Lessons Learned – Viral Marketing




A short study of this web site reveals that a hugely important factor for success in startup companies is finding ways to acquire customers at a low cost. In the Business Models section, we looked at the perfect business model: Viral customer acquisition with good monetization. However viral growth turns out to be an elusive goal, and only a very small number of companies actually achieve true viral growth.

In 2005, I invested in a company called Tabblo (acquired by HP in 2007), and had the good fortune to work with an outstanding entrepreneur, Antonio Rodriguez. Tabblo did manage to achieve good viral growth, but around the same time YouTube was launched and managed to achieve explosive viral growth. In the process of looking at these two companies, we learnt several important things about virality. This post digs deeper into what it takes to achieve viral growth, and examines the key variables that drive viral growth.

To give you a preview of this post, what you will learn is that there are two key parameters that drive how viral growth happens, the Viral Coefficient, and the Viral Cycle Time. To fully illustrate the arguments, I have included two spreadsheet models (embedded) that you can play with interactively to see how viral growth works. There is a risk with this level of depth, that some readers will find this too technical, and if you find yourself reacting that way, may I recommend that you jump straight to the conclusion, which is under the heading Lessons Learned towards the bottom of the article.

What we want to understand in these two models, is how the population of Customers changes over time. The first model that we will build looks in a very simple way at how viral growth works in the marketing world.

The Viral Coefficient (K)

Imagine you are starting a new company that plans to acquire customers through viral growth. You have several friends that you use to become your first customers, and they in turn start inviting friends to join, and those friends start inviting friends, etc.

The model at this stage has the following inputs:

Variable Name Description Example Value
Custs(0) Initial set of Customers 10
i No of invites sent out be each new customer 10
conv% The percentage of invites that convert into customers 20%

The first thing that we need to calculate is the number of new customers that each existing customer is able to successfully convert. This turns out to be an extremely important variable, and is known as the Viral Coefficient. The formula to calculate the viral coefficient is pretty simple: multiply the number of invitations by the conversion rate.

K Viral Coefficient K = i * conv%

Now lets take a look at how K affects customer growth as we go through the first cycle of viral “infection”. Our initial 10 customers will each send out 10 invitations, and successfully convert 20% of those (i.e. 2 new customers each). So the total customers after the first cycle will be equal to the starting 10, plus the new 20, which equals 30.



(In case the model above does not appear, click here to download the spreadsheet.)

To fully understand the model, it’s useful to look at the second, and subsequent, cycles of growth. In the model above, only the new customers that were added in the prior cycle send out invitations. This is because it is highly unlikely that the entire population will continue to send out invitations every cycle. Every time I have looked at other blog articles or formula for Viral Growth, they appear to have gotten this part of the calculation wrong.

Understanding the impact of the Viral Coefficient

Now that we have the model built, we can play with the variables to see what effect they have. In the spreadsheet above, go to cell B11, and change the Conversion rate for invites (conv%) to 5%. This will make the Viral Coefficient less than 1. Now look at what that did to your population growth. Instead of continuing to grow, it grows to 20 people, and then stops.

What this tell us is very interesting:

The Viral Coefficient must be greater than 1 to have viral growth.

Further playing with the spreadsheet will show that increasing the viral coefficient by increasing the number of invites sent out, or the conversion rate, has a nice impact on how the population grows. Try this out by changing cells B10 and B11 in the model above. Later on we will talk about how to design your application to maximize these values.

The Second Important Variable: Viral Cycle Time

Antonio Rodriguez built Tabblo around 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 Internet. What was going on here?

To answer this question, we have to look at the Viral Cycle Time,(which we will refer to in formulas as “ct”).

The full viral cycle involves several steps that work in a loop:

The Viral Cycle Loop

The Viral Cycle Time is the time that it takes for this cycle to complete.

