Click to download Freemium spreadsheet
Background on this discussion
Last year, the stupendous Daniel James co-hosted a talk with me on Lifetime Value metrics for subscription and virtual goods-based items. You can see the video/outline for the talk, Daniel’s commentary, and a mindmap of the talk (scroll to the bottom of the post).
As part of the talk, we worked on a spreadsheet model for freemium businesses that we didn’t get enough time to work on – so I’m going to cover it in this post! If you haven’t gotten the spreadsheet yet, here’s another link to it.
Here are the questions this post (and the spreadsheet) is meant to answer:
- What are the key factors that drive freemium profitability?
- How do freemium businesses acquire customers?
- What are the drivers of customer lifetime value?
- How do all these variables interact?
If these questions interest you, keep reading :-)
Article summary (for people with attention deficit!)
To become profitable using a freemium business model, this simple equation must hold true:
Lifetime value > Cost per acquisition + Cost of service (paying & free)
Said in plain english, the lifetime value of your paying customers needs to be greater than the cost it took to acquire them, plus, the cost servicing all users (free or paying).
There are lots of different factors that influence profitability, including:
- Cost per acquisition
- Efficiency of media (traffic sources, CTR, impressions)
- Signup funnel conversion %
- Average viral invites sent out
- Lifetime value
- Retention metrics
- Revenue mix
By understanding these subcomponents, you can tweak your model and figure out what metrics need to be hit in order to reach profitability.
Now for all the gory details…
The first tab in the spreadsheet covers the issue of paid user acquisition – many subscription businesses mostly rely on AdWords and ad network buys in order to acquire users. For freemium businesses, particularly ones that are social apps, there’s often a word of mouth or viral component, which we’ll cover in a second.
I’ve written extensively on paid user acquisition in the past, particularly the blog post: How to calculate cost-per-acquisition for startups relying on freemium, subscription, or virtual items biz models.
At a high level, here are some of the things you’ll want to track:
- How are you paying for traffic? (CPM/CPA/CPC)
- What do the intermediate metrics look like? (impressions/CTR/etc)
- How does your signup funnel perform?
- How much are you spending for the users you end up registering?
Basically, you end up with a media buying matrix that looks something like this:
||CTR||Clicks||Signup %||Upload pic||Users||Cost||CPA|
and these are some factors worth thinking about, in terms of increasing or decreasing the cost per acquisition (CPA):
|Source of traffic||Ad networks, publishers||++|
|Cost model||CPM, CPC, CPA||+|
|User requirements||Install, browser plug-in, Flash||+++++|
|Audience and theme||Horizontal vs vertical||++|
|Funnel design||Landing page, length, fields||+++|
|Viral marketing||Facebook, Opensocial, email||+++++|
|A/B testing process||None, homegrown, Google||+++++|
As previously mentioned, lots more detail here.
Once you get your users registered onto the site, then there’s the question of how convert to paying customers, and whether there are any viral effects. The model covered in the spreadsheet has a separate tab, called “Funnel” which covers these issues.
At a high level, there’s what is happening:
- Each time period, a bunch of newly registered users come in (both acquired through ads or through viral marketing)
- Some % of these users convert into paying users
- Some % of these users then send off viral invites
- Revenue is generated by building up a base of paying users
- Cost is generated through building up a base of active users (paying or not!)
To me, this tab captures the “art” side of building a freemium business. Persuading peopleto pay for your service and invite their friends requires creativity, product design, and lots of metrics. Josh Kopelman of First Round Capital had a great tweet recently on this topic where he says:
@joshk: Too many freemium models have too much free and not enough mium
As Josh notes, the key is to create the right mix of features to segment out the people who are willing to pay, but without alienating the users who make up your free audience. Do it right, and your conversion rates might be as high as 20%. Do it wrong, and your LTV gets very close to zero. This is why premium features have to be built into the core of a freemium business, rather than added in at the end. You want to be right at the balance between free and ‘mium!
Just remember that during the time period that it takes you to figure out your funnel, viral loop, and everything else, all the free users you’re building up create cost in your system.
Businesses that aren’t eyeball businesses shouldn’t act like eyeball businesses :-)
Anyway, the product design issue (and resultant conversion rates) are a a deep topic, and here are some other related posts (by others and myself):
- Thoughts on free powered business models (Charles Hudson)
- Casual MMOs get between 10-25% of users to pay (Nabeel Hyatt)
- Successful MMOGs can see $1-$2 in monthly ARPU (Jeremy Liew)
- Bridging your traffic engine with your revenue engine
- What’s your viral loop? Understanding your engine of adoption
Of course, it’s not enough to just acquire paying users, you need to retain them. If you have a super high churn rate, then at best you’ll be stuck at a revenue treadmill (doing lots of work but flat revenue and no profitability). At worse, it’s easy to lose a ton of money, if the CPA exceeds the LTV. I wrote about this topic earlier in my essay When and why do Facebook apps jump the shark (which also has a spreadsheet).
