I recently had the pleasure of connecting with TS Ramakrishnan.  Super smart guy and clearly one who know a thing or two about how to launch app platforms/APIs for social networks.

TS has an interesting short-list of four requirements for a successful social network app platform:

1. Users — the social network must be large enough to bring lots of distribution to app publishers
2. Language — platform must be backwards compatible with existing web development languages/frameworks
3. Marketing — the social network must expend significant effort to recruit, retain, and support app developers
4. Transactions –  the social network must facilitate secure transactions (i.e., payments)

I found his last requirement to be really thought-provoking…  It sounds like a pre-requisite for ecommerce transactions, but we haven’t seen a whole lot of apps pursue ecommerce models yet on social networks.  The vast majority of social apps are 100% ad-supported, but we’re starting to see some “freemium” models develop.  For instance, Slide’s SuperPoke app has Premium poke actions that users can get access to via a monthly subscription.

Even in an advertising-dominated economy, I wonder if the social apps on Facebook might monetize more efficiently if Facebook were to facilitate the payments/collections process between ad networks and app publishers?  E.g., would social ad networks operate more efficiently if ad units were rendered in FBML and Facebook provided standardized tools for measuring impressions, clicks, follow-on actions, pathing; and financial transaction support for buying/selling social ads…

A lot of folks have asked for more details on the way we measured and optimized viral app growth in the Stanford class I co-taught recently. So here’s a bit more info on methodology for measuring virality and what it means for an app to “go viral.”

K-factor and R-zero

Terms like “K-factor” (contagion) and “R-zero” (reproduction rate) are often used to describe the growth rate of viral apps. These terms come from the fields of medicine and biology — they’re originally intended to describe the spread of of viral diseases, but they’re nice analogies for how web/SN apps grow. Some would even describe widgets and apps as “diseases” that have “corrupted” popular social networks like MySpace and Facebook! Of course, having worked at Slide and authored some FB apps of my own, that’s clearly not my belief… So, read on if you’re interested in viral apps!

Whether we’re talking about apps or diseases, the key factors in determining virality are the same:

1. Distribution: how many people, on average, will an “infected” host make contact with while the host is still “infectious”?
2. Infection: how likely is a person, on average, to also become “infected” after contact with a viral host?

If you multiply these factors together, that’s your viral growth rate (or “K” or “R-zero” or “viral coefficient”). The product of these factors answers an important question:

“How many people will be infected by a single viral host while the host remains infected?“

With real-world viruses, the infectious period has very dramatic outcomes. E.g., a host remains infectious until either the virus kills the host or until the host’s immune system fights off the virus. If K=1, then the host basically passes the virus on to one new person before either the host dies or the virus is expelled. Either way, if K=1, then the host exactly replaces him or herself in the population of infected people before becoming non-infectious.

Hopefully, the growth of social apps will never involve physical death or illness! [Disclaimer: No readers were harmed for the writing of this post.] Instead, we would consider a host to be “no longer infectious” if they either uninstall the app or stop actively using the app. Using that definition, an app with a K-factor of 1 will have a userbase in steady-state - no growth, no decline, just flatline; where every current user replaces themselves before leaving the userbase. K>1 means an app is growing its userbase virally (exponentially). And, conversely, K<1 means an app’s userbase is exponentially decaying.

With these factors in mind, designers of viral applications have four levers to pull on in order to increase virality:

The Four Viral App Objectives

• Increase the percentage of “active hosts” who actively make contact with uninfected people
• Increase the contact rate for each active host (average number of contacts per time period)
• Increase the duration of each active host’s infectious time period
• Increase the likelihood that contacts turn into infections (i.e., infection conversion)
  

Sidenote on app metrics

Note that this also implies that in order to affect any of these, you, as an app developer, need to be able measure each of these stats for your userbase. You can’t tell if you’re driving any of these numbers up (or down) until you know how many contacts/invitations each of your users sends out per day/week/month that they have the app installed; how many days/weeks/months each of your users tends to keep the app installed; and what the conversion rate from a contact/invite into a new infected user is.

Collection and analysis of metrics for social apps is a meaty topic in and of itself, so we’ll leave that for another day. But for now, it should suffice to say that it’s really important to have an effective way to collect statistics on what your users are doing with your app!

