Don’t get caught using averages (part 2)

Pareto/Power Law distributions: the needle in the haystack

I wrote previously about the prevalence of Pareto/Power Law distributions in product users’ behavior here.  Wow, that’s a lot of alliteration.

But the discussion stopped at only one dimension of data.  For example, a single dimension like Free versus Paid users of a Freemium product such as online backup.

The story gets really interesting when you consider multiple dimensions (aka variables) of data at once, each with its own Pareto characteristics.  The outcome can lead you to a some very interesting places.

In the first scenario, a small set of users in Dimension One (let’s say, Paid product users) also represents a small set of users in Dimension Two (let’s say, country of user origin).  This can mean that a tiny percentage (sometimes less than 1 percent!) of an entire user base represents almost all of the revenue or commercial value.

When this happens, it’s incredibly important to know who these users are; you’ll need to hang onto them for dear life to protect your revenue stream.  For example, you might cater to the specific needs of users from their country of origin.  Do you think users in China have different product needs than in France?  Probably.

In this scenario, you’ll also need to consider a revenue diversification strategy to protect your risks of relying on such a small segment.

Another scenario is that users in Dimension One (again, Paid users) don’t belong to the majority (or, “head”) of the distribution within Dimension Two (again, country of origin).  In which case, the implication is that country doesn’t matter in targeting your best (e.g. paying) users.

You can go astray in this scenario by looking at country of origin in isolation.  Maybe you have a huge pool of users from Germany.  The temptation would be to conclude “Germany is my most important market”.  Unless you knew that paid users didn’t cluster around a single country and that Germany was comprised of lots of free users.

What to conclude?

One: make sure you know if your most valuable user segment is much smaller than a Normal distribution would imply.  Most people think that their most important user segment is something like 10-20% of their base.  If 1% of your users drive the business, know who they are, find more like them, and don’t lose them.

Two: don’t let any one dimension of data drive your definition of user segments and internal decision-making.  If you hear sound bites inside your company like “German users are our most important”, that’s being too imprecise.  It generally takes 2-4 dimensions/variables to be precise about a user segment and to know how to best treat them (“Paid users with broadband PC connections in Germany are most important”).

Three: if you truly have 1% of your users driving the business, consider diversification strategies.  You’re carrying a lot of risk, but you also have 99% of your users from which another valuable segment can be found and served.

Last: as I argued in the prior post, it’s easy to dismiss the Pareto effect as only applying to obvious examples like Freemium for online consumers.  I’ve found the same patterns in other businesses.  In which case the gap between reality and perception is even wider!  Spend some time hunting down these patterns inside your company.  I promise you will be rewarded with new insights.

2 thoughts on “Don’t get caught using averages (part 2)

  1. “But the discussion stopped at only one dimension of data.”

    Not fair, I hinted at multidimensional research… 🙂

  2. You may find through researching that 1% what 4 or 5 valuable parameters are of this pocket. However, the issue then becomes how to target prospects who match those parameters and pay to reach them without having to purchasing to reach a “lot” of others not meeting this criteria. This can difficult/expensive/impossible.

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