After a successful Facebook campaign a few months ago with the "Battle for the UK's Favourite University", involving a sponsored group that got over 100,000 students taking part and an online points based competition between universities to win a big party, O2 have launched a fresh campaign targeting university students on Facebook - "O2 Unlimited Orgy of Fun". Although using the page system, O2 have got some bespoke modifications from Facebook (for example the integration of a banner/app under the fan box), which shows Facebook's willingness to bend over for big brands with big money - just as they did for the March Madness application (which allowed 100 invites to be sent at a time).
It will be interesting to see if Facebook commoditise this service more, in the same way as MySpace do - MySpace have a number of packages depending on the Ad spend you are willing to promise, each one offering more and more bespoke features for your MySpace pages over and above what can be created externally.
In the current campaign, O2 are taking a big step forward - rather than having all the interaction online and very simple, they are setting challenges for teams of friends from universities to compete in, with quite big prizes for teams along the way, and the top four Unis all going to the O2 'Orgy of Fun' day. As companies become more confident with their social media activities, expect to see more integration of offline and bigger prizes offered - O2 are really leading the way here.
I logged on to Facebook this morning, saw that I had some new requests (as well as a load of old ones up there), and decided to have a flick through to see if there was anything interesting, when I saw this:
I just couldn't resist clicking. A mix of playing to my vanity (I'm one of her coolest friends!) and that possibility that I would be letting her down if I didn't accept created a strong desire to click, mostly in order to 'reward' my friend.
This highlights the huge value of the Facebook platform for viral growth, when done properly. It allows you to utilise the emotional connections between people in order to spread applications very fast. Although this is possible outside of the Facebook platform, it's access to the social graph and ability to control and personalise messages automatically means that it (and other social network application platforms) are vastly superior compared to platforms which don't utilise the social graph in the same way.
Unfortunately, the application inside required me to send invites out to see what ranking I am, with no choice to skip (which is now forbidden by the Facebook ToS, but there we go), and because I didn't want to spam my friends I didn't go any further.
So, what drove my response? I would suggest there were three main reasons - a fixed-action response, desire to reciprocate, and liking.
Fixed-Action Response
Many studies have shown that when you give a reason with a request, compliance goes up dramatically - from 60% to 94% when asking people waiting in a queue to use a photocopier to skip ahead of them (Langer, Blank, & Chanowitz, 1978). The remarkable thing is that this is still true even when there is no real reason - when the reason was 'because I have to make some copies', compliance still went up to 93%.
Robert Cialdini calls this a 'fixed-action response' - we have an automatic tendency to comply with requests when a reason is given, regardless of the validity of that reason. This is a really simple way of increasing compliance and great for viral requests like this.
Desire to Reciprocate
It is a common compliance/persuasion technique to give a gift, free sample or favour to someone in the knowledge that this will create a feeling of obligation to reciprocate. This can be very powerful - James and Bolstein (1992) found that mailing a $5 'gift' cheque with an insurance survey was twice as effective at getting people to return surveys as offering $50 as a reward for completing it.
In this instance, the invite request makes clear that my friend has nominated me as one of her coolest friends - quite a compliment. It then connects this compliment to a request to do her a favour in return by accepting her request. This is a clever way of linking the invite action to the request to install the application.
Liking
It is this factor which makes the Facebook viral system so powerful. Facebook, and other social networks, facilitate the linking up of 'weak-ties' (see my longer post on why weak-ties are so useful for marketing here) - people who you have some connection to (e.g. work mate, similar interest, mutual friends) which creates a much more trusting relationship than if they were pure strangers. The viral channels on Facebook then make it very easy to spread applications and other actions/notes/UGC etc across these weak-ties.
The most powerful offline use of the liking rule are Tupperware parties (and more recently Ann Summers parties), which uses the power of liking to directly sell products.
As an advanced method, research shows that the more similar we are to another person, the more we like them (Byrne, 1971). When utilising the weak-ties in a social network, then, you are going to get the highest conversion rate when you use the social graph data to encourage invites to be sent to people similar to the inviter. An obvious way of doing this was used by the Addicted to Scrubs app, which found the friends of users who had Scrubs mentioned as a favourite show but didn't have the app, and suggested to users that they should send invites to them. However a more subtle way could be to find the friends of users with similar age/gender/location/interests and simply put these at the top of the friend invite box.