Assuming that we’ve got a reliable way of collecting metrics, here’s a quick list of some techniques for achieving each of the four viral app objectives.

Some example methods for optimizing virality

Active Hosts

• Require users to invite more people to join the app before they can view/use the app. Typically paired with premium or high value content. E.g., “Invite 10 friends in order to unlock this pr0n video in high-definition” or “Invite 15 friends to see how who has a crush on you.” Some users complain about this tactic, but you may be surprised at how many users will effective. (just kidding about the pr0n, kiddies — that stuff doesn’t fly on most social networks.
• Opportunism — you can’t always predict how people will utilize an application, so give your users multiple ways to share your app. Ideally, every app pageview should contain one or more ways for a user to share the app (and thereby become an active viral host).

Contact Rate

• Create incentives for inviting more people. E.g., “invite 10 more friends to level up and become a Black Belt Ninja”
• Specific requests tend to work better than vague encouragements. E.g., don’t just ask users to “please invite friends”, specifically ask for a number, “invite 10 friends” (don’t laugh, it actually works!)
• Simplify, simplify, simplify. If your #1 goal is to go viral, then that should be the #1 action-request that “pops” out to a user. Make it easy to invite more people. Utilize address book importers. Auto-select large(r) distribution lists for invitations. Basically, minimize the amount of hunting-and-clicking it takes to get a user through your invitation process. Ideally, it should just be 1 click.

Activity Duration

• User-to-user messaging is a great way to keep users coming back to an app. If pokes, walls, comments or private messaging fit into the context of your app, you should seriously consider building those in.
• User generated content and media — in general, apps that have some form of UGC/media built into them (music, photos, videos, drawings, etc.) do a better job at drawing repeat visits. I’d also group collaborative filtering functionality in this bucket — e.g., ratings, rankings, top playlists, “most viewed” lists, etc.

Infection Conversion

• Social context — when you’re writing the content/copy for your app invitations, be sure to keep in mind the fact that all your app invitations are occurring in the context of a social relationship between two friends. Use that knowledge as you phrase every call-to-action and craft each sentence to reinforce the social relationship and play on influence mechanisms between these two friends.
• Images and buttons — beyond writing the actual content/copy, app authors should also experiment with design and layout of their invitations. Some top tips include: use buttons instead of plain text links; and use images of people to draw the eye.

Of course, the list above is not exhaustive, it’s just a sampling of top-of-mind viral engineering techniques. If you have other favorite/top tips for tweaking virality, please post a comment below.

A note on prioritization:

In general, all other things equal, it’s most effective to pull on the Contact Rate and Activity Duration levers first — followed by the Host Activation and Infection Conversion.

By definition, your Host Activation and Infection Conversion rates are capped at 100% — the best you can do is to get 100% of hosts to invite other people, or 100% of contacted users to become infected. In contrast, the Contact Rate and Activity Duration are theoretically unbounded. (Well, I guess all human users must eventually expire, but I haven’t seen any Facebook apps that specifically optimize on age of users, yet!!) In any case, for our purposes, the total number of viral contacts initiated by your userbase is theoretically unbounded.

So as an app developer, you should explore the upper limits of how rapidly you can grow your viral contacts before circling back to optimize conversion rates. E.g., if you think of each of your current users as an “infected host”, then your first priority should be to get a maximal number of invites/contacts sent out by each of your hosts while you have them on the app.

Benchmarks

Just for comparison, I’ve included some common ranges for Host Activity, Contact Rate, Activity Duration, and Infection Conversion below. These are derived from my own experience with Facebook apps, observations of my Stanford Facebook class’es apps in Fall 2007, and also observations from companies that I have worked at or advised.

Active Hosts

• App age is an important consideration — newly launched apps will tend to have more active hosts
• Anything above 50% is pretty good for early-stage apps
• Single-step funnels perform best for maximizing host activation (e.g., select and invite friends from a single page, ideally the FIRST page that a potential new host sees)
• In general, minimize the number of steps/pages that you ask hosts to go through in order to invite their friends. Each additional page/step in a funnel will drop 50% to 60% of users, so each step in a funnel carries a very steep penalty. (Note this is a LOT steeper drop off than the rule of thumb 33% dropoff for page-to-page conversion rates that ecommerce or content sites see!)

