Please allow me to shed some light toward a frequent misnomer in today's vocabulary and in data marketing-- the word, correlated. The expression has been certainly used in marketing for years, both behind the scenes or when marketing a product to a target audience. Check out high cholesterol medicine, for instance. Marketers have skillfully suggested that these goods help stop heart troubles because cholesterol and cardiovascular disease are correlated. A little something isn't really right here though.
Marketers, specifically those concentrated on data marketing, have to beware that a correlation amongst two groups of data does not reveal that that one dataset impacts the other. Simply put, a correlation does not supply statistical proof of a cause-and-effect connection. I'm not pointing out that it wasn't great marketing to mention the correlation involving cholesterol and cardiovascular disease, however beware deriving your marketing judgments simply on correlations in your database.
Did you know that the event of diaper rash and construction is exceptionally correlated? Does this indicate that one leads to the other? No. The missing connection is hot climate. Diaper rash and street construction both happen to transpire throughout the hot times of the year, but neither one are immediately associated with one another (at least to our knowledge).
Regrettably, marketers have contributed to creating some complexity about this topic, so think of this our effort to help put the record straight at the very least in the marketing world. Correlations can help direct your marketing selections, but don't ever always totally trust in them. When you investigate your audience and exactly how they reply to your marketing, take a minute to think about one level below what the correlations are explaining to you. Is it actually only because they're males that they are responding far better? Is the real cause the aspect that they're in between the ages of 18 and 24? Digging deeper utilizing sophisticated data marketing or having a closer look at your target audiences' qualities will certainly expose a clearer image of what honestly leads them to act.
Marketers, specifically those concentrated on data marketing, have to beware that a correlation amongst two groups of data does not reveal that that one dataset impacts the other. Simply put, a correlation does not supply statistical proof of a cause-and-effect connection. I'm not pointing out that it wasn't great marketing to mention the correlation involving cholesterol and cardiovascular disease, however beware deriving your marketing judgments simply on correlations in your database.
Did you know that the event of diaper rash and construction is exceptionally correlated? Does this indicate that one leads to the other? No. The missing connection is hot climate. Diaper rash and street construction both happen to transpire throughout the hot times of the year, but neither one are immediately associated with one another (at least to our knowledge).
Regrettably, marketers have contributed to creating some complexity about this topic, so think of this our effort to help put the record straight at the very least in the marketing world. Correlations can help direct your marketing selections, but don't ever always totally trust in them. When you investigate your audience and exactly how they reply to your marketing, take a minute to think about one level below what the correlations are explaining to you. Is it actually only because they're males that they are responding far better? Is the real cause the aspect that they're in between the ages of 18 and 24? Digging deeper utilizing sophisticated data marketing or having a closer look at your target audiences' qualities will certainly expose a clearer image of what honestly leads them to act.