just too small to drive substantial increases in metrics. Instead, you’ll have to rework your entire value proposition, which means to either go back to Stage 2 or possibly Stage 1. This means you’ll want to stop A/B testing and start building out a deeper featureset.

Refine: Stage 5
If your optimization step was successful, your work is probably not done. The final step is polishing your viral loop.

This includes figuring out issues like:

• Making your loop as user-aligned as possible
• Building a pleasant user experience and removing unnecessary flows or page elements
• Refactoring the code to move it from prototype to production
• Integrating it into your core product in a way that makes sense

A lot of people are tempted to skip this polish step, but don’t do it! Skipping this step means that your initial product experience will suck, or be offensive.

In fact, when there’s “excess” virality, that’s a great opportunity to make changes to the viral loop that make it nicer or friendlier. In general, if you are getting exponential growth, it’ll be great even if it’s a slower exponential. What’s more important at that point is spendfing your extra growth towards changes that positively impact long-term retention.

On the other hand, if your product is just meant to be short-term mad money, then by all means skip this step

More on viral loops and marketing
For those that are interested, I’ve written more about viral loops and marketing here.

Off-topic
Also, I found this image while searching for “Fractals” and thought it was funny enough to share:

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Age (and ARPPU) ain’t nothing but a number
by Susan Su, Gambit Payments

In the game of life, you’ve heard that age ain’t nothing but a number. In the world of social games and virtual currencies, the same thing goes. The smart developers know to segment by age groups and target towards those with the highest demonstrated ARPPUs. The even smarter developers know that age ain’t nothing but a number – a single, lonely metric that can dangerously limit your view when you exclude crucial supporting data.

In this post, we’re using demographic data from Gambit Payments to get a bird’s eye view of ARPPUs by age and transaction volume. We’ll see that age data – and even ARPPUs – mean little without the context of volume.

A look at highest grossing ages
Which users pay the most to play?

From this data set, you can see that Gambit’s developers got the highest ARPPUs with users aged 50+. In this month, 60 year old users brought in a $7.92 ARPPU, more than double the ARPPU seen with the younger set.

Players in their 40s averaged a $4.39 ARPPU range – a pretty impressive figure still. Going younger, players in the 30 to 39 year old range brought in a slightly lower ARPPU of $4.11 while players in their 20s brought in $3.07 on average. Finally, teenage players brought in ARPPUs in the mid-$2 range.

This data should come as no surprise. Let’s take a look at the main levers feeding into ARPPU:

• Income. How much money does this user or group of users make? In most respects, this lever is straightforward; if the user in question doesn’t pull in an income, they won’t initiate direct payment for your currency. But, that’s what offers are for. Note, however, that offers typically do not bring in the same flashy ARPPUs as direct credit card or PayPal payments.
• Access to which type of payment. What payment methods are available for this user or group of users? Since we’re talking ARPPUs here, a paying user is a paying user – and thus already has access to some type of payment. However, remember that not all payment methods deliver the same dollar value to your pocket.
  • Does this user or group of users have access to credit card payment or PayPal? If so, you’re in luck. These methods typically bring in the highest revenues because they’re relatively easy and impose minimal friction. Note that PayPal penetration may be low in some parts of the country and world, so it’s unlikely that PayPal will be your biggest breadwinner overall.
  • Will they be paying through their mobile provider? With mobile, money travels through lots of different hands – mobile aggregators, mobile operators, mobile payments providers – before it reaches you, a trickle-down process that will affect earnings accordingly. Also, keep in mind that mobile is based on fixed pricepoints, which gives you less flexibility for what people will pay for. Finally, when paying with mobile, there’s also a cap on a transaction’s dollar amount – you can’t, for example, pay for something costing $100 through your mobile service. While mobile payments typically bring in lower ARPPUs, they also have lower access barriers and are relevant to a wider swathe of your users.
  • Will they be completing offers to earn your currency? Offers can bring in decent ARPPUs, but, for certain user groups, may lack the longetivity of direct payment methods. Will your users complete offers, only to decide that they hate the experience and would rather abandon the process – or your community – altogether? How will you deal with this? For further exploration of this topic, see Gambit’s post on user complaints and coping strategies.
• Willingness to buy online. What is this user’s comfort level with online purchasing? If they’re uncomfortable with online purchasing, ARPPUs associated with this user or group of users will dive accordingly. This becomes a particularly interesting question when you start looking at other demographic data in addition to age – you may find, for example, that users in a certain geographic region are more comfortable with online purchasing because of variance in internet penetration or fluency.