Contact Rate

• For Facebook apps, 15+ total invitations per user is very good (Note that Facebook imposes a daily limit of 20 invitations per app per user)

Activity Duration

• Activity rates and user tenure will vary widely, depending on the purpose and design of app.
• Healthy ranges are typically between 5% and 40% of an app’s userbase will be active on any given day. (Starting high for young apps and then decaying over time for older apps)
• Good retention practices should generate 8+ repeat visits per month per user

Infection Conversion

• Expect net conversion rates from invites-to-infections in the range of 5% to 8%

K-Factor / R-zero / Viral Coefficient

• Ranges between 1.4 - 2.1 (or higher) are typical for apps experiencing “hot” viral growth

Summary

OK, so good luck to all you app developers out there seeking app virality! As you can see, none of this stuff is “secret sauce” or anything — you can readily view all of these techniques in action on Facebook, MySpace and other social network sites today.  Still, I hope that summarizing this stuff in one place is useful and many apologies in advance if I’ve misrepresented anything, especially the biomedical stuff.  (Is there a doctor in the house?!)  Please leave comments or corrections below!

And, of course, if you’ve got some viral ideas or would like to collaborate on viral apps, drop me a line: yeelee at gmail (or connect to me on Facebook, LinkedIn, etc.)

Mark Davis of DFJ Gotham Ventures posted this nice summary list of six types of risk that venture capitalists typically examine when evaluating a potential investment.

1. Management Risk
2. Product Risk
3. Revenue Model Risk
4. Market Risk
5. Competitive Risk
6. Partnership Risk

Read the full article here

I mentioned AlphaBlox in my last post and that made me think of something I learned from Michael Skok (CEO and Founder of AlphaBlox, now Partner at North Bridge Venture Partners).

Michael had a great framework for explaining how each person’s daily activities fit into the larger company objectives. He called it “VMGOSPA”, an acronym for the following framework:

• Vision — The company’s long term vision. E.g., “Acme Widgets is the world’s leading provider of basket weaving technology”
• Mission — Short-term (1-2 year) mission statement that builds toward the company Vision. E.g., “Acme Widgets’ mission is to dominate equipment sales in the fastest growing segment of basket weaving technology: underwater basket weaving”
• Goals — Immediate goal statements that build towards the Mission. E.g., “Successful completion of human trials for Acme’s innovative noninvasive underwater basket weaving equipment”
• Objectives — Current objective statements for executives, business units, functional departments, or teams that collectively achieve the company’s Goals. E.g., “The R&D department’s Q1 2008 objective is to produce 20 units of UBW equipment for trials.”
• Strategies for achieving Objectives
• Plans for implementation of Strategies
• Actions on a day-to-day basis that execute the Plans

Now, the seven layers of VMGOSPA clearly will not apply to all companies. Small companies and startups, especially, may not have enough team members to subdivide and delegate actions to!

The key concept here, though, is absolutely critical to scaling up an organization: every individual’s daily activities should aggregate up to achieve the company’s mission and thereby propel the company towards its vision. The way to do that is to visualize and define each person’s and team’s objectives in a recursively nested structure.

I had the pleasure of hearing Avinash Kaushik, Google’s analytics evangelist, speak when he came to our CS377W class at Stanford this quarter (the “Stanford Facebook class“).  He’s an amazing speaker, really breathing life and purpose into the too-often dry topic of web analytics.

He’s promoting a new way of looking at web analytics, what he calls “Web Analytics 2.0″.  Avinash’es central message is that analytics cannot stand alone as a decision driver in organizations; rather analytics need to be considered in the context of additional data (from customers, competitors, and other internal sources) in order to drive rational decisions.

