The engagement pyramid - flipping your funnel upside down

So, why don’t we flip that 100% winnowed to 30% winnowed to 5% funnel? Rather than starting by thinking about how to reach or broadcast to as many people as possible to get to those who matter, what if we began with engaging those who matter the most. We could prioritize surfacing the 5% — and make our entire plan better by learning from their interactions and leaning on their advocacy to expand our reach in a smarter way... 

 By turning the reach-driven funnel upside down, we’re in effect creating an ‘engagement pyramid’. The engagement pyramid isn’t just about retention and growth of our existing customer base. It’s about starting with the 5% who will be most interested in what we have to say and most willing to speak for us. This group not only includes current customers, but also those most likely to influence others toward your brand. This is the group whose insights and advocacy can better inform and enhance our broader media plan, maximizing our overall engaged reach.

read more: the engagement project (google)

Correlation of investment funds - python pandas

While playing around with python data mining framework Pandas I really liked how easy it is to calculate pairwise correlation in data series. Let's check out a sample. As data we can download historical investment fund data and we will calculate the correlation between them.

note 1.: If you are using windows the easiest way to install python with every necessary packages is the Anaconda distribution. Just download and run the installer from here and you are ready to start :)

note 2.: It wasn't easy to find historical data about investment funds. Finally I get the data from Bloomberg website. It was a kind of reserve engineering by checking the network communication when the site was drawing the graphs so there is no warranty that the data format won't change in the future. If somebody know better way to get this data I would be happy to hear about it.

Let's download first the data into an array:


Now we load it into a Pandas DataFrame and plot it (maybe you have to import matplotlib first):

The two dataframe has to contain data for the same period of time so we can simply merge them:

The result is:

Finally let's calculate the correlation:

And we get the pairwise correlation of the numeric columns: