Category Archives: Web Analytics

Choosing the Size of Control Groups

DataBase Marketing, Driller Q & A, Newsletters, Web Analytics

Jim answers questions from fellow Drillers
(More questions with answers here, Work Overview here, Index of concepts here)

Q: I am a big fan of your web site and read your Drilling Down book. Great work!

A: Thanks for the kind words!

Q: I was wondering if you could help me picking the right control group size for a project of ours? The population is 25 million telco customers that for which we want to do a long term impact analysis (month by month) in regards to revenue increase versus control group. The marketing initiatives are mix of retention, lifecycle and tactical/seasonal activities. We want to measure revenue increase through any of the marketing activities compared to control group.

A: Great project, this is the kind of idea that can really improve margins if you can find out which specific tactics drop the most profit to the bottom line.

Q:I have searched the web for some help and found calculators that say: On 25 million and smallest expected uplift of 0.1% and highest likely rate of > 5% the calculator gives 250k (1%).  Is that sufficient to calculate the net impact on the remaining base?  Would be very grateful if you could give me your thoughts.

A  Well, it could be and might not be…

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Customer Value in the Freemium Model

DataBase Marketing, Driller Q & A, Newsletters, Web Analytics

Jim answers questions from fellow Drillers
(More questions with answers here, Work Overview here, Index of concepts 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.

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“X Month” Value

DataBase Marketing, Web Analytics

The basic concept of LifeTime Value (LTV) was ably outlined by Seth Godin in a great post here.  If you know the average net value of a customer is $2500 over their “Life”, why would you not spend  $50 (or $200, really) to acquire each one?  As long as you stuck to the model, your company would be insanely profitable over time.

Their are 2 primary challenges to implementing this idea.

1.  “Over time” is a concept many management folks have a hard time embracing; what matters are the profits this year, or this quarter, or this month.  Unless the whole company embraces an “over time” measurement approach it is difficult for Marketers and Analysts to drive towards programs and practices supporting the LTV outcome.

2.  The $2500 is an average figure.  Most customers are worth less; 10% or 20% are worth much more.

Most people I talk to embrace the general idea of LTV models intuitively.  It’s really a cash flow concept, isn’t it?

So Financial people get it right away, and if Marketers could align with it, there would be no conflicts and the Marketing budget becomes virtually unlimited.

In fact, many folks in the PPC world follow just this model – they have unlimited budget as long as each conversion costs no more than “X”.  Because the company knows if it spends no more than X on a conversion, it always makes money.   Marketers and Analysts involved with these “Cost < X” PPC programs love them, because Management loves them. 

And Management loves them, why?  Because the CFO loves these programs  Why?  Because they are based on Cash Flow analysis, which CFO’s understand very, very well.

So then, what will it take to get more acquisition budgets like these Cost < X  PPC programs?  We have to address the two challenges above:

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