If these levers sound familiar to you, you’re doing well so far. Now let’s see how each of these factors works in the context of the data presented above.

Older users
Older users not only have disposable income, they have access to the payment utilities – credit cards, mobile phones, PayPal accounts – that bring their money to your community. Why 60 year olds specifically? You should view the fact that 60 year olds were at the top as a datapoint specific to this set (an outlier) than a generality that should be extrapolated into rule. If you take a look at the groupings, the 50+ group still achieves an average ARPPU (across individual years) of $5.20 – pretty impressive.

At the other end of the spectrum, your community’s youngest paying participants probably don’t have jobs or the disposable income they bring. Their access to direct payment methods is likely to be highly limited or nonexistent. On the other hand, they probably do have access to mobile payments, and can always complete offers. Based on the notes above, you know that payment via mobile and offers will mean lower ARPPUs for these users.

The key here is to know your users – Who are they? How much money do they make? Where do they live? What types of payment methods are available to them, and how willing / able are they to engage with different methods?

Revenue breakdown by age
Finally, does all this mean it’s time to regroup your acquisition efforts and start to go after the 50+ set (or, if you have been already, give yourself a hearty pat on the back and quit working so hard)? Not yet. Let’s take a look at the percentage of total revenue that these groups bring in, respectively.

It turns out, despite impressive ARPPUs, the 50+ group makes a sad showing when we start looking at percentage of total revenue. If we’d halted our analysis at individual ages, or even broader age groupings, and the ARPPUs they demonstrated in this data set, we would have missed the point entirely.

Transactions breakdown by age
For Gambit developers, 50+ was the goose that never laid its golden egg. All the users in this entire group represent only half a percentage point of Gambit developers’ total revenue for this period. This isn’t because there are 200 age groupings, either. Let’s take a closer look.

Wow. The 50+ group represents a meager 0.3% of total transactions – a figure so small that it barely registers a speck on the revenue radar for Gambit’s developers. Users in their 20s, by contrast, produced 22.5% of all transactions. Finally, teen users represented a whopping 73.5% of transactions made across all Gambit developers. 73.5 versus 0.3… suddenly that $7.92 ARPPU doesn’t seem so significant anymore.

What’s good about the user groups bringing in lower ARPPUs, and how do you optimize their experience to impact your revenues? Conversely, is it possible or worthwhile to improve transaction volume for the highest ARPPU groups? In next week’s post, we’ll go over the strategy implications of the data we presented here and contrast a few approaches to make you more money.

For data geeks
If you prefer to look at the above data in a neat table instead of a fancy pie chart, here it is:

Hope you enjoy this data from Gambit Payments, and part 2 of this article will be coming soon!

[Andrew: Thanks again to Susan for putting together this great post!]

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A quick rant:

Every time you talk to an ad network or leadgen network or whatever, if you ask what their differentiation is they will say “targeting.” That’s probably wrong, and let me tell you why, based on the recent announcement of the Netflix prize winners:

Netflix was able to wring three years of research to nudge its recommendation algorithm up 10.5 percent, at a cost of $1 million in prize money — a stunning feat on its own.

This means if you combine dozens of the best machine learning people in the world, some of the cleanest datasets, you get a measly 10.5% increase. Compare this to starting a new ad network where you end up with noisy datasets, lots of crappy traffic, and a small team looking at the problem – that’s not an easy path to disruptive change. In general, 10% is not a big enough number to counteract the other economic drivers in the ad market, which revolves around better deal terms, a larger selection of advertisers, better ad inventory, etc.

