Four Requirements of Great Marketing Data Visualization

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Four Requirements of Great Marketing Data Visualization

Shashi asks, “People are very visual. What is your best tool for a novice marketer to create good visuals with data?”

The best tool? It depends. If we think of a visualization like a cooked dish, like a cake, then we have to have four things:

  • The recipe. What are we making? What should it look like when it’s done, and how should it taste?
  • The ingredients. What do we have to work with, and are the ingredients good? No matter how skilled you are, if all you have is a bag of sand, you’re not making a cake.
  • The skill. Do we know how to bake?
  • The tools. What will we make the cake with?

Obviously, some tools are more important than others; it’d be almost impossible and certainly very frustrating to make a cake without a mixing bowl of some kind.

The Four Requirements of Great Marketing Data Visualization

The same is true in marketing analytics.

Four Key Components of Marketing Reporting

Our visualization, our outcome, is like the cake baking process:

  • The recipe. What should the visualization be communicating and more critically, what is the objective?
  • The ingredients. What data do we have to work with, and is it any good?
  • The skill. Tools can help, but all of them still require skills – in this case, knowing what visualizations communicate insights best, as well as the necessary skills to process the data for the visualization.
  • The tools. What tools will you use that best help you work with the above three criteria?

Here’s where most marketers go wrong, most people go wrong in marketing analytics and marketing technology. They start with the tools, with figuring out tools. That’s like getting out an appliance and not knowing what you’re cooking. You may have a great waffle iron, but if you’re making a cake… not so helpful.

The recipe, ingredients, and skill dictate the tool. They define the requirements for the tool and help you narrow down what tools should be available to you. Let’s look at a couple of examples.

Example 1: Simple Web Analytics

What you’ve been asked to create, your recipe, is a dashboard of some kind to show the performance of your website or mobile app. That’s what you’re trying to bake, and you have a list of things that should be on it, like a line graph showing traffic over time.

Your ingredients are Google Analytics data.

Your skill is the ability to use basic productivity software.

What tool should you use? In this specific scenario, without a doubt, the tool to use is Google Data Studio. It’s functionally very similar to Google Slides and Google Sheets, it requires little to no coding skills, and it’s highly compatible with Google data.

Now imagine how this would be different if you used, say, Adobe Analytics. You’d have a very different set of ingredients, and Google Data Studio might not be the best tool for the job any more.

Imagine how this would be different if you were asked to show a multi-touch attribution model. Knowing that, you’d need both different skills and a different tool – probably a person who can program in R or Python and with a data science or machine learning background. In this scenario, you’d probably be looking at IBM Watson Studio or another machine learning tool.

Example 2: Marketing ROI

What you’ve been asked to create, your recipe, is a visualization of marketing ROI over time, a simple line graph showing the fluctuations of your campaign data.

You’ll need to gather a lot of ingredients for this recipe. You’ll need hard cost data – what you spent in currency – on things like ads. You’ll need soft cost data as well, the time people spent on specific tasks and their effective hourly bill rates. You’ll probably need data from accounting about overhead costs per employee as well. And you’ll need marketing performance data, how much revenue is attributable to each marketing channel.

This, by the way, is why marketing ROI is so elusive for so many of us. Despite the simplicity of its formula – (earned – spent)/spent – the data that goes into that formula is incredibly complex and lives in multiple parts of the organization if you want to do it right.

Your skill is the ability to aggregate all that data together, probably in some kind of data storage engine, and then perform the math on it. The math isn’t overly complicated – it’s the aggregation and computation of the data that will be the biggest skill gap.

What tool should you use? That depends on how you gather, store, and process the data. You could do it in spreadsheets if you’re an Excel deity, but you’d be better off doing it with a database so that you can bring in the data regularly and frequently. You’ll also need to be fluent with database querying, so that you can perform complex joins and filtering on the data. You’re probably looking at a SQL database of some kind and spreadsheet software.

The reporting software you use to visualize that final result, the simple line graph, can be anything – it’s all the pieces under the hood that will be the biggest challenge.

Know the Four Parts Before You Start

Understanding what you have to work with is critical before you select any tool. If you don’t know what the outcome is you’re trying to achieve, if you don’t know what kind of data you’re working with, and you don’t know what skills people do or don’t have, any effort you make is doomed from the start. You’ll waste precious time and frustrate everyone involved with the project.

Do the work up front to figure out what you’re baking, whether you have the right ingredients, and whether anyone can bake. If you come up short, you’ll know what you need to remediate before worrying about what tools to use.

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