In YouTube’s case the Viral Cycle Time was extremely short: a user would come to the site, see a funny video, and immediately send the link on to their friends. Tabblo, on the other hand, had a much longer cycle time. A customer would post some photos on the site and invite their friends. The friends might see the photos on Tabblo, and like the experience and decide that they would use the site the next time they took photos they wanted to share. However, that is where the problem came in: it could take months before they next took photos, and decided to share them.

Later on this post, we will talk about how 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 web, high and low, looking for 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 appeared to make the same mistake, which was assuming that the entire customer base would continue sending out invitations for every cycle. So I collaborated with my partner, Stan Reiss, who turns out to be a whole lot smarter than I am, and he helped me develop the fomulae that are used in the more sophisticated model for viral growth below:



(In case the model above does not appear, click here to download the spreadsheet.)

A quick look at the table that shows the effect of varying the Viral Cycle Time shows that customer growth is dramatically affected by a shorter cycle time. For example, after 20 days with a cycle time of two days, you will have 20,470 users, but if you halved that cycle time to one day, you would have over 20 million users! It is logical that it would be better to have more cycles occur, but it is less obvious just how much better. A quick look at the formula tells the whole story. The Viral Coefficient K is raised to the power of t/ct, so reducing ct has a far more powerful effect than increasing K.

This explains why YouTube exploded at a faster rate than ever seen before.

Lessons Learned

There are a large number of interesting lessons to learn from the above models:

  1. Unless you have a Viral Coefficient that is greater than 1, you will not have true viral growth.
  2. The most important factor to increasing growth is not the Viral Coefficient, but the Viral Cycle Time (ct) which should be made as short as possible. This will have a dramatic effect on growth.
  3. The second most important area to focus is the Viral Coefficient (K). Anything that you can do to increase the number of invitations sent out, and the conversion rate, will have a significant effect on growth.

In addition to the above lessons that come from the model, there are some other important observations:

  1. Virality is not a marketing strategy that can be executed by the marketing department. It has to be built into your product right from the beginning. This is a function that needs to be thought through by the product designers and developed by the engineers.
  2. The most viral products are those that only work if they are shared. For example, Skype only worked in the early days if you got your friends on to Skype, otherwise you had no way to call them. If you have an application today, think about how you can make it social, where it would work better by sharing data with friends/co-workers. That provides a great incentive for customers to invite their friends/colleagues to use the application.
  3. To make the Viral Cycle Time as short as possible, we can apply the same thought process that we use in Building a Sales and Marketing Machine, where we look at what are the customers motivations and negative reactions as they flow through the viral cycle.  For example, when I reach the stage where I have to enter my friends addresses, I will not bother to do very many if I have to look them up in another program, and copy and paste them one-by-one into the browser. You can solve this problem by providing me with Facebook Connect integration to invite 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 can be done.) Getting at email contacts is easy with web mail clients like GMail, etc. – but harder with Outlook. However viral products like LinkedIn have created Outlook adapters that you can download. It is also feasible to get at that information via Outlook Web Access (OWA) provided you can deal with the security concerns.You should also be looking for ways to encourage customers to invite people at various junctures in their use of the application. And of course, you should be asking yourself the question: is the value proposition of your product really that compelling that your customers will want to share it with others? Another great way to increase virality is to incent customers with a reward for every customer they successfully convert. Since this can result in an individual feeling guilty that they are making money off their friends, the best way to do this is to also provide the friend that is receiving the invitation with an equal incentive. Now your customer will feel like they are doing their friends a favor.
  4. Consider leveraging viral platforms such as Facebook, which have built in social features to let friends know what apps you are using. The wall, and status updates provide a great way for their friends see your app.
  5. Use A/B testing to figure out which approaches and creative presentations are getting you the highest conversion rates.
  6. If you are successful in creating a viral model with very short cycle times, watch out for what can happen. Several companies that have been lucky enough to achieve this have been shocked by the enormous need to scale server capacity. Fortunately with cloud computing offerings such as Amazon EC2 and S3, it is easier than in the past to scale on demand.