I would guess that you need a number closer to 50% lift or higher in order for an upstart to dramatically change the ad landscape and neutralize the weapons of the mass of ad network players.

I think disruptive change will come not from algorithms, but rather two other areas:

• Better ad inventory: New websites and mechanics emerge all the time, and who knows what happens when you put ads on them? It was clear, until they tried it, that with the right ads search can be >30% clickthrough rates or more, which is unheard of.
• Better data: The other big opportunity is in using specialized data to drive your algorithms – rather than basing everything off of domains, cookies, and ad impressions like everyone else, there may be ways to extend the targeting to unique datasets that no one has access to. This is what’s happening in the world of retargeting.

The Netflix prize also included people adding in additional data, and that’s factored into the 10.5% improvement. Anyway, the point is, increasing performance on stuff like this is very hard, so when an ad network tells you about their targeting, you should push them instead on their revenue split

Why A/B testing and visual design come into conflict
It’s great to implement consistent A/B testing in their product process, but then it becomes even harder to keep a consistent visual design while doing test after test. This tension comes from the fact that A/B tests push you towards local maxima, making the particular section of page you’re testing high-performance, but at the expense of the overall experience. As a result, there’s a lot of temptation to “hack in” a new design, the way that software engineers have to “hack in” a feature – but this is short-term at best. This often means adding a bold, colored link to the top of page with “NEW!” or adding yet another tab – these are all band-aid solutions because once you get to the next set of features, it’s not a scalable design to have 100 tabs.

Each of these competing features, taken by itself, moves the needle positively. However, there isn’t a great way to measure the gradual “tragedy of the commons” effect to the overall user experience. Each new loud page element competes with all previous page elements, and must be louder as a result – this leads to the Vegas effect that many Facebook apps end up in.

To really solve this problem, you need a central design vision – there’s no way around that. It also helps a lot to have a flexible design that embraces A/B testing – you can work with your designers to make this happen through modular, open elements.

Closed designs make it hard to add or remove content
Let’s take a particular example and look at it – this might be a standard example on a page like a video or otherwise:

It looks nice, but also has tremendous sensitivity to the content and an inflexible design that makes it hard to test new content. To be more specific, ask yourself the following quesitons:

• If you wanted to add a comments count in addition to views and votes, how would you do that?
• What happens when the views number gets beyond 10,000?
• What if you wanted to add favorites, or flagging for inappropriate content?
• If we decided to hide the thumbs down, how would this visually balance?
• If we wanted to fit more thumbnails onto a browse page, how easy it is to shrink the main thumbnail?
• etc.

The above design is an example of a “closed” design where everything fits just right, but makes it very difficult to add or remove elements. There’s an exact balancing of all the parts of the element, which makes it very sensitive.

Many of the solutions to the questions involve either require building out new pieces next to the element, which throws it off balance. Thus, if the above were used in an A/B test, the visual look would be immediately ruined.

Open designs that are A/B test-friendly
Let’s compare this to the elements below, which have a more modular design that can scale vertically:

The above elements don’t have the same “just right” visual appeal, but make it much easier to add and remove content. The key design decision is to add multiple bands of content which can be grouped together and extended vertically. Ideally, you would never end up with a repeating tile of 4-buttons and 3-stats, but you could certainly test it much more easily than with the closed design.

Here are some of the variations that can easily be tested:

• Switch the title section and the stats/buttons sections
• Add and remove buttons (or no buttons!)
• Add and remove stats (or no stats!)
• Combine price tags with other stats
• Try different buttons
• etc.

Following an open design on page elements enables substantial A/B testing within some flexible constraints. Now you may still be tempted to do something crazy like big hover overlays, <BLINK> tags, and other stuff, but at least you can make it easy to test a wide variety of low-hanging fruit. It also makes the owner of the overall visual design able to maintain a central “style guide” while still offering enough flexibility to keep people creative.

