Thursday, May 23, 2013

Conversion = Desire - Friction, more or less

One of my favorite equations for a while has been the very simple Conversion = Desire - Friction,  I have always said that I did not think the math was right but it was so simple and deliciously directionally perfect.  Serious credit to Sean Ellis on it (I believe this is his baby).  More desire, less friction, all good. Made me happy too : )  One day I was thinking that the right answer is actually conversion = p(desire>friction). So if you have a distribution of desire and a distribution of friction for a population, the actual conversion will be those where, for an individual, desire is greater than friction.

All kind of questions start swirling.  Are we better off making desire just a tiny bit better than friction everywhere?  Stop effort once we have motivated conversion, anything beyond that is wasted.  Are there business models where desire distributions or friction distributions should be dictating approaches that we are not thinking about that well yet?  Probably.  Gimme some time to grok on that or some help at least.

Simple enough I guess. But it is rare that the conversion event is enough. Loyalty, LTV, advocacy all go way past conversion.  So even if we get the conversion event perfectly modeled it may not help us succeed better. If upfront conversion and promoter score were independent, maybe, but we all know they are not. 
So if you have the data to think about audience desire and friction distribution, and can build these things independently and impact downstream behavior. You should use a more refined equation perhaps. But for most of us, we are better off keeping it simple probably.  Geez, it is hard enough getting statistical significance on a test as we fragment markets more and more anyway.  I will probably stay with the directional goal of increase desire and reduce friction broadly most of the time for now.  But I am keeping my eye out for a place to apply a more refined view to good effect.  I am sure it is out there for some high scale consumer model.  Seen it?

1 comment:

DrB said...

I found this blog thought-provoking, and that is something from a guy who's current reading list is a Java book, a real thick book on interaction design, and a SolidWorks manual.

I like the idea of conversion = p(desire>friction), as I think it begins to explain a lot about behavior. One aspect of the equation that is not noted, and drives the situation is all the parameters upon which this function depends. Surely desire and friction are not consistent across products (they may differ for a toothbrush and a MacBook Pro computer). There is generally little friction with a toothbrush (except for my son, where brushing is a necessary evil) as they are usually low cost. MacBook Pros have some friction due to their high cost, a customer's ability to use other means of computing, and the like. So, it would seem that there are many factors upon which these distributions depend, such as cost, prestige, early adopter image (that's what I look to you for), and environmental conditions, to name a few. And they change all the time.

This last parameter, environmental conditions is interesting. I recently saw a lady in a store that made it well known that she had recently undergone a divorce, and she was changing around her life. What did she drive outside? None other than a brand new convertible Porche. Desire and friction changed dramatically based on that incident.

On many items, particularly those associated with prestige, it may be true that we only need to create enough desire to get consumers over the hump. Once they are there, they are often times committed, and work to justify what they have, creating more desire, and less friction for future purchases. Of course, if we do not deliver on the expectations, we cannot expect the customer to continue to justify the purchase indefinitely, so the analysis is tricky.

I think the aim to keep the model simple and use conversion = p(Desire>friction) works best as it guides us, rather than being a numbers game that is rigorously prescriptive. Knowing that we have distributions of desire and friction motivate us to understand more, even if the analysis is qualitative. we can then set out to understand the drivers behind desire and friction, such as the ones I mentioned above.