Hybrid Viral Models

Many entrepreneurs reading this post will realize that they may not have the means to achieve true viral growth (where they have a Viral Coefficient of greater than 1). Rather than giving up, it is worth considering a hybrid viral model. In the hybrid viral model, you make up for the shortfall in customers by acquiring those through some other means such as paid search, or SEO.

Model Limitations

The model above is pretty simplistic and does not take into consideration several real world phenomena:

  1. 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.
  2. 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.
  3. The customers that you have may send out more than one set of invitations beyond the initial set.
  4. etc.

Further Resources

Since publishing this post, I created a SlideShare presentation that has a several additional ideas on viral marketing: The Science behind Viral Marketing. Also check out Andrew Chen’s blog, as he has written extensively on the subject of Viral Growth. For example, here is one great example: What’s your viral loop? Understanding the engine of adoption.

Uzi Shmilovici has a nice list here of the Eight Ways To Go Viral.

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.

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David Skok

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  • I just thought I’d share some of the variables which comprise my ‘Viral co-efficient’:
    • The value of the information to the avg person, relative to what news is out there currently, and what is conceivably possible.
    • The value of the information to the avg person, relative to what news is out there currently, and what is conceivably possible. Is this a big deal to me? Is this a big deal to others around me/I know/can tell?
    • The number of channels the info can flow from the initial source to the total audience (TV, Radio, Newspaper, or just during the half time show during the superbowl?) as well as the ‘total reach’ expressed as a percentage of the total population. There is still 10%? maybe you can’t reach within X timeframe.
    • Fidelity, which is tied in part to the complexity of the information. Will the person understand what is being explained to them? Or will the message degrade from transmission to transmission?
    • The number of initial dissemination points. Great news/terrible dissemination = failure, lame news/incredible dissemination = possible success, etc.)
    • The value of sharing this information with others, usually expressed as the avg shares per individual …which partially hinges on the number of channels the info can flow from the downstream sources to other downstream sources who are already not aware of whatever you are trying to show them.
    • Time. How long is the duration of viral-ity? Is there a build up before hand, like New Years? Is it flash news that becomes useless just as fast? Or something that will hold people’s attention for days/weeks/years? I’s it a race with a start and a finish, or does it run until it just peters out and dies?
    • Authenticity. Who told you the sky is falling? Drunk bum, or your best friend? (unless the drunk bum *is* your friend:)
    • Orchestration. Was this by chance? Or planned carefully to maximize all the synergistic feedback cycles possible?

  • Great article for me as a newbie to viral marketing and something that I will no doubt include into my must have methods for success.

  • It can be powerful but most people don`t understand how to use it effectively.

  • I would agree with that. Do you have any interesting suggestions for those people?

  • Tom Birch

    Something to add to your Lessons Learned:  When you have a viral coefficient of greater than 1; make sure your close your next financing round quickly.  Based on my personal experience in early 2003, I lost 5 term sheets in less than three weeks when the virality declined from 1.2 to 0.99.  Some fun.

  • It was impossible to learn all of them even after months of studying. Thanks, Specially the three lessons which we learned from model were great.

  • Delighted to hear that. Thanks for taking the time to provide the feedback!

  • This is a great post and really helped me understand the concept of the viral coefficient and the math behind it. Has anybody written about/modeled the relationship b/w K, ct, and the continuously compounding interest model?

  • Thanks for the positive feedback. I am not aware of anyone who has modeled that. Sorry!

  • wow, awesome post, I was wondering the same thing. and found your site by google, many userful stuff here, now i have got some idea. bookmarked and also signed up your rss. keep us updated.

  • I really like how you broke everything down so well here. I cofounded an ecommerce company that has a very unique product line with large market. We have from the beginning tried to take these concepts and apply them towards an ecommerce model for a recurring physical goods product that households spend on average about $15 a month on.

    We are seeing a pretty close to a coefficient of 1 right now with tools like curebit.com that encourage people to share a special coupon code post purchase to their networks on facebook/twitter/via email. That and then having people upload pictures and videos of the rings they find in their candle are our soft ‘invites’ for our ‘app’. 