How sensitive are retention numbers on lifetime value? Here’s a quick thought experiment: Lifetime value is the sum of the revenue that a user might generate from their first time period to when they quit the service. Think of it as an infinite sum that looks like:
where rev is the revenue that a user produces during a time period, and R is the retention rate between time periods.
You can simplify this, based on the magic of infinite series:
So let’s say that you make $1 per time period, and you have 1000 paying users. Let’s compare the difference between a 50% retention rate and a 75% retention rate:
This means that in this case, by increasing your retention rate by half (relatively speaking), you actually DOUBLE your revenue. And even more when you reach “killer app” status and attain retention rates around 90%. This is a big lever.
Note that retention rates are generally not fixed numbers – they usually get better the longer a cohort of users stays with you! I’m using a fixed retention number to set a lower bound, and for mathematical simplicity.
OK, so the biggest factors affecting retention boil down to three things:
- Product design
- Notifications (optimize them, of course)
- In success cases, saturation effects
For more reading on product design, I’d recommend Designing Interactions from IDEO. For notifications, there’s been a lot of great work in the database and catalog marketing world, for example Strategic Database Marketing. Tesco, Harrah’s, and Amazon are all companies well-known for their strategic use of personalization and customer interaction. For saturation effects, as previously mentioned, my old-ish article When and why do Facebook apps jump the shark.
Cashflow (and ad-reinvestment)
The tab “cashflow” in the spreadsheet captures a couple different issues:
- Paid user acquisition is usually an upfront expense, whereas the revenue comes in over time
- Your revenue per paying user depends on a mix of revenue sources
- You pay a “cost of service” across all users, whether they are paying or not – be careful that this cost of service is not too high!!
Some more detail on the above:
In a model with paid user acquisition, it takes time to break even. You pay for a user upfront, but then the revenue stream trickles in over several time periods. As a result, you tend to be cashflow negative for some number of time periods, and which then goes positive later. This effect is compounded further if your model specifically depends on viral acquisition, because you don’t get significant users in virally until your userbase becomes large.
This is why you get a graph like this, where you’re unprofitable for a while, then break even:
Note that it’s also VERY possible that they never cross, and the entire business is unprofitable. Just play around with the numbers in the spreadsheet and you can see how easy it is to happen!
In terms of average revenue per paying customer, what you typically find is that your customer base is made up of multiple segments. You can price them differently through different tiers of subscription (Free versus Pro versus Business) or with Pay-as-you-go or with many other models.
Ultimately you can roll this all up into a single number, which is referred to in the spreadsheet as revenue per paying customer. You can also divide the revenue by the number of total users (paying or not) in order to get the average revenue per user (ARPU).
As for the cost of service, your mileage will vary. The main thing is, try not to do anything too expensive for free users! After all, given that typical conversion rates are <10%, and subscription services are typically <$20/month, the following thought experiment is insightful:
Plus then you have to factor in the acquisition cost! (Probably a couple bucks per user, so thousands of bucks per 1000 users).
And finally, the last tab on the spreadsheet calculates lifetime value. Basically you figure out the number of payments that a paying user will generate over their lifetime, referred to in the model as “user periods.” (I arbitrarily took this out to 20 time periods, but you can do something different) This is then multiplied by revenue per paying user, to get the total dollar figure generated.
More important for the paid acquisition model is to do the LTV calculation not for paying users, but for all registered users (paying or free). Doing this then lets you figure out if you can profitably arbitrage traffic via ad buying. This is done using the same method detailed in the above paragraph, but using total user numbers rather than just paying users. Then you compare this LTV number with the effective LTV that you get from buying users and then factoring in their viral effects (as shown in the Funnel tab).
Of course there are tons of things in this model of freemium businesses that ought to be improved!
In particular, a couple ideas:
- Benchmarks of real world data for comparison
- More granularity for user acquisition for affiliate versus ad buys versus other
- Saturation rates in the viral model
- Better model for retention rate other than one fixed number
- More sophisticated accounting of cost per user (infrastructure/employees/etc.)
- Model in multiple revenue sources including transaction fees, for Paypal versus Offerpal versus In-store cards versus mobile
- Better intelligence around ad-buying, including ramping up when profitable, slowing down when unprofitable
More on funnels, retention, viral, etc.
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