We go through life in a time-oriented manner, through cause and effect.

Naturally, we look for connections between one event and the next. 

For example:

First Josh hit his thumb with a hammer, then he got a bruise.

First Beckie ate some grapes, then she became sick.

Most of us would conclude that being hit with a hammer is what caused Josh to get a bruise, and that eating the grapes is what caused Beckie to become sick. This is what’s known as causation, where one event (being hit by a hammer/eating the grapes) caused another event (getting a bruise/becoming sick).

These two examples are pretty clear cut and sometimes the cause of an event is obvious. However, as we’re so geared up to look for links between events, we can often claim causation without considering all the other variables.

This means that it’s often not causation at all, but actually correlation at work.

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the other variable. Causation indicates that one event is the result of the occurrence of the other event; i.e. there is a causal relationship between the two events.

Don’t make the mistake of assuming causation, when actually, it’s only correlation.

Many companies are guilty of making the following assumption:

“We ran this campaign and saw a huge increase in applications, thus our campaign must have been the cause of the increase in applications.”

However, this might not necessarily be the case.

Let’s use an example. Say we launch a two month campaign in November to advertise jobs for a retail company. At the end of the campaign the retail company comes back to us and says,

“We’ve seen a 50% increase in applications during the time that your campaign ran,”

We could sit back and think, “Well done us! What a success our campaign must have been to cause such as increase in applications.”

It’s not an illogical assumption to make (and it’s one made so often). However, what if I told you that the retail company recruits for their Christmas temp roles during November and December?

Suddenly our campaign looks less likely to be the cause of the increase in applications and it’s more likely to be the result of the retail company’s seasonality.

On the flip side, say we run a campaign for a tech company and at the end of the campaign they come back to us and say,

“We’ve had hardly any applications during the time the campaign has run.”

We might worry and think that the campaign was ineffective, and that we didn’t do a good job. However, what if during the length of our campaign the company had far fewer job openings than in previous months? Our campaign may well have been successful in getting applicants for those roles, but naturally, you’ll still see fewer applications if you have less jobs to apply for.

How can you know if a campaign is actually the cause of your increase in applications or hires (i.e. is it successful or not?)

The answer is simple. Use metrics.

The beauty of tagging and tracking campaigns is that not only can we know where your applications are coming from (and which ads they interact with) but we can also use the data to determine how successful a campaign is.

Take the earlier example of the retail company. Let’s say there were 1,300 extra applications made during November compared to October. If I tagged and tracked that campaign and it showed me that out of these potential candidates, 1,050 people had interacted with the campaign, then it’s more likely that the campaign had a positive effect and did influence those extra applications.

The same goes for the example with the tech company. If we used metrics to track the campaign and saw that 300 out of the 400 applications received, during the length of time the campaign was live, were from applicants interacting with our campaign, then we could say our campaign was successful in driving applications, even though the total applications were lower than the previous month.

The decrease in applications is more likely because of the lack of job openings during the campaign. We could advise to run the campaign again when they have more job openings to better measure the success.

Metrics and data opens the door to understanding your applicants and their journey to applying and being hired. It helps improve campaigns by building on previous successful (or unsuccessful) campaigns where we can see what went well and what didn’t.

Ultimately, metrics and data pave the way to creating more successful campaigns in the future.

Metrics Analyst

As a Metrics Analyst at AIA Worldwide, Emma is responsible for tracking and reporting on clients' campaign data, from basic website analytics to fully fledged cookie based tracking. With a passion for numbers and a background in mathematics, Emma loves analysing and applying mathematical theories to real world data.