Everybody needs 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 once you don’t perceive the distinction between correlation and causation, you possibly can 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 prompted 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. Nonetheless, we don’t have sufficient information to grasp 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.
For this reason you should know the right way to inform correlation from causation. With out that understanding, you possibly can find yourself investing within the unsuitable areas of your campaigns. The definitions under will allow you to perceive the qualities of every and the right way to distinguish them.
What’s correlation?
Correlation merely means that there’s a vital relationship between 2 variables. A variable is one thing you possibly can measure—in advertising, consider issues like income, visitors, social shares, the variety of e-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 may be both optimistic or unfavourable. A optimistic 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 nicely. In the event you chart factors on a graph of those 2 variables, the factors type an upward line.
A unfavourable correlation is when the connection reveals that when 1 variable will increase, the opposite decreases. In the event 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 folks incomes grasp’s levels and the overall field workplace income of the movie business have elevated steadily. Each variables elevated, so it is a optimistic correlation. Our instance of emails and gross sales income is one other instance of a optimistic correlation.
For an instance of a unfavourable correlation, think about 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, however it’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 optimistic or unfavourable. In a optimistic 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 unfavourable causal hyperlink, the connection is the other. If A will increase, it causes B to lower, or vice versa. As an example, extra folks 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. Folks typically mistake the two, assuming that as a result of 2 variables have a relationship (whether or not optimistic or unfavourable), 1 should have prompted 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 group’s scratching their heads, questioning why their new marketing campaign isn’t driving vital outcomes.
In actuality, there are numerous different explanation why 2 variables would possibly exhibit a sample in how they modify. Understanding these causes helps you keep away from assuming causation when it’s actually only a correlation.
Third variable (or confounding variable)
As a substitute 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 a substitute of assuming 1 causes the opposite, we should always think about a 3rd variable impacting each: the climate. Increased 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 nicely, or are you not sleeping nicely 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. In the event 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
Fashionable gross sales and advertising campaigns are data-driven, so you should perceive the patterns in information and what relationships they point out. Once 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 continuously 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 selected impact. That doesn’t imply there isn’t one thing to be taught, simply that it’s a must to watch out.
E-mail advertising is a big element of digital advertising, and entrepreneurs typically check a lot of other ways to doubtlessly enhance outcomes. You can 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 kind of subscribers occurred to see the e-mail that day?
That’s why it’s necessary to manage 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 looks like entrepreneurs don’t have time to be this exacting. Within the subsequent part, we’ll cowl the right way to navigate this drawback.
Correlation vs. causation in gross sales
Gross sales groups are additionally more and more searching for to optimize their processes and practices with “large information.” In search of 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, think about when a pricing change appears associated to a change in gross sales. Let’s say your organization sells snow boots. Growing prices power you to lift your costs. And gross sales improve month over month! It would seem to be rising costs prompted gross sales to extend, however it’s a must to think about different elements, like seasonality and whether or not your advertising and product availability has remained constant.
Possibly across the time you needed to increase costs, you additionally had been in a position to 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 numerous elements. Whilst you ought to at all times be in search of methods to optimize the gross sales course of, you also needs to keep in mind that causation is tough to show, and assuming a cause-and-effect relationship may result in false assumptions and dangerous selections.
The right way to decide correlation or causation
Your corporation doesn’t happen in a superbly managed laboratory atmosphere. Some countless variables and situations might be affecting your advertising and gross sales outcomes. However that doesn’t imply you possibly can’t examine your information to learn to optimize your processes.
One of the best ways to ascertain 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 assume will occur in case you make some change to a variable or situation.
For instance, you would possibly hypothesize that sending your month-to-month e-mail e-newsletter earlier within the day will result in a better open price. The variable you’re altering is the time the e-mail e-newsletter is distributed. To get significant proof that your speculation is appropriate, you could preserve all the opposite variables fixed: the topic line, the sender identify, and many others. If some 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. Some of the widespread makes use of of A/B testing is figuring out higher e-mail topic traces. You ship the very same e-mail on the similar time to 2 teams of individuals, with every group getting a unique topic line. If there’s a distinction within the open price or conversion price, you possibly can fairly 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 possibly can nonetheless be looking out for correlation and causation. In the event you or a colleague imagine you see a relationship between 2 issues, ask yourselves:
- What proof is there for a causal relationship?
- What different variables may be affecting the end result?
- Might this be a part of a sequence response?
In a enterprise setting, chances are you’ll not at all times have the ability to distinguish correlation and causation precisely. Nonetheless, you possibly can examine your proof, carry out extra experiments, and make knowledgeable selections.
Steadily requested questions
Solutions to among the commonest questions on correlation vs. causation.
How are you going to 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 wherein 1 variable causes a change within the different.
A rigorously managed experiment is good for figuring out causation. Maintaining every part else the identical, you’d change the variable you assume is the trigger and observe to see if it creates a change within the variable you imagine is affected.
In the event you can’t do such a managed experiment, it’s a good suggestion to search for exterior elements which may 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 delivery container will increase, the load of the container will increase. The addition of merchandise provides further weight and subsequently causes the load of the container to extend.
What’s an instance of correlation however not causation?
The variety of folks shopping for calendars and the variety of folks becoming a member of gyms each improve across the starting of the 12 months. Persons are not becoming a member of a fitness center as a result of they purchased a calendar, nor are they shopping for a calendar as a result of they joined a fitness center. Each variables are affected by the point of 12 months and cultural norms.
Correlation doesn’t suggest causation
Everybody needs to work smarter. Everybody needs to make progress rapidly. 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 in case you’re conscious of the variations between correlation and causation, you possibly can check and analyze your information to deduce when adjustments are price implementing. And with the ever-growing variety of digital buyer interactions, you possibly can accumulate and be taught 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.
