Assumptions, Facts, and Models

Without assumptions, analysts wouldn’t be able to say very much about the world. Even facts have assumptions.

Consider the following statement:

“There were 19,000 pageviews in October, 2010.”

Okay. I’m willing to accept that fact as true. Assuming that all the tags were in place, on every page. Assuming that server errors identified and not counted as pageviews. Assuming that all the tags fired without fault. Assuming the web analytics software is calibrated to the WAA definition of the term ‘pageview’.

People form associations and communities, in part, to standardize assumptions so that progress can be made. Whoever is behind the Metric System. The IEEE. The WAA.

Consider the following statement:

“Traffic to the website causes conversions.”

Oh boy.

So let’s unpack.

What needs to be true for that statement to be true?

Assume that traffic means visits.
Assume that ’cause’ means ’cause’, that is to say, Conversion occurs as a consequence of Traffic.
Assume that ’cause’ does not mean ‘only cause’.
Assume that conversion means ‘a monetary exchange that is measurable on the site’.

Under those four assumptions, I’m willing to accept that statement as truthful.

But there are more specific statements.

“Qualified traffic to the website causes conversions.”
“Qualified traffic to a trustable, W3C compliant, with content that inspires desire to purchase a product, and a strong checkout funnel, causes conversions.”

And less specific ones.

“Marketing causes conversions.”

All of which can be unpacked to expose a very specific model of how marketing works. And by model, I mean a range of variables which interact in way where causality is causality and correlation may be observed statistically.

We’re willing to accept a high degree of abstraction because that’s the price of brevity. And different communities pack an awful lot of meaning, even models, into single terms. Moreover, entire communities identify themselves along lines of assumptions that may not be completely acceptable to other communities.

Brand managers have their own universe of assumptions, facts, and models. As do data miners, public relations folks, CRMers, and marketing scientists. To spend at least a day in each of their worlds, making the effort to understand, is valuable.

We’d all be better off if, instead of gagging at the sight of something and rejecting it, to probe into those assumptions. Check to see if the logic holds, and then, discuss. There may be entire regions of knowledge that could be incredibly enriching and make us better.