The march to data-driven advertising and marketing lately has been as relentless because the movement of lava down the perimeters of an erupting volcano.
Using knowledge in advertising and marketing is in no way new, however entrepreneurs now have entry to an enormous quantity of knowledge concerning clients and potential consumers. Equally vital, additionally they have entry to highly effective and reasonably priced analytics applied sciences.
At the moment, it is almost not possible to discover a marketer who does not assume utilizing the precise knowledge in the precise methods can enhance advertising and marketing efficiency.
A lot of the heavy lifting in advertising and marketing knowledge evaluation includes correlation. In easy phrases, correlation is a relationship between phenomena or issues – “variables” within the lingo of math and statistics – that are inclined to fluctuate or happen collectively in ways in which aren’t because of likelihood alone.
It is not stunning that correlation performs such a central position in advertising and marketing analytics. A single knowledge level can present helpful info, however the true energy of analytics is its means to establish and quantify relationships between two or extra “variables” in your advertising and marketing knowledge. Understanding these relationships can allow entrepreneurs to make choices that enhance advertising and marketing efficiency.
Correlation ≠ Causation
One of many elementary rules of knowledge evaluation is that correlation doesn’t set up causation. In different phrases, knowledge evaluation might present that two occasions or situations are strongly correlated statistically, however this alone does not show that one of many occasions or situations induced the opposite.
The next chart offers an illustrative instance of why entrepreneurs should always remember the excellence between correlation and causation. It reveals that from 1999 by 2009 there was a powerful correlation ( r = 0.99789126 for you knowledge geeks) between US spending on science, house, and know-how, and the variety of suicides by hanging, strangulation, and suffocation. (Notice: To see this and different nonsensical correlations check out Spurious Correlations.)
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| Supply: Tyler Vigen, Spurious Correlations |
I doubt any of us would argue that there is a causal relationship between these two variables (regardless of the robust correlation) as a result of they simply haven’t got a believable relationship. In advertising and marketing, nonetheless, it is simple to come across occasions which might be strongly correlated and have a believable cause-and-effect relationship. The issue is, the causal relationship, whereas believable, could be weak or nonexistent.
When To Rely On Correlation
It is preferable, after all, to base advertising and marketing choices and actions on confirmed cause-and-effect relationships, however this may occasionally not at all times be real looking and even doable. Proving the existence of a causal relationship sometimes requires the usage of a well-designed and tightly managed experiment. In advertising and marketing, such experiments could be simple to conduct in some conditions, however troublesome, if not not possible, to run in others.
Beneath these circumstances, the true query is: When ought to entrepreneurs act primarily based on a correlation?
David Ritter with the Boston Consulting Group described a course of for answering this query in an article revealed on the Harvard Enterprise Assessment web site a number of years in the past. I’ve used Ritter’s course of – with a few minor modifications – quite a few occasions in my work with shoppers, and I’ve discovered it to be efficient at focusing the eye of decision-makers on the precise points.
The diagram beneath is my adaptation of Ritter’s framework.
Whether or not you must depend on a correlation relies upon totally on two components – your confidence within the correlation as an indicator of trigger and impact, and the stability of dangers and rewards.
Confidence within the correlation – The primary issue is your degree of confidence that the correlation factors to an actual cause-and-effect relationship. This issue is in flip a operate of two issues:
- How typically the correlation has occurred previously. The extra ceaselessly occasions have occurred collectively, the extra possible it’s they’re causally associated.
- The variety of doable explanations for the impact into account. For instance, your knowledge might present a powerful correlation between the variety of advertising and marketing emails despatched and income progress throughout a given interval. However, if there are a number of believable explanations for the elevated income, you have got much less cause to assume there is a causal connection between the variety of emails despatched and income progress.
The stability of dangers and rewards – The second issue concerned in figuring out whether or not you must depend on a correlation is an analysis of dangers and rewards. Any resolution primarily based on a correlation ought to embody an evaluation of the potential dangers and advantages related to the motion.
The above diagram illustrates how these two components are used collectively that will help you resolve whether or not you must act primarily based on a correlation.
I must make two factors about utilizing this framework. First, it is vital to undergo this evaluation for every motion you are contemplating. If you establish a correlation, there’ll in all probability be a number of methods you possibly can act on that correlation. Every choice ought to be evaluated individually as a result of they’ll in all probability have totally different risk-reward profiles.
It is also vital to think about the dimensions of the “hole” between the potential dangers and rewards. For instance, if a possible motion has big potential advantages and really low dangers, chances are you’ll need to act even when your confidence that the correlation signifies a cause-and-effect relationship is not very excessive.
High picture courtesy of International Panorama by way of Flickr (CC).
