Andrew Chen Archives

Here’s more people to follow on Twitter!

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

When does Free stop and Premium start?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Graph of searches for “myspace”

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

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

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

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

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

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

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

Align the ad product with your site’s user experience

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

No credit card needed to receive L$ within minutes.

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

Price – 7.05

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

Purchase required to receive L$ within minutes.

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

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

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

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

Price – 3.78

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

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

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

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

Purchase required to receive L$ within minutes.

L$ awarded when you buy now.
Price – 19.80

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

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

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

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

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

Scenario A:

Scenario B:

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

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

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

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

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

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

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

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