Three guiding principles for building better marketing analytics dashboards

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Modern marketing campaigns are constantly churning out data that can allow companies to continually improve customer engagement. But for that data to be useful, it has to be organized and presented in a way that key stakeholders can understand and act upon.

Enter the marketing analytics dashboard. These visual data representations provide a single, unambiguous source of truth about campaign performance. They allow stakeholders without a background in data science to easily see patterns, trends, and outliers, and to garner insights that enable rapid optimization and better decision-making on future campaigns directed at individuals.

What you see is what you get

The best dashboards use a variety of dynamic visual aids like charts, graphs, and dot plots, pulling data from multiple disparate analytics tools in real-time. They incorporate sensible design and an intuitive UX to illustrate performance with respect to key performance indicators like revenue, engagement and conversion rates, source tracking, attribution modeling, and other significant metrics.

Whether you’re leading an agency’s creative team or in-house brand marketing department, you can significantly enhance your team’s effectiveness and value to your organization by harnessing the power of visual analytics dashboards. If you want to start developing better dashboards, for your own use or for client reporting, remember the following principles:

#1: Start with the end in mind

Your dashboard will do more than just visually display performance metrics; it should tell a story and answer important business questions. Decide on the individual story you want to tell and the questions that need answering prior to beginning design work. Choose KPIs that are directly linked to overall campaign or business objectives and that can be easily monitored based on the analytics tools at your disposal. The data you display should be actionable and demonstrate how specific variable changes can affect overall performance.

It’s also important to be realistic about your data collection and analytics capabilities. You don’t want to be making important decisions using data that isn’t trustworthy. Dashboards can be tailored to reveal micro-level insights by audience segment or cohort, channel, placement, line of business, tactic, sub-tactic, and many other data “slices,” but those insights will only be as good as the integrity of the data that goes into them.

#2: Measure what matters

Once you realize how much data a single digital campaign can generate, you might want to include all of it in your reporting. But a dashboard isn’t a visual data dump. Prioritize macro- and micro-level perspectives that lead to discovery rather than information overload. In other words, once you’ve set your KPIs, use your dashboard to provide context. Don’t waste time consolidating data that isn’t directly linked to those KPIs or building visualizations that you can’t act on.

For example, scatterplots can compare click-through rates and click-to-open rates of an email campaign, revealing the effectiveness of certain subject lines and content that individuals consume. This information is useful only if you have the resources to make in-flight optimizations. When you’re thinking about the metrics you’ll include in your dashboard, make sure you account for the time and personnel resources needed to act on the insights you’ll discover. Before building your dashboard, you’ll need to determine roles related to data collection, transformation, dashboard development, and user administration, and you’ll want to select the dashboard platform that best suits the support resources you have in place.

#3: Focus on individuals — the end-user

When it’s time to start development, keep the perspective of the individual front and center, because the user experience is everything. Your dashboard should be seamless and intuitive and should incorporate a navigation framework similar to what users might encounter when surfing the web or using common software tools. It should also account for design best practices. For instance, colors should be used consistently across the interface. If the conversion rate metric is a blue line on one visualization, then it shouldn’t be a red bar on another.

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