This same idea of open designs with horizontal bands of content can be applied to whole pages too – let’s examine a page from the king of A/B testing, Amazon.com.

Open page layouts
From the snapshot below, you can see that Amazon groups the center column of content – each element has a title explaining how it is, a list of items, and a navigation link to see more. This is also true with the item detail pages, which use a similar grouping to show everything from similar books to reviews to other elements. These pages can get very long, but because most of it is below-the-fold, it’s easy to get away with.

I’ve been told that this modular design enables Amazon to take a “King of the Hill” approach to testing each horizontal band of content against each other. Different software teams will create different kinds of navigation and recommendation, and if it causes people to click through to buy, then it floats up higher in the page. This systematic A/B testing is much more easily enabled when there’s the design flexibility for that sort of thing.

Here’s a snapshot for a reminder of what this looks like:

While you may argue that Amazon’s design is cluttered and actuallysucks, on the other hand, this approach lets them take a very experimental approach to pushing out features. It makes it very low-cost to implement a new recommendations approach and try it out without needing to figure out how to design it into the UX.

What’s next? Modular user flows?
Of course, if you can take a modular approach to scaling individual page elements or entire pages, the next question is whether you can take this approach to user flows.

I’ve never seen anyone do this, but this is how it might work:

• Any linear user flow is identified in a product (like new registration, payment flow, etc)
• This flow might be 1 page, or broken into N pages
• Similarly, every individual page might have a bunch of fields (like photo, about me, etc.)
• As part of the A/B testing process, you might want to drop a new page (or new fields) into the flow
• Then an optimization process shuffles pages throughout the flow to identify the best page sequence and page-by-page configuration

You might imagine something like this could be a very powerful process as it would allow you to identify whether you should offer a coupon pre-transaction or post-transaction, or on any given page, where an input field should be placed.

For those who want to know more, I have written a bunch more about A/B testing here.

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This slideshow was on a number of blogs over the summer (see here) , but I wanted to make sure that everyone saw it and, frankly, to get a post here so I have a record of it.  Apparently, Reed Hastings, the amazing CEO of Netflix, put-up a set of 128 slides that is a “reference guide to our freedom and responsibility culture.” I realize that most 128 page slide decks are deadly dull, but this is an exception.  You may not agree with all their values and approaches, but on the whole I think you will be fascinated by the detail and thought.  Now, I have no inside knowledge of what it is like to work at Netflix, but if this is accurate, it is a pretty impressive company — frankly far more enlightened than most in SiliconValley.

Low-fidelity prototyping versus high-fidelity prototyping
In my discussions with designers, one of the interesting recurring conversations is the tools and process they use to prototype and mock up experiences. In particular, there’s a lot of divergence on how high or low-fidelity to go with a prototype.

For designers that primarily come from agency backgrounds, I’ve found that there’s an emphasis on quickly getting to a near pixel-perfect mockup, and the variations are minor in detail. In that worldview, the ideal deliverable is a single version of something that feels high-quality and gets minor feedback from clients. In the client-agency model, if you give your clients a bunch of rough mockups that seem low-fidelity, then you risk looking unprofessional. Or worse yet, you might get a ton of diverging comments that you then have to work out and iterate – in some cases that’s the last thing you want to do.

This is especially not a good situation for companies that focus on products delivered to customers –  in that case, you mostly want your product to be the right one, no matter how many iterations it takes. As a result, low-fidelity prototyping can be really useful because it aids an iterative, customer-focused approach rather than one where the Great Designer comes up with something directly from his brain.

Here’s a couple of the main advantages:

• Get better and more honest feedback
• It’s great for A/B testing
• Make the cost of mistakes cheap, not expensive
• Refine the page flow, not the pages
• Figure out the interaction design rather than the visual design

In addition, after I’m done arguing my point, I’ll recommend some of the tools that have been useful for me in doing low-fidelity prototyping.

