@andrewchen

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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Today we have the first part of a fantastic two part series where Gambit, a microtransactions platform, is sharing exclusive data and analysis for the payments happening on their platform. The author, Susan Su (@susanfsu), is a writer, marketer, and Stanford alum who’s currently at Gambit Payments. She wants startups to make it big, and you to make more money. Enjoy! –Andrew

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