    Any thoughts David on how an ecommerce site can take these same concepts and use them as a growth model?

  • Hi David,

    Thoroughly enjoyed your article. I would be very keen to ready more about Stan’s derivation of the viral growth model. Any chance you could post in the comments?

    Ref: Custs(t) = Custs(0) * (K ^ (t/ct +1) – 1) / (K-1)


  • Here’s the derivation:

    Assuming each customer successfully invites K new customers once, in each viral cycle period the number of new customers is K times the number of new customers who signed up in the previous viral cycle period. We start with Cust(0) as the initial set. After the first period, they will have invited K*Cust(0) new customers. After the second , those new customers will have invited K*themselves or K*K*Cust(0) new customers or K^2*Cust(0) customers. This will continue and accumulate. So at time t, when t/ct viral cycles have passed (remember ct is the length of a viral cycle), the number of customers will be:
    Cust(t) = Cust(0) + K*Cust(0) + K^2*Cust(0) + K^3*Cust(0) + … + K^(t/ct)*Cust(0)
    This is just a geometric series that we all learned in calculus but few of us remember.
    We are looking for the sum of the first t/ct terms of this geometric series. It’s not hard to derive the formula from this if you remember the approach, if you do not it’s illustrated here: http://en.wikipedia.org/wiki/Geometric_series.
    Best, Stan Reiss

  • Dani

    Hi David, great stuff!
    Just one thing… This model assumes a 100% subscribers retention rate, what it’s not real. right?

  • You make a great point. That is of course not realistic. The main point of the original model was not to aim for complete accuracy, but to help illustrate which variables impacted growth. But there are two things I would add if trying to build a more accurate model: churn, and an ability for existing customers to continue inviting others over time at a reduced level.

  • So let’s add churn into the formula you published in the spreadsheet.  It seems to me that we could substitute (K * Rentention Rate) for K in the formula below to get a much closer approximation to the real answer.  The logic being that the number of new Users implied by K is reduced by the Retention Rate.  The formula:

    Custs(t) = Custs(0) * (K ^ (t/ct +1) – 1)  /  (K-1)  when you factor in Churn, becomes: Custs(t) = Custs(0) * ((K*Rentention Rate) ^ (t/ct +1) – 1)  /  ((K*Rentention Rate)-1)  I’m not a math-whiz so this may be off base.  Please comment.

  • David – Thanks for your prompt reply. Much appreciated.

  • Sparsloe

    Very good point.  I realize you posted this a year ago but I’m working on a model for a new web application group.mx.  This article was very useful and I just read your feedback and it addresses a challenge for us in figuring out the ongoing virality with the active users    Today we have an active user base just over 150,000.  Figuring out the viral impact with them will be tricky and I can’t treat them as new users. 

  • Anonymous

    I just found this via Quora and wanted to thank you.  this is the most complete and useful explanation of this concept I’ve ever seen. 

  • Thanks for the kind feedback!

  • i really like to appreciate you because your information is good for my work and I think more people need to read blogs like this.

  • Hi David,
    Thank you for the article. It’s a great read and highlights the importance for the Viral Cycle Time to be as short as possible. Social games do a great job at shortening the ct and K.

    One question I have is in your experience, how can enterprise mobile apps become more viral? I have a startup that helps property managers perform their inspections on an iPad app. It’s based loosely on a freemium model. We are looking to provide additional credits to users if they TWEET about our product. But what other successful implementations have you seen?

  • VS Joshi

    The entire article is good.. However, the line that is extremely insightful is  –  
    Virality is not a marketing strategy that can be executed by the marketing department. It has to be built into your product right from the beginning. This is a function that needs to be thought through by the product designers and developed by the engineers.

  • Hunter

    Really great article, when there are a lot of really bad articles across the web on this topic. One question about social network ‘Sharing’. I’m trying to measure my viral coefficient for a mobile app. When users download, they have the choice of posting/sharing to their Facebook wall (or Twitter) that they downloaded, and recommending that others check it out as well. Let’s say this user has 200 friends. Should this be considered 200 invites? And if so, it would have a very low conversion rate? If that is the case, then I wonder the best way to estimate the number of friends people have (and thereby the # of people that they’re sharing with).

