Wednesday, September 24, 2014

Using Your Own Customers to Crowd-Source Analysis

How can we leverage the fact that we're creatures of habit?

We often talk and read about benchmarks by tactic.  For instance 'email open rate' is tracked because it is the gate keeper to engagement and involvement.   As an example Silverpop reported the median open rate in APAC in 2012 as 27.2%.

I chose a two-year old number from 5,000 miles away in order to focus on the fact that these metrics are generated thru the lens of the campaign, not the consumer.

The health of a continuity email program relies on involvement over time and leads to the important question: How many more emails will you open?

The nice thing about 27.2% is that it is derived from nothing but 0s and 1s - consumers did or did not open the email.   If we look at the campaign or program level we now have a series of events from our intended audience that help answer that bigger question.  It turns out that there are two truths about consumer marketing metrics:
  1. Even if your numbers are flat, it is highly unlikely that it is the same consumers each week.
  2. The number of times consumers do something very often follows a predictable pattern.
In the case of email, consumers act just the way they do with buying products - that is there is a decay in the number people who do sequentially more things. 

The number of additional emails opened looks like this for one program.

Yes, the campaign had a problem with relevancy - something originally hidden by the rate of acquiring the email list.  Message: fix the communication strategy.

This is a very simple example of crowd-sourced analytics, there are lots of behaviors that can be treated in a similar fashion.  In fact there is a whole class of work being done in anomaly detection that takes advantage of habits.

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