Avinash has a brilliant decision framework, consisting of the five decision inputs that should be considered in order to gain insight into customer behavior and drive optimal decisions.   He calls this “The Five Pillars” and here’s the cliff’s notes summary:

1. Clickstream — Typical web analytics can give you data on unique viewers, pageviews, session times, browser types, and geolocation.
2. Multiple Outcomes Analysis — The payload for most web analytics tools is goals/funnels and conversion rate analysis.  Having clearly defined website goals is necessary and a great starting point, but not sufficient for true user insight.  Analysts should recognize that users and companies may have many different reasons for visiting or hosting a website.  All those objective outcomes need to be measured to see if the site is really driving the desired outcomes.
3. Experimentation & Testing — In it’s simplest form, this means A/B testing the design of your website, including text, graphics, buttons, banner ads, everything.  Some free tools like Google Website Optimizer actually automate the process of running multivariate tests to help you quickly find the optimal.
4. Voice of the Customer — Even after taking your best stab at Multiple Outcomes Analysis (#2, above) ask website visitors through surveys: (1) why are you here?; (2) were you able to achieve what you came for?; (3) if not, why not?  The results can be tied back to analytics data and may reveal customers’ true motivations.
5. Competitive Analysis — Your site does not exist in a vacuum.  Your competitors may be running campaigns or launching products/features that are impacting your site’s performance (could be either up or down).

An important theme that runs throughout Avinash’es comments but doesn’t explicitly show up in his framework is:

• Analysts should not be the only people in an organization who see all this information.  The process of gaining customer insight is becoming more and more automated and this information can and should be put into the hands of more people throughout each organization.

This is really, really powerful stuff — everything I’ve seen in my experience at AlphaBlox, PayPal, and Slide confirms that (1) companies are hungry for this kind of analytically-driven insight; and (2) teams that really LIVE this analytical process are winners — when people talk about “cracking the code” on how to continuously improve a web-based business, this is analytical process is really what they’re referring to.  The early PayPal team established a metrics-driven culture that did a good job at upholding the Five Pillars (though we didn’t call them that at the time).  And the Slide team is doing an awesome job at this, using a slightly different but equivalent analytics framework.  The information is out there…  Avinash does a great job packaging it up, now companies need to really internalize his message.

If any of this strikes a chord with you at all, you should do yourself a big favor and read Avinash’es entire post (and watch the video!) on this topic:

http://www.kaushik.net/avinash/2007/09/rethink-web-analytics-introducing-web-analytics-20.html

He’s even got a primer at the bottom of his blog post on how to get started with this analytics methodology.

Setting objectives for yourself and for others is a critical organizational function.  This will be painfully clear to anyone who’s ever sat in a team meeting where some “critical corporate goal” was described but no specific actions were assigned and everyone left the meeting wondering, “Ummm, so what am I supposed to do now?”

The SMART framework helps make objectives crystal clear so that anyone who is on the assigning or the receiving end of a SMART objective really understands exactly what actions are going to take place, by when, and how to measure success.

SMART is an acronym for:

• Specific: is the objective described in concrete, actionable detail?
• Measurable: what quantitative measurements will tell us when the objective has been achieved?
• Attainable: is the objective really achievable within budget and schedule constraints?
• Results-oriented: what tangible work output does the objective produce?  (i.e., not just conversations and ideas)
• Time-driven: what is the due date for the objective?

I think the SMART framework for writing and communicating objectives is invaluable — it applies at all levels of any organization, whether setting corporate objectives for a business unit, specifying product initiatives/featuresets, or day-to-day management of individuals.

(image from Fractal.org)

People exhibit a huge range of emotions and it can be a bewildering challenge to ascribe unique meaning to each emotional state that a person might find themselves in. A framework for analyzing emotional states can help us understand emotions by decomposing them into underlying dimensions.

Three key dimensions appear to be:

1. Valence: positive vs. negative
2. Activation: ready-to-act/aroused vs. relaxed
3. Power: dominant vs. submissive

We can describe almost any human emotion using these three dimensions or axes. E.g., “admiration” could be considered a positive, aroused, submissive emotion. Or “boredom” could be considered a negative, relaxed, dominant emotion.

The “valence” of emotions is usually pretty straightforward for us to immediately grasp.  “Activation” and “power”, though, may not typically spring to mind for most people when trying to analyze emotions.  Yet thinking about the activation and power dimensions of any emotion can help us recognize and adapt to emotionally-driven urges and impulses.

Anyone who has been involved with launching a website knows that there are lots of design factors to consider. Even a marginally sophisticated website will require development and coordination of multiple web page layouts, graphics (visual assets), text (marcomm or copy), server code, maybe some browser-side code (javascript), database tables, etc.

