Everybody desires to be data-driven of their advertising and gross sales course of. This can be a good aim. Understanding your information and making use of that perception means that you can optimize your campaigns and drive extra visitors, conversions, and gross sales.
Typically, these “insights” come within the type of trigger and impact: “after we do X, it leads to Y. So let’s do extra of X.” However many entrepreneurs don’t analyze the info deep sufficient and mistake correlation for causation.
And whenever you don’t perceive the distinction between correlation and causation, you’ll be able to misread your information and make misguided selections. You possibly can waste some huge cash and time on ineffective and even detrimental channels and techniques.
On this article, we’ll cowl what correlation and causation are, how they differ, and clarify how that impacts advertising and gross sales. Lastly, we’ll share some sensible methods to constantly inform correlation from causation in a enterprise setting.
What’s correlation vs. causation?
Let’s say you elevated the variety of gross sales emails you despatched final quarter. You went from sending 1,000 emails per 30 days to sending 3,000 per 30 days. On the similar time, your gross sales income elevated. Does that imply that sending extra emails brought on you to make extra gross sales?
Not precisely. We will see that there’s a connection, i.e., a correlation, between the two metrics. As 1 goes up, the opposite intently follows. Nevertheless, we don’t have sufficient information to know if that’s why the income elevated.
What if the variety of emails you despatched elevated as a result of different advertising channels drove considerably extra focused leads, thus driving up the overall variety of sends? It may even be an outdoor issue, like elevated model recognition that drives each extra leads and makes them simpler to shut.
That is why that you must know inform correlation from causation. With out that understanding, you’ll be able to find yourself investing within the fallacious areas of your campaigns. The definitions under will allow you to perceive the qualities of every and distinguish them.
What’s correlation?
Correlation merely means that there’s a vital relationship between 2 variables. A variable is one thing you’ll be able to measure—in advertising, consider issues like income, visitors, social shares, the variety of electronic mail campaigns, or advert spend.
The two variables within the instance we launched above are the variety of emails despatched and the gross sales income.
A correlation will be both constructive or adverse. A constructive correlation is when each variables improve or lower collectively. In different phrases, when 1 variable will increase, so does the opposite, and when 1 variable decreases, the opposite does as properly. When you chart factors on a graph of those 2 variables, the factors type an upward line.
A adverse correlation is when the connection reveals that when 1 variable will increase, the opposite decreases. When you chart factors on a graph of those 2 variables, the factors type a downward line.
For instance, over the previous century, the variety of individuals incomes grasp’s levels and the overall field workplace income of the movie trade have elevated steadily. Each variables elevated, so it is a constructive correlation. Our instance of emails and gross sales income is one other instance of a constructive correlation.
For an instance of a adverse correlation, take into account the gross sales of smartphones and the weekday circulation of newspapers. Since 2007, smartphone gross sales have elevated whereas newspaper circulation has decreased.
What’s causation?
Like correlation, causation is a relationship between 2 variables, but it surely’s a way more particular relationship. In a causal relationship, 1 of the variables causes what occurs within the different variable.
A causal hyperlink may also be both constructive or adverse. In a constructive causal hyperlink, the rise or lower of 1 variable causes the identical change within the affected variable. So if A will increase, B will increase. And if A decreases, B additionally decreases. For instance, extra rainfall will trigger the native river’s water ranges to rise.
In a adverse causal hyperlink, the connection is the alternative. If A will increase, it causes B to lower, or vice versa. For example, extra individuals driving into city and parking their vehicles will trigger there to be fewer empty parking areas.
Why does correlation not equal causation?
As we’ve lined, a correlation simply means that there’s some relationship between 2 variables. In distinction, causation implies that the change in 1 variable is inflicting the change within the different. Individuals typically mistake the two, assuming that as a result of 2 variables have a relationship (whether or not constructive or adverse), 1 will need to have brought on the opposite.
Entrepreneurs are particularly responsible of this. “Look, we did X, and our gross sales elevated!” Cue plowing time, effort, and sources into extra of the identical. Two months later, the workforce’s scratching their heads, questioning why their new marketing campaign isn’t driving vital outcomes.
In actuality, there are various different the reason why 2 variables would possibly exhibit a sample in how they alter. Understanding these causes helps you keep away from assuming causation when it’s actually only a correlation.
Third variable (or confounding variable)
As an alternative of 1 of the two variables inflicting the change within the different, there could also be a 3rd variable that impacts each. One traditional instance is that ice cream gross sales improve as charges of sunburn improve. As an alternative of assuming 1 causes the opposite, we must always take into account a 3rd variable impacting each: the climate. Larger temperatures and extra sunshine have an effect on each ice cream gross sales and sunburn charges.
Directionality points
The problem of directionality refers to when it’s unclear whether or not variable A is inflicting variable B or if variable B is inflicting variable A. For instance, are you consuming extra espresso since you didn’t sleep properly, or are you not sleeping properly since you’ve been consuming extra espresso?
This can be a traditional drawback with advertising and gross sales, as every division will fortunately take the glory for a rise in income and drum up some metrics that appear to show that “we did it.”
Chain response
Much like the third variable challenge, chain reactions are when 1 or extra different variables act as an middleman between A and B. Somewhat than A inflicting B, maybe A is inflicting a change in variable C, and the change in C impacts B. When you had been to vary one thing about variable C, the correlation between A and B would possibly disappear.
How correlation vs. causation impacts your online business
Trendy gross sales and advertising campaigns are data-driven, so that you must perceive the patterns in information and what relationships they point out. Whenever you perceive the place correlation and causation present up in your online business, you’ll be higher ready to determine every.