Get better and more honest feedback
The first time I really undestood the power of low-fidelity prototyping was when I started doing usability tests on consumer products I had built (This was years ago). Initially, I wondered why anyone did paper-prototyping? I immediately concluded that it was due to a deficiency of many designers that they couldn’t write code, and thus couldn’t do HTML mockups of the products they wanted to build.

But once I started getting people to view and interact with my prototypes, I realized that one of the big problems was that people didn’t give good feedback when the prototype you present to them is too perfect. Rather than telling me about the really high-level things, like “does the value proposition make sense?” instead they would focus on colors, fonts, the layout of the page, etc. And furthermore, they didn’t feel that they could really jump in and build on top of the ideas you showed them, because it was far beyond their capability to duplicate.

Compare this to a simple exercise where you are using hand-drawn cards or drawing paper and are literally sketching stuff out during a customer interview – you’re much more likely to try something out, and have the person you’re interviewing grab your pencil and say “no, more like this!” And that’s exactly the kind of interaction you want.

It’s great for A/B testing
As for as a metrics-driven approach goes, you have to remember that techniques like A/B testing fundamentally thrive off of variety. In particular, it thrives off of variety at the UI layer, where many small UI changes that cost very little technically can be tried out and optimized. As a result, you don’t want 2-3 pixel-perfect mockups, you actually want 10 or 20 rough mockups where you can select only the most high-variance candidates.

Some of the highest variance stuff has to do with changing the order in which you do things, or opt-in versus opt-out, or richer AJAXy interactions. These are all things where it’s easier to generate many candidates through low-fidelity prototypes since you’re often looking at things form a systems level.

Make the cost of mistakes cheap, not expensive
One of the hidden benefits of having a low-fidelity prtotyping process is that it makes changing directions much easier, which naturally facilitates a collaborative design discussion. When you’re using a customer-driven product philosophy that incorporates a lot of outside metrics and qualitative feedback, you’ll probably get multiple people involved in the design process. If it’s done by one person or a small group, and is polished significantly before it reaches the greater group, one of the problems is that it discourages collaboration. It’s very hard for people not to get defensive when they’ve spent a lot of time polishing something only for it to get changed significantly. Using a low-cost process makes it so that you can try a lot of variations cheaply, without any of the emotions involved.

Refine the page flow, not the pages
One of the highest leverage design  decisions you can make is not about the look of an individual page, but what happens before and after it. For example, you can take a multi-step process and condense it onto one page, or change the ordering of something so that you do something and then register, rather than the other way around. These kinds of design decisions ultimately focus on the order and flow of the user, rather than the look or interactions of any specific page. If you go with a low-fidelity, then it’s easy to draw lots of small pages and link them up in a flow, and do things like cross pages off, change the ordering of a funnel, and lots of things that feel natural when the prototype is very rough. Otherwise, it’s too easy to get caught in the details on the “right” way something works without exploring the options.

Work your way up from low-fidelity to high-fidelity
Of course, you want to make sure that you use the right process for the right job. So there’s nothing wrong with high-fidelity prototyping, especially when you are in the later stages and thinking about issues like branding, look and feel, and all those other details. One way to keep this process going is to have multiple rough prototyping checkpoints so that design decisions are constantly getting refined – maybe the first step is a sketch on a paper, the next is a rough mockup on the computer, then a detailed mockup, then a rough built-out version, and then iterate to the final product. These steps make it so that all the design decisions are well understood, refined, and debated all the way through.

Tools I recommend
Finally, a couple recommendations on tools for paper prototyping:

• Number 2 Pencil
• Giant art pad for drawings – you can get these at an art shop or office store
• Balsamiq (check out the video on the linked page)
• Macromedia Fireworks

In general, nothing beats pencil and paper, but that’s just me I’ve been told that for people who aren’t comfortable drawing, using tools like Balsamiq helps quite a bit.

