The march to data-driven advertising lately has been as relentless because the move of lava down the perimeters of an erupting volcano.
Using knowledge in advertising is certainly not new, however entrepreneurs now have entry to an unlimited quantity of information relating to clients and potential consumers. Equally necessary, additionally they have entry to highly effective and reasonably priced analytics applied sciences.
At the moment, it is practically unimaginable to discover a marketer who does not assume utilizing the precise knowledge in the precise methods can enhance advertising efficiency.
A lot of the heavy lifting in advertising knowledge evaluation entails correlation. In easy phrases, correlation is a relationship between phenomena or issues – “variables” within the lingo of math and statistics – that are likely to range or happen collectively in ways in which aren’t because of likelihood alone.
It isn’t shocking that correlation performs such a central position in advertising analytics. A single knowledge level can present helpful info, however the actual energy of analytics is its potential to determine and quantify relationships between two or extra “variables” in your advertising knowledge. Understanding these relationships can allow entrepreneurs to make choices that enhance advertising efficiency.
Correlation ≠Causation
One of many basic rules of information evaluation is that correlation doesn’t set up causation. In different phrases, knowledge evaluation could present that two occasions or circumstances are strongly correlated statistically, however this alone does not show that one of many occasions or circumstances brought on the opposite.
The next chart supplies an illustrative instance of why entrepreneurs should always remember the excellence between correlation and causation. It reveals that from 1999 via 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. (Word:Â To see this and different nonsensical correlations check out Spurious Correlations.)
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| Supply:Â 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 only haven’t got a believable relationship. In advertising, nonetheless, it is easy to come across occasions which can be strongly correlated and have a believable cause-and-effect relationship. The issue is, the causal relationship, whereas believable, may be weak or nonexistent.
When To Rely On Correlation
It is preferable, after all, to base advertising choices and actions on confirmed cause-and-effect relationships, however this will likely not all the time be real looking and even doable. Proving the existence of a causal relationship usually requires the usage of a well-designed and tightly managed experiment. In advertising, such experiments may be straightforward to conduct in some conditions, however tough, if not unimaginable, to run in others.
Below these circumstances, the actual query is:Â When ought to entrepreneurs act based mostly 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 Evaluate web site a couple 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 purchasers, 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 it’s best to depend on a correlation relies upon totally on two components – your confidence within the correlation as an indicator of trigger and impact, and the steadiness of dangers and rewards.
Confidence within the correlation – The primary issue is your stage of confidence that the correlation factors to an actual cause-and-effect relationship. This issue is in flip a perform of two issues:
- How typically the correlation has occurred previously. The extra continuously occasions have occurred collectively, the extra probably it’s they’re causally associated.
- The variety of doable explanations for the impact into account. For instance, your knowledge could present a powerful correlation between the variety of advertising emails despatched and income progress throughout a given interval. However, if there are a number of believable explanations for the elevated income, you could have much less motive to assume there is a causal connection between the variety of emails despatched and income progress.
The steadiness of dangers and rewards – The second issue concerned in figuring out whether or not it’s best to depend on a correlation is an analysis of dangers and rewards. Any resolution based mostly on a correlation ought to embrace an evaluation of the potential dangers and advantages related to the motion.
The above diagram illustrates how these two components are used collectively that can assist you resolve whether or not it’s best to act based mostly on a correlation.
I have to make two factors about utilizing this framework. First, it is necessary to undergo this evaluation for every motion you are contemplating. While you determine a correlation, there’ll in all probability be a number of methods you could possibly act on that correlation. Every choice must be evaluated individually as a result of they are going to in all probability have completely different risk-reward profiles.
It is also necessary to think about the scale of the “hole” between the potential dangers and rewards. For instance, if a possible motion has enormous 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.
Prime picture courtesy of World Panorama by way of Flickr (CC).
