I have always had, and will always have, enormous respect for project managers.
Especially in marketing where work quickly becomes a tangled mass of to-do lists, random (and sometimes conflicting) data visualization, lofty goals, and marketing technology rabbit trails, project managers serve as welcome beacons for organizations. But as marketing teams continue to grow and become ever more common in the business mix of many companies, project managers have grown into taking on the dual responsibility of managing teams and communicating with stakeholders.
But, say you’re a project manager who, in the midst of an insurmountable pile of ongoing projects and data, just left a meeting with one more major, high-priority task. Something that’s going to take a few days of work—meaning all those other projects will fall by the wayside—and your senior staff wants an update by Thursday. How can you keep all those projects on the front burner while ensuring your team is up to date and properly equipped and that all stakeholders are happy and informed of all the progress you’re making?
Effective data visualization lies very comfortably at this intersection of being actionable and informative—so long as a marketer knows how to approach it effectively.
Embracing data and constructing nice visuals seems well and good enough. Pretty much every marketing manager today can wax poetic about data, while every day offers a new white paper or research study with simple but engaging visuals. Your brand should be able to do all of this just as easily, right?
If only this were the case.
Project managers have to rely heavily on their creative thinking skills to answer the host of difficult questions they face before they can effectively use data on their team. What information should be compiled, and how frequently? What’s the best way to present this data once compiled? Which marketing technology tools do I need to effectively collect and present my data? How much work should be placed on my team to gather and organize this data, rather than actually working on their projects?
While all of these questions (and many more) will have to be answered in time, there’s only a handful that managers have to tackle on the front end to make sure their data engine is humming along nicely.
The Right Questions
An easy mistake to fall into when first approaching a project like this is to try and do too much, too early. Start by tackling these fundamental questions first:
Who do I need to share the data with?
This is as basic as it gets: who is actually going to be reading the data you compile? As with all things in marketing, you can’t effectively communicate without first knowing your audience, and this remains just as true internally for your brand as it is true externally.
What decisions are they hoping to make from this data?
Think for a moment about the “why” of pulling your data together can help you narrow down what you need and how to best present it to streamline decision making.
What KPIs most effectively speak to these decisions?
The less you have to track down and present, the more quickly and accurately you’ll be able to present data on a regular basis.
What tools do I need to gather and communicate this data?
This is the question that far too many marketers start with, rather than end with. There are a lot of very cool dashboards and reporting tools out on the market today. But using attractive functionality, rather than team needs, to direct your tech will just result in too many disparate tools pulling conflicting data. Try to answer this question with as few platforms as possible. (And if you’re looking to create interactive content to make your stories sing, check out Ceros!)
Accessibility and Communication
After answering these questions, you’re likely going to have a lot of work on your hands. If old gaps in your analytical tracking are going to come back to haunt you, it will likely happen at this stage. Take the time to set it up right however, and you’ll save immense amounts of work for you and your team into the future.
With your foundation settled however, you now two more specific questions to consider: how, and how much do you share of your data?
Consider regular web traffic reporting for instance. You might want to report on some basic site metrics— organic traffic, bounce rate over time, time on page— in addition to some custom goals you’ve set up like a generated lead or a sale. So you pull it all together into one place, export it as a PDF once a month, and send it off to all of your relevant team members and stakeholders. Case closed, right?
Well actually, your marketing team is sending you messages about why they can’t drill down the data more specifically or look at it more frequently than once a month, while your leadership stakeholders (who may not have a lot of marketing savvy) are battering you with questions and ultimatums based on a single data point taken out of context. Instead of facilitating work, you’ve created an internal roadblock.
Just as important as gathering and presenting data is considering the manner in which you make that data accessible and communicate it. This type of partitioning can largely be broken down into three sections:
For members of your team who work with this data on a regular basis and are trained in what each data point means, all you have to do is help set up systems to make data more easily accessible. Rather than reports, building out tools like Google Analytics Dashboards will facilitate your team’s work—without hampering their ability to access data on the fly or in detail.
Maybe your whole team doesn’t need full analytic access, but they’re still curious to know how things are going. This presents a great opportunity to communicate goals to your entire team, tie these goals to a handful of specific metrics, and then come up with a regular way of sharing with your team how they’re doing in comparison to their goals. This can be as simple as a weekly emailed update, or as attractive as real-time dashboards on televisions around your office.
The “Tell, Don’t Show” View
You’ll likely have to use your material to communicate with leadership about how your team is progressing. Especially where marketing knowledge may not be assumed, these types of reports should aim to be the most attractive and immediately understandable. But most importantly, this data should be presented in a way that allows you to actively interpret and provide context for every number. A written report or in-person presentation will make it easier to address question up front and keep interpretation in line with marketing reality.
Tools of the Trade
The last bit after you’ve figured out all of this organization and direction is to identify what tools will help you best accomplish these goals. This can be a difficult and expensive phase if your let your eyes get bigger than your marketing plate. Remember, the goal should be the fewest amount of tools possible. Consolidation in this case makes everything easier.
A good place to start is by trying to funnel everything into one of two places: either a marketing dashboard service like Domo or Cyfe, or a spreadsheet. Where marketing specific dashboards can help you integrate easily with a wide range of data reporting sources, spreadsheets remain supreme in terms of freedom to organize and present your data on your own accord. For example, the Google Analytics Plugin for Google Sheets allows you to pull data from your account for use however you see fit, allowing comparisons to be made across data views that can’t normally be down in Google Analytics’ platform.
In terms of creating visuals from your consolidated data, there are a number of free and easy ways to go. For more basic or infographic style presentations, tools like Canva or Piktochart can go a long way towards giving your presentations a graphic artist style finish. For more technical teams, tools like plot.ly and Gephi make it easy to create slightly more involved visuals that include a wide variety of metrics— more useful for decision making within marketing teams, rather than informing a wider audience.
At its most basic, data reporting and visualization comes down to two steps: knowing what questions to answer, and then answering them as simply as possible. By spending most of your time asking and considering questions, rather than tweaking and patching a large network of too-much data with too-many tools, your team will be able to understand the data they need today while slowly growing a more robust marketing technology system into the future.
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About the AuthorMore Content by Kyle Harper