The march to data-driven advertising in recent times has been as relentless because the circulation of lava down the edges of an erupting volcano.
The usage of information in advertising is on no account new, however entrepreneurs now have entry to an enormous quantity of knowledge relating to prospects and potential patrons. Equally vital, additionally they have entry to highly effective and reasonably priced analytics applied sciences.
At present, it is almost not possible to discover a marketer who does not suppose utilizing the suitable information in the suitable methods can enhance advertising efficiency.
A lot of the heavy lifting in advertising information evaluation entails correlation. In easy phrases, correlation is a relationship between phenomena or issues – “variables” within the lingo of math and statistics – that are inclined to range or happen collectively in ways in which aren’t as a result of probability alone.
It is not shocking that correlation performs such a central function in advertising analytics. A single information level can present helpful info, however the actual energy of analytics is its capacity to determine and quantify relationships between two or extra “variables” in your advertising information. Understanding these relationships can allow entrepreneurs to make choices that enhance advertising efficiency.
Correlation ≠ Causation
One of many basic rules of knowledge evaluation is that correlation doesn’t set up causation. In different phrases, information 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 supplies an illustrative instance of why entrepreneurs should always remember the excellence between correlation and causation. It exhibits that from 1999 by way of 2009 there was a powerful correlation ( r = 0.99789126 for you information geeks) between US spending on science, house, and expertise, and the variety of suicides by hanging, strangulation, and suffocation. (Be aware: 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 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, in fact, to base advertising choices and actions on confirmed cause-and-effect relationships, however this will likely not all the time be lifelike and even attainable. 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 simple to conduct in some conditions, however troublesome, if not not possible, to run in others.
Beneath these circumstances, the actual 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 Evaluation web site a couple of years in the past. I’ve used Ritter’s course of – with a few minor modifications – quite a few instances in my work with shoppers, and I’ve discovered it to be efficient at focusing the eye of decision-makers on the suitable 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 operate of two issues:
- How usually the correlation has occurred prior to now. The extra ceaselessly occasions have occurred collectively, the extra seemingly it’s they’re causally associated.
- The variety of attainable explanations for the impact into consideration. For instance, your information might 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 cause to suppose 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 determination 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 it’s best to 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. Whenever you determine a correlation, there’ll most likely be a number of methods you would act on that correlation. Every choice must be evaluated individually as a result of they are going to most likely have totally different risk-reward profiles.
It is also vital to contemplate the scale of the “hole” between the potential dangers and rewards. For instance, if a possible motion has large 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 International Panorama by way of Flickr (CC).