    To take a step back – is it even possible to measure a viral coefficient based on ‘sharing’? Or is ‘inviting’ individuals the only way that this can be effectively measured?

    Thanks again for the great article!

    — Hunter

  • David – I keep referring back to this post often.  When you are calculating the # of people invited, do you do this as an average or a median across your existing user base?  E.g. 1 person might invite 50 people and another might invite 1.

  • Hi Vijay. Answer: Yes.

  • Hi Hunter, when I wrote the article, I had to use a simple term for the notion of “inviting” someone, so I used invites. But effectively sharing is the same as inviting someone if in some cases a few of them decide to use your service as a result. It is just a less explicit form of invitation. So if someone shares with 200 people, and 1 person converts (which is a 0.5% conversion rate), then you have a viral coefficient of 1.
    I hope this makes sense.
    Best, David

  • Saul

    Great post! 

    But suppose you don’t know “i”…how would you calculate K then?Suppose you just have the total number of people who succesfully invited someone and the total number of invited people who signed up (you only have stats on people who actually converted).

    How could you calculate K from that dataset?


  • K can be figured out from that. Put simply, if each member you have ends up creating more than 1 new member, you will be viral. K is equal to the number of new members generated by each member.
    I hope that helps!

  • Marine808

    How does 1.0E-10 factor into the viral coefficient?  I’m not sure what it is there for because I am working on combining your model with a few other ideas including figuring out the ongoing virality of current users (as well as a retention saturation which I could explain more in another conversation) but I still haven’t figured out why it is needed.  How does it affect the viral coefficient?  I am not a math whiz but have a pretty good base to work from yet it still boggles me.


  • The only reason it is there is to make sure that there is never a division by zero. So I just added a tiny amount to avoid it ever being zero. For your purposes, just use zero.

  • Ali Anani

    This is an extremely enlightening post. I appreciate greatly the clarification of the point that you ably explained about cycle time.
    I published yesterday on slideshare a presentation on Dose of Does. I showed the role of the Rule of 72 in bringing up viral growth of our experiences. Are experiences subject to your line thinking of customers? I have to think about it

  • Hi Ali, I don’t believe the formula shown here will have a direct relationship as the loop you show in your presentation is very different to a viral loop. The big difference is that there are no additional people to spread the experience in your loop.

  • David–Thanks for the thorough explanation of the formula! We were inspired by this post and all of the other resources that came with it.

    We created a javascript version of the viral calculator here:

    We also added an equation for monetization where folks can estimate CTRs and project monthly revenue potential.

    Thanks again!
    Josh (Facebook App Rush)

  • Hey David,

    Thanks for this, I’ve been using this article for a while and it’s very helpful. One question however that I think would be useful for any e-commerce company trying to use this model:

    Part of the model assumes that we take the starting customers from the previous cycle add the new customers to get ending customers and from there we compound on all your other factors. For e-commerce however, the “starting customers” from the previous cycle should not be included in the following cycle. E-commerce customers will not return each cycle as say youtube users would, but rather at some retention rate much further out in time, for an example let’s say 60 days or on the 30th cycle if ct=2. How does one account for this in your model? I’ve had trouble trying to nail this down using your formulas.


    – Adam

  • David – I’ve read a bunch on K but this post was by far the most helpful. Thanks very much for taking the time to thoughtfully lay out this thinking. And nice of you to let my pal Stan help out a little too :-). Was in fact looking for ideas on how best to incorporate time into our model when I found the link to your post on Quora. Thanks again! -Nate

  • chris

    I agree this is an important factor and something worth building into the model. It is rare that every new user will invite friends, so I think it is important to discount K by the % that share… K=%share*i*%conv

  • Great article, David!

    I wonder if, on the calculation and understanding of the Viral Coefficient, at any point were population modelling using Leslie’s Matrices used as references.
    Thanks for the great read.

