Customer Value in the Freemium Model

The following is from the November 2009 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment and I’ll reply.

Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

Q: You kindly clarified a few issues when I was reading Drilling Down earlier this year – so I hope you don’t mind the direct email.

A: Yes, I remember!

I am working for www.XYZ.com, a social networking / virtual world site based abroad but visitors are 85% US.

Our growth up to now has been mainly viral and in the summer we hit 1.2M UVs operating on the Freemium model with only 5% of our registered users converting to paying customers and a significant portion of our revenue coming from ads.  On average our customers are active on the site for something like 4 months making their first purchase around day 28. 

But to take us to the next stage we are embarking on some marketing for the first time using AdWords and various revenue share campaigns, and of course to do this sensibly we need to arrive at a reasonable estimate of LTV.

A: Makes sense!

Q: To calculate an adjusted LTV I removed all customers with a lifetime of less than 4 months but this gives a low estimate as this calculation ignores the bumper summer months and the extra paid for features put in place earlier this year.  Calculating LTV using ARPU and monthly churn (not sure how to calculate this in our environment) gives another different estimate.  Is there any help or advice you could perhaps give us?  If not in the US then perhaps you could recommend somebody abroad – can’t find anything in the literature relevant for start-up like us.

A:  It sounds to me like you’re trying to make this too complicated, at least for the place you are at this time.  Monthly churn and the “28 day” threshold are nice to know on a tactical level, but LTV is more of a Strategic idea that does not necessarily benefit from analysis at that level.  And you may not really want LTV, but a derivative that might be more helpful.

Let’s say the average user sticks around 4 months.  Say also that you generate revenues of $1 million over that period, and 1,000,000 users had some level of activity.  So your revs per active user are $1.  In terms of generating net revenue, you want to acquire  users for less than $1.

Now, we know that number is topline, and obviously there are expenses.  Companies like yours do not have very straightforward financial models because of the large amount of R & D that may be capitalized rather than expensed.  So you need to go to someone in Finance and determine what the right number is to use for looking at ROI.  Ask them, what percent of our revenues are left over to pay bills?  Or, what number would you like to see increased through a Marketing program?

Is it cash flow? Earnings before Interest, Taxes, Depreciation, Amortization?  Gross Margin?  Some other?  Then, what percent of sales does this number generally run at?

Let’s say it’s 40%.  In other words, 40% of revenues is actually available to pay bills and so forth.  So in the example above, .4 x $1 = 40 cents, which is the max you can pay to acquire a user, and anything less than that generates money to pay bills.

This method of course looks at all revenues. Not sure why you would want to look at it any differently, since even users that don’t “purchase” still generate ad revenues.

But let’s say you want to be more specific, you care only about buyers and only want to run campaigns that generate buyers.  In other words, the advertising revenue is “nice to have” but you want to build out the paid marketing model based on the acquisition of visitors likely to purchase.  You can run the same model above, but only look at the known the buyer group.

Take any 4 month period, find revenues from purchases and divide by number of people purchasing (not purchases, but individuals who purchase, revenue / user).  Then apply the same 40% flow through from the model above, and that’s the max you can pay to acquire a buyer.

When you aggregate known buyers, segment by source and you will find different campaigns generate different kinds of buyers; some will stick around longer than 4 months, some less.

Segment these folks by campaign and run the same model as above, purchase revenue for the period they stick around divided by purchases times the 40% flow through.  That’s the max you can pay to acquire a buyer in that segment.  So you end up with (just guessing) being able to pay 5 cents for campaigns that generate people who stick around 2 months, 15 cents for people who stick for 3 months, and 40 cents for people who stick 4 months.

You can make LTV equations very complex, but often the point of the exercise is not really “what is the customer LTV?” it’s “how much should we spend to improve cash flow?” or something similar.  This is a much easier question to answer and often what the company really wants to know.

Said another way, it’s very difficult and often dangerous to peg an LTV number in a dynamic environment because there are so many potential changes that will impact it; LTV is a number you may fully understand 2 – 5 years from now.  Until then, you need something “close” that drives the same kind of thinking and action, and the approach above will get this done for you.

As things evolve, this number (called “flow through”, recently have heard it called “Near Term Value”) will basically approach true LTV as you extend the number of months in the measurement period.

At the point where your business is somewhat static at an operational level, you can then look for true LTV by examining the revenue of actual defectors.  This is the only way to really peg LTV – after users have left and sufficient time has elapsed where you do not believe they will come back by themselves.

Until then, you are better off trying to figure out how much you can pay to attract high  quality user / buyer segments.

Jim

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