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Seed funding and building out the initial team
One of the most exciting events for a startup is landing seed funding, which transforms a “2 dudes in a living room idea” into something with much more potential. I wanted to summarize a couple things that are relevant for this stage, learned from personal experiences and conversations with other entrepreneurs. This blog is targeted at startups who have raised their first $500k-1M of funding, which often leads to hiring 4-6 people – this first batch of folks is critical, of course.

Here’s a quick outline of some of the things I’ve encountered:

1. Hiring T-shaped people versus specialists
2. Try to get doers
3. More candidate flow solves a lot of problems
4. Interview for the actual work you’ll be doing, not skillset trivia
5. Raw intelligence is just one factor – don’t overestimate it

There are many more topics, of course, but this is a good start – let’s dig in:

Hiring T-shaped people versus specialists
One of the truisms in startupland is that everyone has to wear many hats – backend programmers might have to pitch in and do some feature work, designers might have to write some marketing copy, and the CEO might have to vaccuum the office Just as importantly, if you believe that startups are fundamentally undergoing a process to learn about their customer and the market, then you need people who are  versatile who can see distant connections between a variety of topics. So you want generalists, but a specific kind.

I’ve come to believe that the first batch of people you want on your team are going to be T-shaped, meaning they are broad in a bunch of different areas and deep in a particular one. The breadth of skills gives them enough common context that they can have conversations with anyone on the team about anything, but the depth gives them a source of knowledge that makes them vital to the team.

Testing for this can be as simple as asking a deeper set of questions when interviewing candidates, and asking them to do exercises that are outside of their stated skillset. Most engineering interviews are specialized enough that there are coding questions, but not many that I’ve seen also include an interview around product creation or UI design. Similarly, when discussing candidates, you’ll want to give equal weight to their depth area as their breadth areas.

Watch out for people who are so deep in one area that they seem to be overspecialized – it can be a signal for a lack of interest for pitching in on areas that might be vital for your team, or they may have nothing to do if your startup inevitably changes directions.

Try to get doers
It’s very important to hire people who are execution-focused early on. You just don’t have a lot of room for senior people or “philosophers” that don’t immediately contribute value in the product development process. When it comes to seniority, I’ve often liked to hire people who have recently had titles as team leads or directors, but nothing more senior. That way, you get people who are used to the responsibility of leading a team, yet are still low enough to the ground to have immediate impact. This is why people who are fresh out of consulting or banking backgrounds make for impractical partners – they are too focused on strategy and financials when you really should be focused 100% on concrete products and customers.

The other type that you find that’s not execution-oriented is the philosopher type. These folks often interview really well, are familiar with a wide spectrum of things and often experiment with new technologies all the time. The hard part about adding these folks to your early team is that they may be more interested in reading blogs and indulging themselves intellectually than really working hard on a team to get a lot of work done.

More candidate flow solves a lot of problems

For most seed stage startups, getting the first 2-3 people usually won’t be a problem – you’ll have people in mind, or people who are in your immediate group of friends who are easily accessible. What’s much harder is once you move beyond your immediate network, where you may find:

• People you want have jobs and aren’t interested
• As an entrepreneur, you know lots of entreprenurs who want to start something, not join something
• Lots of “OK” people who are interested, but who are hard to get jazzed about
• etc.

It’s easy to immediately get into a state where bars get lowered, things you don’t want to get accommodated are, and all sorts of other problems. Or you’ll have interviews where the person was OK but not great, and you really want the skillset.

All of this hand-wringing can be solved if you find a repeatable model for contacting qualified people and getting them in the door. I’ve found that when you start hiring for a new job role, it’s hard to figure out who a perfect candidate is – it isn’t until you see 10 candidates that you start to hone into what you really want and like. Figuring out the repeatable process that works for you is the hard part – but you want to find communities where your ideal candidates are already involved, and start talking to as many of them as possible. This may be Newgrounds for Flash people, or the Firefox extensions directory for browser folks.

