I’m excited to see so much attention being paid to freemiumbusinesses lately. These are companies that generate revenue by
offering a free product with an upsell or premium version. Their
economics blends elements of the free, advertising-supported,
“eyeballs” business with more traditional e-commerce and subscription
businesses. For founders, I think it also has another big attraction:
the ability to avoid a lot of “free vs paid” arguments. You can reach for all the scale of a free service and still make money. Pretty good deal, right?
Andrew Chen recently did a great article on the economic model underlying freemium (it includes a detailed spreadsheet, too!). In it, he mentions the core dilemma that all freemium companies face:
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!
Having worked in the
freemium business for a number of years, I can safely report that
struggling with these questions never goes away. I have sat in hundreds
of meetings about what to give away and what to charge for, and they
are not easy. Things get a lot worse when we don’t have a coherent
model of why we’re using freemium in the first place. This is a common
outcome I’ve observed among founding teams that take the “split the
difference” route to freemium.
The way to resolve this tension
is to agree on a fundamental freemium strategy, that leverages the free
part of your business to add real value. In my experience, there are
three basic choices:
- Free serves paid. In this model, your free users trade their time for the benefit of your paying customers. For example, Puzzle Pirates
does a great job of allowing trading between two different currencies:
one that is earned with time, and another that is earned by paying.
Both currencies are valuable, and free users can trade theirs to the
paying customers, who are allowed to access benefits that would
otherwise take a long time to achieve. Other examples are
user-generated content sites, where free users create content that is
valuable to paying customers. The key to this model is to make sure
that customers who create value are heavily rewarded, and those that
don’t create value are marginalized, so that they have a strong
incentive to pay. - Free trial.
The original freemium model, where customers are given a certain amount
of time before being forced to either quit the service or pay. The big
question is when to pull the plug – too soon, and you risk customers
not being sufficiently addicted to say yes; too late and you risk
giving all the value away for free. Luckily, once you realize you are
in this business, you can answer these questions empirically with good split-testing and linear optimization. - Free as inventory.
Some businesses actually sell access to their free users. I think this
is the right way to think about many advertising businesses, like
Google AdWords.
Some dating sites work this way too, where you can post your profile
for free, and people pay only when they want to contact you. This
ensures that the most popular people get lots of value without having
to pay. Here the key is for your free customers to get value from the
site that is greater than the costs they perceive by the fact that
you’re selling access to them. Google has shown their awareness of this
issue by carefully managing the annoyance-factor of ads that are shown
during search. What differentiates this model from “free serves paid”
is that the free users don’t need to consciously do anything special to
be valuable. For network-effects businesses, like Skype, just being on the network is enough to create value.
going to do; for a freemium business, it’s about which users you’re
willing to turn away. Knowing which model you’re in can make these
decisions a little less excruciating. It’s not that you shouldn’t
experiment – on the contrary, this is one of the most fundamental
hypotheses you want to test early on. However, it’s not very useful to
mix and match features from different models. Better to set up complete
experiments that are themselves internally coherent. Take a cohort of
new customers and expose them to a completely different approach, and
measure their behavior over time. Use that data to make an informed
choice about whether you want to change strategies.
