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A short study of this internet site reveals that a hugely important factor for fulfillment in startup companies is finding ways to accumulate customers at a coffee cost. within the Business Models section, we checked out the right business model: Viral customer acquisition with good monetization. However viral growth seems to be an elusive goal, and only a really small number of companies actually achieve true viral growth.
In 2005, I invested during a company called Tabblo (acquired by HP in 2007), and had the great fortune to figure with an impressive entrepreneur, Antonio Rodriguez. Tabblo did manage to realize good viral growth, but round the same time YouTube was launched and managed to realize explosive viral growth. within the process of watching these two companies, we learnt several important things about virality. This post digs deeper into what it takes to realize viral growth, and examines the key variables that drive viral growth.
To give you a preview of this post, what you’ll learn is that there are two key parameters that drive how viral growth happens, the Viral Coefficient, and therefore the Viral Cycle Time. to completely illustrate the arguments, I even have included two spreadsheet models (embedded) that you simply can play with interactively to ascertain how viral growth works. there’s a risk with this level of depth, that some readers will find this too technical, and if you discover yourself reacting that way, may i like to recommend that you simply jump straight to the conclusion, which is under the heading Lessons Learned towards rock bottom of the article.
What we would like to know in these two models, is how the population of consumers changes over time. the primary model that we’ll build looks during a very simple way at how viral growth works within the marketing world.
The Viral Coefficient (K)
Imagine you’re starting a replacement company that plans to accumulate customers through viral growth. you’ve got several friends that you simply use to become your first customers, and that they successively start inviting friends to hitch , and people friends start inviting friends, etc.
The model at this stage has the subsequent inputs:
Variable NameDescriptionExample ValueCusts(0)Initial set of Customers10iNo of invites sent out be each new customer10conv%The percentage of invites that convert into customers20%
The first thing that we’d like to calculate is that the number of latest customers that every existing customer is in a position to successfully convert. This seems to be a particularly important variable, and is understood because the Viral Coefficient. The formula to calculate the viral coefficient is pretty simple: multiply the amount of invitations by the conversion rate.
KViral CoefficientK = i * conv%
Now lets take a glance at how K affects customer growth as we undergo the primary cycle of viral “infection”. Our initial 10 customers will each send 10 invitations, and successfully convert 20% of these (i.e. 2 new customers each). therefore the total customers after the primary cycle are going to be adequate to the starting 10, plus the new 20, which equals 30.
To fully understand the model, it’s useful to seem at the second, and subsequent, cycles of growth. within the model above, only the new customers that were added within the prior cycle send invitations. this is often because it’s highly unlikely that the whole population will still send invitations every cycle. Whenever I even have checked out other blog articles or formula for Viral Growth, they seem to possess gotten this a part of the calculation wrong.
Understanding the impact of the Viral Coefficient
Now that we’ve the model built, we will play with the variables to ascertain what effect they need . within the spreadsheet above, attend cell B11, and alter the Conversion rate for invites (conv%) to five . this may make the Viral Coefficient but 1. Now check out what that did to your increase . rather than continuing to grow, it grows to twenty people, then stops.
What this tell us is extremely interesting:
The Viral Coefficient must be greater than 1 to possess viral growth.
Further twiddling with the spreadsheet will show that increasing the viral coefficient by increasing the amount of invites sent out, or the conversion rate, features a nice impact on how the population grows. do this out by changing cells B10 and B11 within the model above. afterward we’ll mention the way to design your application to maximise these values.
The Second Important Variable: Viral Cycle Time
Antonio Rodriguez built Tabblo round the same time that YouTube was built. Both sites were viral, but while Tabblo was reasonably successful, YouTube exploded and amassed users at a rate that had not been seen before on the web . What was happening here?
To answer this question, we’ve to seem at the Viral Cycle Time,(which we’ll ask in formulas as “ct”).
The full viral cycle involves several steps that employment during a loop:
The Viral Cycle Time is that the time that it takes for this cycle to finish .
In YouTube’s case the Viral Cycle Time was extremely short: a user would come to the location , see a funny video, and immediately send the link on to their friends. Tabblo, on the opposite hand, had a way longer cycle time. A customer would post some photos on the location and invite their friends. the buddies might see the photos on Tabblo, and just like the experience and choose that they might use the location subsequent time they took photos they wanted to share. However, that’s where the matter came in: it could take months before they next took photos, and decided to share them.
Later on this post, we’ll mention the way to optimize Viral Cycle Time — (see Lessons Learnt).
How Viral Cycle Time affects growth
To model Viral Cycle Time’s effect on growth, I searched the online , high and low, trying to find a pre-defined formula. To my great surprise, there was no formula that I could find that correctly calculated customer growth, and showed the impact of Viral Cycle Time. What was also surprising, was that I did find several blogs showing formulae for viral growth, but in every case, they seemed to make an equivalent mistake, which was assuming that the whole customer base would continue sending out invitations for each cycle. So I collaborated with my partner, Stan Reiss, who seems to be an entire lot smarter than i’m , and he helped me develop the fomulae that are utilized in the more sophisticated model for viral growth below:
A quick check out the table that shows the effect of varying the Viral Cycle Time shows that customer growth is dramatically suffering from a shorter cycle time. for instance , after 20 days with a cycle time of two days, you’ll have 20,470 users, but if you halved that cycle time to at least one day, you’d have over 20 million users! it’s logical that it might be better to possess more cycles occur, but it’s less obvious just what proportion better. a fast check out the formula tells the entire story. The Viral Coefficient K is raised to the facility of t/ct, so reducing ct features a much more powerful effect than increasing K.