Correlation vs. causation in advertising
In any efficient digital advertising marketing campaign, you’re continually making adjustments and changes on the fly. And with so many variables, it may be difficult to find out that 1 trigger is having a specific impact. That doesn’t imply there isn’t one thing to study, simply that it’s a must to watch out.
E mail advertising is a big element of digital advertising, and entrepreneurs typically check a number of alternative ways to probably enhance outcomes. You would change the topic line and see a higher open price. However what if that open price is affected by the point of day, the day of the week, and even which sort of subscribers occurred to see the e-mail that day?
That’s why it’s necessary to regulate different variables and check 1 factor at a time (and with sufficient quantity to get a statistically vital consequence).
Within the quest for enchancment, it typically seems like entrepreneurs don’t have time to be this exacting. Within the subsequent part, we’ll cowl navigate this drawback.
Correlation vs. causation in gross sales
Gross sales groups are additionally more and more looking for to optimize their processes and practices with “massive information.” On the lookout for exterior elements that have an effect on gross sales may also be key to planning and technique.
For a easy instance of how correlation and causation can get fuzzy, take into account when a pricing change appears associated to a change in gross sales. Let’s say your organization sells snow boots. Growing prices drive you to boost your costs. And gross sales improve month over month! It would appear to be rising costs brought on gross sales to extend, however it’s a must to take into account different elements, like seasonality and whether or not your advertising and product availability has remained constant.
Perhaps across the time you needed to increase costs, you additionally had been capable of get your snow boots in a brand new retailer. Or perhaps, it’s late fall, and the approaching winter means demand for snow boots has skyrocketed.
As in advertising, your gross sales outcomes could also be affected by varied elements. Whilst you ought to all the time be searching for methods to optimize the gross sales course of, you must also do not forget that causation is troublesome to show, and assuming a cause-and-effect relationship may result in false assumptions and dangerous selections.
Easy methods to decide correlation or causation
Your online business doesn’t happen in a superbly managed laboratory setting. Some infinite variables and circumstances might be affecting your advertising and gross sales outcomes. However that doesn’t imply you’ll be able to’t examine your information to discover ways to optimize your processes.
One of the best ways to determine a cause-and-effect relationship is to vary simply 1 factor and see what occurs. You possibly can consider this as testing your speculation. A speculation simply means what you suppose will occur should you make some change to a variable or situation.
For instance, you would possibly hypothesize that sending your month-to-month electronic mail e-newsletter earlier within the day will result in the next open price. The variable you might be altering is the time the e-mail e-newsletter is distributed. To get significant proof that your speculation is right, you need to maintain all the opposite variables fixed: the topic line, the sender title, and so forth. If every other particulars are modified, you gained’t have the ability to say that the ship time affected the open price conclusively. That is the place A/B testing is available in.
A/B testing means that you can change and check simply 1 variable in your advertising or buyer expertise at a time. One of the crucial widespread makes use of of A/B testing is figuring out higher electronic mail topic strains. You ship the very same electronic mail on the similar time to 2 teams of individuals, with every group getting a special topic line. If there’s a distinction within the open price or conversion price, you’ll be able to moderately assume that the topic line was the trigger (if there’s a statistically vital distinction).
In conditions the place you don’t have as a lot management, you’ll be able to nonetheless be looking out for correlation and causation. When you or a colleague consider you see a relationship between 2 issues, ask yourselves:
- What proof is there for a causal relationship?
- What different variables is perhaps affecting the end result?
- Might this be a part of a series response?
In a enterprise setting, you could not all the time have the ability to distinguish correlation and causation precisely. Nonetheless, you’ll be able to examine your proof, carry out extra experiments, and make knowledgeable selections.
Steadily requested questions
Solutions to a number of the commonest questions on correlation vs. causation.
How will you know if a relationship is causal or correlational?
Correlation is when there may be an observable relationship between 2 variables. Causation is a particular relationship by which 1 variable causes a change within the different.
A fastidiously managed experiment is good for figuring out causation. Retaining all the pieces else the identical, you’ll change the variable you suppose is the trigger and observe to see if it creates a change within the variable you consider is affected.
When you can’t do such a managed experiment, it’s a good suggestion to search for exterior elements that could be inflicting a correlation.
What’s an instance of causation?
Causation refers to 1 variable inflicting a change in one other—for instance, because the variety of merchandise in a transport container will increase, the burden of the container will increase. The addition of merchandise provides extra weight and subsequently causes the burden of the container to extend.
What’s an instance of correlation however not causation?
The variety of individuals shopping for calendars and the variety of individuals becoming a member of gyms each improve across the starting of the 12 months. Persons are not becoming a member of a gymnasium as a result of they purchased a calendar, nor are they shopping for a calendar as a result of they joined a gymnasium. Each variables are affected by the point of 12 months and cultural norms.
Correlation doesn’t suggest causation
Everybody desires to work smarter. Everybody desires to make progress shortly. These wishes make it tempting to see relationships the place you need them and assume trigger and impact. In advertising and gross sales, this might lead you to waste time and sources on adjustments that don’t really trigger enchancment.
The excellent news is that should you’re conscious of the variations between correlation and causation, you’ll be able to check and analyze your information to deduce when adjustments are price implementing. And with the ever-growing variety of digital buyer interactions, you’ll be able to acquire and study from information greater than ever earlier than.
To get entry to finish buyer (and prospect) information and begin leveraging A/B testing in your campaigns to fact-check your data-based hunches, entry your free trial of ActiveCampaign.