Interview for the actual work you’ll be doing, not skillset trivia
I’ve previously written a bunch about my feelings on this topic, so I’ll just link here. The short of it is that I think most interview processes suck because they aren’t actually tests of what it would be like to actually work together. The ideal interview, imho, is just to interview, then work together for 2 months and do a checkpoint to see if it’s working OK. But because most people looking for a job aren’t willing to do that, having a 3-day “working interview” is a reasonable substitute as well.

Raw intelligence is just one factor – don’t overestimate it
All the young, energetic entreprenurs I know want to hire other people like them – high-horsepower people who work hard. As a result, you can organize an entire hiring process around intelligence, full of puzzles and brainteasers, and reward anyone who thinks quickly on their feet. I’ve found that this actually sucks as a minimum bar for hiring people – it’s just as important to evaluate things like passion in the area you’re working in, their reasons and goals for being at your startup, etc. The reason why you need to evaluate this stuff is that startups are really hard, and often take more time than you think they will – as a result, it’s important to understand peoples’ motivations to make sure it’s a good match from the beginning.

A couple of the things that are useful to evaluate:

• How do they see work fitting in their life? Does the style match? (Mornings vs late, hours, etc.)
• Level of process and improvisation – how are decisions made, how well are things spec’d and defined, etc.
• Mutual goals and motivations – money versus serving a goal versus learning versus others
• What stage of startups do they like? Seed, mid, later on? Why?
• Long-term goals – create a great lifestyle company, or try to go big?

I think questions like the above should get equal billing as testing for skillset – these are the kinds of things that impact long-term collaboration and performance as much as mastery of skillsets of knowledge.

Figuring out the alignment of these soft skills reminds me a lot of this great interview with John Doerr of Kleiner Perkins, where he discusses Missionaries versus Mercenaries – you can watch the video at your leisure below. Ideally, you find people who really understand and believe in the mission of the company – and if you have really smart mercenaries in the team, it may work well for when things are going well, but there’ll be significant issues if there are any hiccups (which there are bound to be some).

As a side note, this is often a problem with hiring metrics-oriented people, because their passion and interests aren’t as much in the value that’s created through the product as much as figuring out how to make the metrics go up. This can create a very revenue-oriented mercenary culture that leads to weird company vision problems. I think the ideal scenario is to find people who have a passion for the particular product you’re bringing to market, and then training them to be metrics-oriented, versus taking people who love numbers/data/algos and trying to train them to love a particular product area.

Comments? Any lessons to share?
If so, please comment below – would love to hear from other early stage startups and their lessons in hiring the first 10 or so people.

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I was always more of a Barren Realms Elite fan, but this picture was cooler!

And now you learn how I spent my childhood…
Some of the fondest memories from my childhood are playing BBS door games. Back before the web existed, I was a 10 year old kid in Seattle dialing into 3 or 4 different  different BBSes using a pirated version of Procomm Plus. There, I found that you could download all sorts of awesome products (though in 20 different parts, which you had to put back together), and more importantly, they had the ability to launch “door games” like Tradewars 2002, Legend of the Red Dragon, Barren Realms Elite, and a number of other games I grew to love. I spent so many hours tying up the phone line after getting back from school that eventually my parents banned me from playing these games – but that only convinced me to set an alarm for 3am to wake up in the middle of the night to play!

Furthermore, it became a critical thing in my life to get all my friends from school to also play these games with me. Together, we’d team up into a powerful, coordinated unit, and dominate the other players on the BBS and regional network. (Or at least that was the plan) Eventually years passed, I learned the internet was more than gopher, and I moved on to better and more powerful massively multiplayer games.