  • samo

    Thanks, David, this is really helpful for me, as im writing my Bachelor-Thesis on viral videos. What would you say is a realistic cycle time for videos on YouTube? 1 day?

  • It really depends on the specific video, and how well it is seeded to a strong initial audience. For the best videos, 1 day is probably right. But many videos never go viral.

  • Josías De La Espada

    Thank you! Excellent/useful article!

  • fiveshorts

    Thanks David (and Stan) for this wonderful article. It’s been an absolute eye-opener and boon for our VC pitches. Turns us from textbook bores into cutting edge experts and helps us defend our financial projections with REAL arguments, rather than the usual “what if 1000 people signed up…” BS.

    @Chris — I was wondering the same thing as you, so, instead of just having David’s starting set of users, I built a sheet that inroduced a new variable which I clumsily call the “proactive %” which is my way of saying “ok, so 100 people may get exposure to my app; may buy it; but what percentage are sufficiently rabid keen on it that they feel driven to tell others. The result of my 100 * ‘proactive%’ forms my starting set.

    Not rocket science, but I get SUCH a kick out of getting closer and closer to reality…

    likewise building in this idea of churn/retention variables. Love it. Haven’t worked out how to adapt the formula yet, but working on it.

    thanks everyone.

  • fiveshorts

    There is no doubt that we can all be as fancy as we like with formulae, but if our product is unremarkable or the opportunities for users to actually ACT on their desire to ‘tell’ are not engineered right into the very fabric of the product itself, then it’s all for nought.

    the real value of this article, as David points out, is that you can’t bolt vitality on to a product with marketing, you have to engineer the very architecture of what you’re offering to facilitate virality.

  • fiveshorts

    Yes, that’s a discussion that’s been very compelling here. The idea of viral spurts — obviously not something one can really project, predict of build into a spreadsheet of any value, BUT, what we’ve elected to try is employing a metrics ‘person’ who’s sole job is to collect, collate and present all of these data in as fine a granularity as possible, so we might be able to tally iterative development with changes in ct and K. We think it’s worth the investment, because one, decent ‘spurt’ can fund months of less remarkable K, so to know where the spurt came from is priceless.

  • fiveshorts

    Very well delineated. We’re blessed with inherent virallity and work mainly on reducing ct (without annoying the customer).

  • fiveshorts

    btw, I built a real perty version of David and Stan’s advanced spreadsheet in Apple’s Numbers (so I can present on my iPad), with all the variables as nice big, editable cells (in red), so that meeting participants can mess with the numbers to see the effect on the bottom line… If anybody wants a copy, lemme know.

  • fiveshorts

    btw, I built a real perty version of David and Stan’s advanced spreadsheet in Apple’s Numbers (so I can present on my iPad), with all the variables as nice big, editable cells (in red), so that meeting participants can mess with the numbers to see the effect on the bottom line… If anybody wants a copy, lemme know.

  • fiveshorts

    And here it is with David Miller’s “churn” or Retention Rate as % variable factored in. I really like this.

  • I would love to see it. Please could you email me a copy. (My email address is dskok at matrixpartners dot com.). Thanks, David

  • fiveshorts

    Hi David.

    I’ve been working a lot with your Viral spreadsheet and formula.
    I was wondering if you’ve given any thought to how one might simulate what I think of as “viral fade”.

    The reason I ask is because I have your advanced Viral formula producing believable growth for year one of a business model, based on realistic numbers of invites and conversion rate etc, but we all know that is not a growth pattern that is usually maintained, and that same formula, when extrapolated out over the second year generates a theoretical userbase many times greater than the population of the planet…

    Now this would be good for business :-), and I’m VERY aware that this is not an exact science, but I was wondering if maybe there was a way to introduce new, realistic variables into the mix, that might ‘temper’ the growth and calm it down…

    I tried a simple “multiply everything by a retention rate %”, but it didn’t feel right.

    Any ideas?

    Many thanks


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