"I got a phone call one day from a friend who had recently opened an Indian jewelry store in Arizona. She was giddy with a curious piece of news. Something fascinating had just happened, and she thought that, as a psychologist, I might be able to explain it to her. The story involved a certain allotment of turquoise jewelry she had been having trouble selling. It was the peak of the tourist season, the store was unusually full of customers, the turquoise pieces were of good quality for the prices she was asking; yet they had not sold. My friend had attempted a couple of standard sales tricks to get them moving. She tried calling attention to them by shifting their location to a more central display area; no luck. She even told her sales staff to "push" the items hard - again without success.
Finally, the night before leaving on an out-of-town buying trip, she scribbled as exasperated note to her head saleswoman, "Everything in this display case, price x 1/2", hoping just to be rid of the offending pieces, even if at a loss. When she returned a few days later, she was not surprised to find that every article had been old. She was shocked, though, to discover that, because the employee had read the "1/2" in her scrawled message as a "2", the entire allotment had sold at twice the original price!"
This is an extract from Influence, Science and Practice, by Robert B. Cialdini, showing how people equate value with price. Over the past couple of years a 'virtual item' economy has been cropping up - from Facebook's digital gifts, Habbo Hotel's virtual items, and Second Life's user-created economy. However, when I've mentioned some of the estimated figures and real money being spent on these to clients, many of them are incredulous - why would people spend money on a 'fake' gift??
The truth is, as human beings we equate price and cost with value. We have the general assumption that if something costs more, then it is worth more, and although this may seem shortsighted, it's actually an extremely valuable tool. If I wanted to buy a diamond, how would I know the different values? I have the choice of a) learning everything about diamonds so that I am able to personally value them b) paying an independent expert to accompany me shopping or c) going by price.
Now, diamonds are pretty, but I know I haven't got the time or inclination to learn everything about them, and paying an expert to accompany me shopping just seems a bit... over the top. Of course diamonds are only one product, but the same applies across the board. We have trained ourselves to use these kind of short cuts in many areas of life - rules of thumb. They help us operate in an extraordinarily complex world.
The same thing applies to virtual items and gifts - as soon as you put a price on them, that effectively becomes their value. As long as a buyer can't get the same gift for free next door, then the item has that real world value. This can carry over into gifts as well - if I receive a gift that I know is worth £1, then the value of that gift is £1, plus the value of having someone who cares about me enough to actually buy the gift and give it to me.
Although they don't release any figures, the recent move by Facebook to remove the 'gift of the day' from the homepage suggests that this is become a less meaningful revenue source - not a surprise when you consider that it's so easy to send free gifts to each other on Facebook with different applications. This is a problem for any business strategy revolved around selling virtual items - you need to ensure that someone else can't come in and undercut you with the same features. This has been a problem for Facebook because their gifts were essentially extremely simple 2d graphics. It hasn't been so much of an issue in the Second Life economy because building new items is complicated and requires significant knowledge, and isn't an issue at all in Habbo Hotel where they hold a monopoly over creating and selling items.
So, selling virtual items can work - but only if a virtual scarcity is created. If you can create this, either through the complicated nature of the virtual item or by holding a virtual monopoly, it can be a very effective business strategy.
A few days ago, the New York Times ran a piece about how two bloggers had died, and how another two were having health problems. They put this down to the non-stop stress of trying to build up a blog network, working 24-hours, and the associated lack of sleep and unhealthy lifestyle. I'd flicked through it at the time, noting with sadness that people had died trying to build their businesses, and hoping that Michael Arrington over at Techcrunch manages to sort himself out and get a proper work/life balance.
Yesterday Jason Calacanis wrote a post discussing the New York Times story - and pointing out that the fact that they were all building a blog network wasn't the trend, it was that they were all entrepreneurs, trying to build their own businesses and dealing with the stress of that. It's a great post by an experienced entrepreneur on how to build a business whilst maintaining your work/life balance and look after your stress levels, and really hit home for me - a couple of years ago I thought that the only way to be a successful businessman was to power work and never rest. I was working til 1am and getting up at 5am every day. After 6 months of this I came down with a bad case of appendicitis - bit of a blow to go from working non-stop all hours of the day to suddenly being bed-ridden, hospitalised and then recovering from surgery.
Since then I've kept more of a balanced load - I'm still working pretty much non-stop, but always ensure I take a bit of time for myself each day, get out early to train/go for a run in the morning, and get a reasonable nights sleep. If you're a fairly new or first time entrepreneur then this is something you need to start thinking about now -it's easy to think we're invincible, but unfortunately it's not true; it's not maintainable, and in the long run it's not good for the business, or you.
On Wednesday night I spoke at the Facebook Developer Garage in London on 'Jumping the Shark', based on mathematical analysis by Andrew Chen. See my presentation at the bottom of the post - I have written up the essentials of my talk below.
Slide 2: this is the 'shark fin', a graph showing the exponential growth and then equally fast fall in active users over time - this could be daily active users of a Facebook application, but could also refer to other time periods such as a week or month, and could apply to web sites or other viral applications.
Slide 4: Network saturation causes a slow down in new user growth, and eventual plateau. With Facebook applications, this can be seen easily by considering the following: if in each time period your current users send out 10 new invites, and you have a 10% conversion rate, then at 0% saturation all ten of the invites will be sent to new users, and you will acquire 1 new user for each current user in each time period. However, as the network becomes more saturated this conversion rate will lower - at 50% saturation, 5 of every 10 invites will go to users who either already have the application, or who have already rejected it. Thus, your 10% conversion rate will only apply to the 5 new users, so you'll get 0.5 new users for each current user in each time period.
Slide 5: You may think that, with 60m users, it would be almost impossible for any one Facebook application to hit its 'carrying capacity', i.e. full network saturation. However, the average user on Facebook does not have a social graph equally spread across the whole Facebook ecosystem. Instead, they will exist within 1-3 closely knit networks. The picture in the slide is a visual representation of my own social graph on Facebook, using an application called nexus. The large radial curve shows my former university, Durham, where you can see very heavy connections between all users. Likewise, the small curve shows my hometown, Malvern, which is equally interconnected.
If a developer seeds an application to their own social graph, it is likely to only gain traction in the networks he is heavily connected to. So, for example, the Durham University network has 24,000 users - if only 10% of users are likely to install your application, this leaves a total carrying capacity of any application only seeded to Durham University of 2,400. Of course, you could be lucky, and the application may spread to other networks, but the chance of this is much lower.
Slide 6: Once new user growth has slowed or plateaued, it is left to the retention rate to ensure that any application maintains a high active user base. The graph shows how at 99% retention rate the active user base steadily falls off once new user growth has stopped.
Slide 7: Although 50% retention rate may sound high, once your new user acquisition has slowed or stopped, this will create a dramatic fall in active users, down to almost nothing in the same time period it took for your userbase to grow to its peak - creating the shark fin.
Slide 9: There are a number of ways to prevent the 'shark fin'. The first is to ensure that you have the maximum carrying capacity possible - this means that an application must be seeded early into as many different and diverse networks as it can. For some applications you may be able to do this by seeding to fan pages - even if these only have a few thousand fans, they are likely to be from diverse networks, and are more likely than other users to install a relevant application. If you don't have access to a fan page, then advertising, either with banner ads or social ads around Facebook, or by using application advertising networks, is the only way to ensure that your application is widely seeded (you could also use traditional advertising or other channels to reach your current customers).
Once you've ensured a high carrying capacity, you then need to ensure that your application has a clear retention loop. The Facebook application platform is great for viral loops, with applications able to build in newsfeed stories and invite requests into the fabric of the application. However, although this is fine for growing the installed user base, to maintain a high active user base you must have a clear retention loop that keeps users coming back. This is a serious pitfall that many applications on Facebook have fallen into - and without a continuing active user base, there's no-one to market to or to click on adverts.
It's extremely important that you measure your retention rates from day 1, and test the effect of changes to the application on retention rate. Although the drop off is disguised by exponential growth during the initial phase, the drop off is still happening, and if you wait until active users have dropped off the other side of the shark fin then even if you fix the retention loop you will have less active users than otherwise. Thus, you need to ensure the retention loop is working as early as possible to ensure that you hit the maximum number of active users you can, and keep them.
If you're interested in the maths I'd advise you to check out Andrew Chen's post on this, especially his notes about cohort analysis for testing retention rate over time.
