Of course they’re not. Neither are Marketers regardless of what Seth Godin may think.
But in a growing era of math challenged masses it is not uncommon to be the perpetrator of lies albeit inadvertently. This affliction is especially true among online marketers using analytics with very limited time to ponder their conclusions, and/or have associated skills to evaluate the veracity of their conclusions. Three common untruths that seem pervasive are:
1. Normalizing for natural growth
Most metrics have a tendency to grow on their own accord, from unique page views to population growth, to people who have seen the daily show by Jon Stewart. This does not mean that Jon Stewart is getting more popular, it just says that those that have seen his show cannot undo the fact they have seen his show.
Marketers frequently ignore normal growth when they report new numbers. Maybe you heard that the number of Indians who keep pet elephants in their garage has nearly doubled since 1950. But the total U.S. population has nearly doubled in the same period, from roughly 150 million to 290 million. Thus we are far from being overrun with garage-dwelling pachyderms.
This trick is most used when marketers talk about an all-time box office record with unmatched collections at the ticket counter. What they fail to account for is that dollars are not worth as much as they used to and that the potential movie going audience is larger than before
2. Exceptions become the rules
All human beings like concrete facts rather than abstract statistics. Marketers and politicians know this, and cater to this inherent desire by using “best case” data points that prove their hypothesis. Think of ads for diet supplements, car mileage and other such examples – disclaimers tell the story.
Such measurements will lead one to provide data that fulfill the prophecy. Singling out specific campaigns that have a tremendous ROI are most always suspicious. Maybe those campaigns should be labeled with “results may vary” disclaimers???
3. Picking your measurement point carelessly (or, should I say carefully)
The genesis of this malaise is that of choosing to start or end a comparison at an abnormal point in an ongoing cycle. For example, the conversion this afternoon for electronic goods was 3.5%. Last week’s conversion was 2%. Hence, one can shake their fist in the air and claim a 75% improvement in conversion rates. Of course the conversion in the electronic category at this time of the year (people planning vacations) vary from 1.5% to 4%. So, this fluctuation is pretty normal. Measuring from the low on a bad day to a high on a great day is misleading.
This trick is most used when politicians talk about job gains and losses. One will talk about losses from a high point, whereas the other will talk about gains from a low point.
Of late I have seen many of these claims made by vendors as part of their marketing arsenal – I would urge all to keep the analytics community honest by demanding details.