It’s obvious, in retrospect, that a lot of the door games that existed 20 years ago pioneered a lot of the same techniques that social games use today. Let’s drill into a bunch of these similarities, covering the following topics:

• Door games are the “apps” to the BBS platform
• Social gameplay with friends and neighbors
• Turn-based, RPG-like gameplay
• Low-tech graphical experiences, delivered as a persistent social experience

If I’m missing anything, please leave a comment! Anyway, let’s get started…

Door games are the “apps” to the BBS platform
First, let me describe what a BBS actually is – you can read a more official version on Wikipedia here. Anyone with a phone line, modem, and computer running the right software could start up a BBS. The software would tell the computer, when it received a call, to automatically pick up the line and start talking to the computer on the other end. On the other side, anyone could dial into the BBS with the right terminal software and once the connection was established, you’d get a screen that looked something like this:

On these BBSes, you’d typically find a couple different sections:

• Private communication (reading and writing messages)
• Public communication (message boards)
• File sharing (downloading and uploading)
• External applications, including Door games

The external applications were integrated with the BBS as described by Wikipedia:

The BBS software starts the external program, and the door system passes data back and forth between the door program, the BBS, and the remote user. To supply the door program with the user’s information (such as the user’s alias and the amount of time they had spent online), the BBS software creates a dropfile containing information for the program to read.

This “dropfile” typically contained all the user information, so rather than the standard API where the app asks for that information, instead it was provided in one big file. The door game would then parse this data and use it for the game. For the extra nerdtastic detail, you can go here for the dropfile specs.

Now of course this process of extending the BBS’s functionality isn’t as flexible as things are now – after all, you couldn’t just upload a new Door game to a BBS and get it running. You also couldn’t update your game and instantly propagate the new version out to all the users. But the central idea is still the same.

Social gameplay with friends and neighbors
One of the interesting properties of BBSes is that because they are all accessed by phone, and you don’t want to pay long-distance bills, you end up dialing into BBSes in your own area code. For me, that was 206, and it means that I was mostly gaming with people in my same regional area. Similarly, I convinced all my friends to also dial into the same BBSes and play games with me. For the games that had leaderboards and user-to-user interaction, it was easy to feel the same fun game-like motivations that make social gaming work today.

As an aside, while doing research for this blog post, I found this funny ASCII based dating site with Myers-Briggs based writing, and a Wall!

You can tell from the number of dudes on the screen above that the world of lonely nerds has not changed much over the last 20 years.

Turn-based, RPG-like gameplay
One of the big design challenges for any BBS game is that you want to encourage social gameplay, yet it can’t be real time. This is because, of course, people can’t be logged in to the same BBS simultaneously unless the BBS had a ton of different phone lines (not likely). As a result, each of the Door games had to support an social, asynchronous play style that allowed people to dial in one after the other and still engage.

The way that was done depended on the game, of course, but usually combined a mix of computer players (aka NPCs) and “slow” real-time action where each loop of action lasted a day. Then on any given day, you are given a number of turns which you can expend. Once you play these turns, then you need to wait until the next day to get more turns. This made it so that for a game like Tradewars 2002, you can log in, do your trading/mining/attacking, and interact with computer-controlled characters. If you encounter another player’s ship, then you can interact with them with the computer taking control of the other player, so when attacking them, they will automatically defend.

Some of these games played very much like RPGs, with levels, currency, monsters, swords, quests, and the usual mechanics. One of the most popular games was called The Legend of the Red Dragon:

Having the combination of social gameplay with the traditional RPG mechanics created a rich world that allowed for months of play time.

Low-tech graphical experiences, delivered as a persistent social experience
You may notice from all of these screenshots that these games are very, very basic. Many of my friends at the time criticized the simplicity of the gameplay, only to be  sucked in for social reasons Similar to the current incarnation of social gaming, the emphasis is not on graphics. The primary advantages of a persistent, continually updated world with social gameplay far outweighed the fact that downloadable single player games had much better graphics. Of course I still downloaded and played Wolfenstein and Doom when it first came out, but throughout that time, I stilled played BBS games.

If there’s one thing to be learned from the BBS games and their related cousins, MUDs, is that great social interactions can trump pretty much everything else. Of course the products that can deliver higher production values and great social experiences are even better off.

It was fun to write this article! Such a blast from the past. Hope you share your memories of BBSes in the comments

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(This blog was republished by the excellent blog Inside Social Gaming)