1. 99% of All
Statistics Are
Wrong
So the story goes, 50% of all investments will never bring a return, in a related
story, 99% of all statistics are wrong. The thinking person asks “does that include
the statistic that says that 99% of all statistics are wrong as well?”
Weighted Data
Points
The inherent risk of any statistical or mathematically engineered formula is that
your results are inexorably tied to the quantification of simultaneous data points
with the result of a derivative zero point analysis of digital integers. In other words,
it’s made up! It’s made up based on what you have right now and it’s just a number
that has no empirically testable meaning. This is true of any provider out there. So
what’s the sauce then?
The Sauce
The main ingredient of any tomato sauce is the tomato. Getting into the business
of canning and distributing tomato sauce with the intent of total re-invention is
foolish. When we compare the taste of one sauce against another, we should look

2. for the differences not similarities to enhance or change our sauce. Creating a
great sauce versus a good sauce can simply be by adding a few additional
ingredients in the correct proportion. In many instances instead of just having a
“good sauce” we will then have a “great sauce”. To follow this analogy is like the
difference between an “influencer formula” that functions as you would expect
versus one that gives you the results you can actually do something with to better
market your service or product.
Many experts agree that the value of your results is proportionally linked to the
amount of data churned. More data equals better results, not necessarily correct.
A few organizations try to correct this by adding human analysis to counter the bad
conclusions thus trying to statistically improve accuracy. Since computers can
only imply what you program them to imply, you will reach a negative conclusion
quickly. So what conclusions are you left with?
Shaping The
Data
Accuracy may always be some-what relative, and that allows us to make certain
definite conclusions. The ultimate goal of any Influencer Formula is to provide
information as a deliverable to clients. In creating a discussion around developing
this framework from communities input, there are ulterior factors at play that are of
critical importance. In my view, the defining factor should be delivering context
relative to the nature of the clients’ needs. The reality seems to persist that even if
you have a small amount of data you need to accurately define the context. If you
are able to accomplish this it would make reaching a conclusion of sentiment a
simpler part of the equation.
Posted in: Influencer Formula