Lessons Learned
There are an outsized number of interesting lessons to find out from the above models:
Unless you’ve got a Viral Coefficient that’s greater than 1, you’ll not have true viral growth.
The most important factor to increasing growth isn’t the Viral Coefficient, but the Viral Cycle Time (ct) which should be made as short as possible. this may have a dramatic effect on growth.
The second most vital area to focus is that the Viral Coefficient (K). Anything that you simply can do to extend the amount of invitations sent out, and therefore the conversion rate, will have a big effect on growth.
In addition to the above lessons that come from the model, there are another important observations:
Virality isn’t a marketing strategy which will be executed by the marketing department. it’s to be built into your product right from the start . this is often a function that must be thought through by the merchandise designers and developed by the engineers.
The most viral products are people who only work if they’re shared. for instance , Skype only worked within the youth if you bought your friends on to Skype, otherwise you had no thanks to call them. If you’ve got an application today, believe how you’ll make it social, where it might work better by sharing data with friends/co-workers. that gives an excellent incentive for patrons to ask their friends/colleagues to use the appliance .
To make the Viral Cycle Time as short as possible, we will apply an equivalent thought process that we use in Building a Sales and Marketing Machine, where we glance at what are the purchasers motivations and negative reactions as they flow through the viral cycle. for instance , once I reach the stage where I even have to enter my friends addresses, i will be able to not bother to try to to very many if I even have to seem them up in another program, and replica and paste them one-by-one into the browser. you’ll solve this problem by providing me with Facebook Connect integration to ask my Facebook friends, and an adapter to import my email contacts. (Check out the “Share This” button on the left side of this post as an example of how this will be done.) accessing email contacts is straightforward with web mail clients like GMail, etc. — but harder with Outlook. However viral products like LinkedIn have created Outlook adapters that you simply can download. it’s also feasible to urge at that information via Outlook Web Access (OWA) provided you’ll affect the safety concerns.You should even be trying to find ways to encourage customers to ask people at various junctures in their use of the appliance . And in fact , you ought to be asking yourself the question: is that the value proposition of your product really that compelling that your customers will want to share it with others? Another good way to extend virality is to incent customers with a gift for each customer they successfully convert. Since this will end in a private feeling guilty that they’re making money off their friends, the simplest thanks to do that is to also provide the friend that’s receiving the invitation with an equal incentive. Now your customer will desire they’re doing their friends a favor.
Consider leveraging viral platforms like Facebook, which have inbuilt social features to let friends know what apps you’re using. The wall, and standing updates provide an excellent way for his or her friends see your app.
Use A/B testing to work out which approaches and artistic presentations are becoming you the very best conversion rates.
If you’re successful in creating a viral model with very short cycle times, be careful for what can happen. Several companies that are lucky enough to realize this are shocked by the big got to scale server capacity. Fortunately with cloud computing offerings like Amazon EC2 and S3, it’s easier than within the past to scale on demand.
Hybrid Viral Models
Many entrepreneurs reading this post will realize that they’ll not have the means to realize true viral growth (where they need a Viral Coefficient of greater than 1). instead of abandoning , it’s worth considering a hybrid viral model. within the hybrid viral model, you create up for the shortfall in customers by acquiring those through another means like paid search, or SEO.
Model Limitations
The model above is pretty simplistic and does not take into consideration several real world phenomena:
What happens when you grow so fast that you start to saturate the population. This has happened to several Facebook app developers. They experience very rapid growth, and then suddenly the growth dies. Andrew Chen has written a great blog post about this: Facebook viral marketing: When and why do apps “jump the shark?”. (Side note: I don’t believe that the equation that Andrew puts forward for simple viral growth is correct, as it assumes that the entire population will continue sending out invitations at each viral cycle. However his work on saturation of the population is very relevant for highly successful viral apps.) In case you are interested in where the term “jump the shark” came from check this out: Wikipedia: Jumping the shark.
What happens if you have attrition in your customer base over time. An easy way to extend the model to take this into consideration would be to add a variable to model Attrition Rate as a percentage of the entire installed base at each cycle, and simply subract this from the total population at each cycle. This topic is nicely covered in this blog post by Andrew Chen: Is your website a leaky bucket? 4 scenarios for user retention.
The customers that you have may send out more than one set of invitations beyond the initial set.
Kevin Lawler very kindly created a post explaining how to derive the formula for viral growth used in this post: Virality Formula.
Acknowledgements and Thanks
My thanks to Antonio Rodriguez, the founder of Tabblo, who got me started on thinking about this topic several years ago. Also to Andrew Chen, whose writings on this topic are excellent. And to my partner Stan Reiss, who took my simple logic and turned it into an elegant mathematical formula.
Acknowledgements and Thanks
My thanks to Antonio Rodriguez, the founder of Tabblo, who got me started on thinking about this topic several years ago. Also to Andrew Chen, whose writings on this topic are excellent. And to my partner Stan Reiss, who took my simple logic and turned it into an elegant mathematical formula.
