Most communities have jargon. Buried within that jargon are all the biases, beliefs and worldview that are held by that community. (This can be referred to as a paradigm.)

The web analytics community is no exception.

The terms ‘analytics’, ‘optimization’, ‘engagement’, ‘unique’, ‘pageviews’, ‘funnel’, ‘A/B Test Split Test’, ‘personalization’, and ‘filter’ all have their own baggage that anybody outside the community might not fully understand.

Sometimes people get into disagreements over definitions in an effort to gain specificity. These activities are really quite important. An outsider might be mystified by why such disagreements become so heated. That’s because sometimes the real fight is over the paradigm or some feature of the paradigm.

(For instance, the fighting over the term ‘unique’ was much more about the tension between accuracy and understandability.)

These shifts are indicative learning. I’m finally backed up on this whole hypothesis that language and learning are inextricably linked by Bickerton, 1995.

For instance: Google and Web Analytics. Let’s set aside the disruptiveness of FREE for now (besides, it isn’t free, because time has a cost), and turn to some of the new words.

“Filter” is one of them. It’s about three years old now and is really just a proxy for the word ‘dimension’, which is a data warehousing / data modeling term. Now we use the term ‘custom segment’ instead.

Two new terms: “possible causal factor” and “statistically significant”, have been recently introduced. I welcome the additions to the Google Analytics product.

Usually I need to export a large amount of data out of tools like Google, reformat them, and then load them into SPSS to look for ‘possible causal factors’ that are ‘statistically significant’.

Now Google promises to democratize that process for all those who don’t have SPSS, or know how to use such tools.

There’s going to be a learning cycle where somebody will have to explain the difference between sampling error and Types I and II error and between confidence interval and confidence level. If we’re indeed merging with the Business Intelligence and Data Mining communities – we’ll need to learn a harmonized language. It might as well be the right language.

The process of how this community will learn will be contentious and heavily based in definitions.

We can be fairly certain that some vendor will try relabel a word like ‘confidence’ in an effort to get first mover status. Somebody will mislabel the word ‘correlation’ for ‘causality’. And I’m fairly certain that we’re going to spend two or three years undoing the damage.

This is basically how communities learn. Through jargon and discussion about what the underlining terms mean.