It can be a lot of stuff to consider and anyone responsible for launch of a product (e.g., product manager, product marketing manager, or a business unit lead) needs to think about “product design” from multiple levels.

Personally, as a product manager, I like to think about “product design” as the sum of five design perspectives:

1. service design
   • what user objectives are we trying to achieve?
   • and therefore, what features/functions do we need to support?
2. interaction design
   • what (minimum number of) logical steps do we need to lead the user thru in order to achieve their objective(s)?
   • how is a user going to navigate (link to) those steps?
3. page layout design
   • what information does the user need and how to present it?
   • what does the user see in their browser window at each “step” in the interaction?
   • what visual assets and marcomm/copy do we need to provide at each step to drive the actions that we want?
4. database design
   • what data do we need to collect from the user during the interaction?
   • how are these data going to be efficiently stored and retrieved (queried)?
5. technical architecture design
   • what software components (either 3rd party or homegrown) will be needed to deliver this service?
   • how will the code be organized to deliver this service?
   • what hardware will be needed?
   • how will the hardware be physically arranged and connected?

That’s what I would consider to be the bare minimum set of design perspectives to cover just to ship a website that isn’t fundamentally broken. E.g., if all these designs simply do not conflict with each other, then you’ve got a site that basically works. To set a higher bar, then all these designs should mesh and reinforce each other to achieve some highly valuable customer objective in a way that really captivates and delights the user.
I’m sure we could add more to the list above (e.g., brand design, design for testability, etc.) and we could sub-divide each perspective into finer sub-categories. Or we could chunk them up differently, too (e.g., Frank Spiller’s 10 Types of Design).

And if we expand the scope of design to include hardware products, then that also opens up all sorts of other perspectives:

• industrial design
• design for manufacturability
• design for assembly
• design for serviceability
• materials design

etc.

Point being that successful product design requires looking at a product from multiple perspectives!

READ THIS POST! 

Robert Cialdini’s insightful book Influence: The Psychology of Persuasion revealed how six “weapons” of social influence can be put to use to persuade people into taking actions or exhibiting behaviors.

The six weapons include:

1. Reciprocity - returning a “favor”
2. Commitment and Consistency - honoring a previous agreement or statement
3. Social Proof - do what other people are doing
4. Authority - do what the person with the highest title/rank, the fanciest car, or the nicest clothes says
5. Liking - follow the advice/instructions of people you like or are attracted to
6. Scarcity - perceived scarcity generates demand

The weapons are listed in order of their typical strength. Their ability to influence or persuade people varies greatly on the individuals involved and the situation. They operate most strongly under conditions of great uncertainty — e.g., when people are unsure of what the “right” answer is, these six influencers can operate essentially as “fallbacks” or mental short circuits.

More on Robert Cialdini

“The Five Forces” are Michael E. Porter’s framework for assessing the level of competitive intensity industry participants should expect to see. Highly competitive industries are likely to produce lower average profitability for participants in that market. Useful for rationalizing market entry or market exit decisions. E.g., competitors should seek out markets where the Five Forces are less severe, and exit markets with strong pressure from one or more of the Five Forces.

This is probably the first formal “strategy” framework that many of us were ever taught in business classes or corporate management training. It’s a classic!

The Five Forces include:

1. Bargaining Power of Customers — large or concentrated customers have more bargaining power vs. a pool of many smaller, disparate customers
2. Bargaining Power of Suppliers — large or concentrated supplier base will have more bargaining power vs. a pool of many smaller, interchangeable suppliers
3. Threat of New Entrants — low barriers to entry create high threat of new entrants; high barriers (e.g., intellectual property protection, large financial startup costs, or scarcity of qualified human resources) can lessen this threat
4. Threat of Substitute Products or Services — presence of many alternative products/services creates high threat of substitution
5. Intensity of Competition within an Industry — all else equal, intensity of competition increases when any of the first four forces increases

This framework is often expanded into “Six Forces” with the addition of “Government & Legal Regulations” as a sixth force that can affect the intensity of competition.

More info on Porter’s Five Forces from Wikipedia