If you jumped in to the new year with plans for revamping data analytics but still haven’t acted on those resolutions, read on to learn the basics for adding simple yet powerful visualization to reports. You may have seen complicated graphs or mind blowing tables and charts, but you don’t need an exorbitant amount of experience to make your own and put your data to work.
Step 1: Find Relevant Data Points
Using relevant data points is the first step to building great visuals. Previously on The Coegi Blog we discussed how to determine KPIs and measure the correct variables for a variety of basic campaign types. It would be a waste of time and effort to put in work on visualizing data that doesn’t really apply to the points that are trying to be made. Before planning your visuals, select the KPIs that actually matter, and make sure any moving parts such as installing pixels or tracking tags are in place before campaign launch. This will ensure accurate data collection and measurement.
Keep in mind it is better to gather excess data and then utilize only what makes sense as opposed to aimlessly grasping at the importance of only a few variables. Whether conducting a branding, conversion, engagement, sales, or any other type of marketing campaign, having relevant data points in mind will save many hours of heartache.
Step 2: Draw Comparisons between KPIs
After accumulating the data it’s essential to draw comparisons between KPIs in order to show what is creating effective marketing. To provide actionable insight into the campaign and compare data points correctly, it is important to understand the difference between correlation and causation. Two variables may be correlated, but that does not imply causation or that one causes the other to behave in a certain way.
For example, it is common knowledge that as ice cream sales increase so does crime rate. Does this mean that ice cream sales cause more crime? Probably not. It simply means the two variables- ice cream sales and crime rate- are correlated. Maybe ice cream sales actually does cause more crime, or maybe there’s just more people out and about with more opportunities to commit a crime during the peak ice cream season. The point is, we can’t imply causation from correlation, and the same principle applies when evaluating campaign data. Look at the correlation between different data points to gain some insight into performance, but don’t confuse correlation with causation.
Step 3: Tell a Story
Finally, the key to getting a constructive marketing message across is the ability to tell a story with the data collected. Now that relevant data points have been selected for comparison, you are ready finish visualizing the entire picture with clear graphs and charts to elaborate on both the positive and negative aspects of the campaign.
Tying all of the information together can make or break analysis. By providing a narrative about the campaign with visual assets, you will be able to show off strong KPI growth, or in some cases, identify campaign problems and find potential solutions. Either way, data visualization is both informative and helpful for making campaign optimizations and future marketing decisions.
In summary, finding relevant data points, drawing useful comparisons, and telling a story with the data will avoid leading you or your client down the wrong path, or worse, in no particular direction at all- “Oh this graph makes a great point, as we increased marketing spend we came closer to spending the budget in full.” Think before you data.