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How to Create Dashboard Frameworks That Support Marketing Analysis

5 minute read
Pierre DeBois avatar
The dynamism of marketing data demands an analytics dashboard that is flexible and robust enough to change with the times.

In the article 5 Signs Your Dashboard Needs An Update, I discussed how a dashboard becomes outdated. I raised the questions that a dashboard should generate among a team, such as "Is the comparison relevant?” "Are our ideas still being sparked?" and “Are there technical changes?" 

All of this implies that an updated dashboard should be more flexible to be beneficial. With many teams still working from home, marketers may face a justifiable opportunity to introduce a dashboard framework — an arrangement of measurement solutions that cover two key facets: to maintain data and visualization quality, and to highlight potential consequential decisions.

If you are a marketer deciding on a framework for a dashboard, you have a wide variety of solutions. But keeping the following facets in mind can help you simplify your choices to achieve the right arrangement.

Know What Dashboard Inputs Can Be Managed Quickly and Easily

Similar to what I mentioned about APIs in my post on martech stacks, marketers must assess their planned data to select a solution: does the dashboard solution provide the right level of filters against the medium and metrics the team is interested in? For example, a connection of Google Ads to Google Analytics can work well if a marketing team just needs to link digital campaigns from online searches to online activity monitored through Google Analytics. 

One factor in your decision here is to assess if a Tableau Dashboard or Google Analytics Dashboard will easily provide the desired dimensions and metrics. Many dashboards and frameworks have third-party plugins that enhance functionality, leaving you with better context across dimension sources at a lower expense and learning curve for analysts and managers. Straightforward metrics such as click through rate (CTR), campaign costs (CPC, CPA) are usually a breeze to establish from the dashboard UI, while complex marketing metrics, like churn rate, customer lifetime value (CLV), and monthly recurring revenue (MRR), usually end up being a deciding factor. Their calculation must be easily verified, creating the right visualization to highlight data performance and what risk may be emerging.

Developing dashboards from programming languages like R and Python has become increasingly easier, enough where a marketer with light web developer knowledge and skills can consider creating a notebook or visualization documents with real-time data via an API. The programing nature of R and Python introduces more customization choices not possible from an out-of-box solution. Ongoing updates of data for complex metrics benefit, as well as advanced analytics models like regressions.

Related Article: How to Make the Most of Self-Service Dashboards

See How The Dashboard Summarizes Narratives From Data

No matter which platform you choose, your dashboard should be able to minimize details to maintain a simple  view of dimensions and metrics. They should be easy to label accurately, while appropriate charts and graphs can be selected to keep visualizations clear.

The result of the graphics should indicate the nuance of a data change.

What does it mean when ads spend goes up and converts? Context gives an answer. Good visualizations should lead your stakeholders to think of what is supporting their conclusion from the dimensions and metrics. The dashboards must reflect color choices and a core set of charts that tell a story about the metrics and dimensions that matter to your business.

Learning Opportunities

The visualizations should also trigger what needs to be automated, in terms of refreshing data and when users should receive analysis. Automation avoids report bloat — adding material and visualization that won't be used. The dashboard should also offer the ability to review and adjust any automated steps.

All of this leads to imagining how a reoccurring analysis will look to a team and their mission, with the end result ideally being a simple, concise report for end users.

Related Article: B2B Marketing Analytics Is Broken. Here's How to Fix It

Create an Accountability Report for Your Supporting Actions

The last deciding piece of the dashboard selection puzzle involves questions around reporting structure — who will follow up on reoccurring insights? Marketers can create an accountability table to plan the follow up. A table should contain the following categories:

  • Action: The issue or action that results from the reported metrics.
  • Owner: The person or department who will champion the action and see a resolution.
  • Status: A brief description of progress from the action. The status should highlight just enough to help the team stay on the same page for a process issue or to understand what programmatic bugs related to the data are occurring.
  • Next Report Date: A follow up date to hear status updates or when an action is expected to be closed.

Creating an action framework for the dashboard should help guide the questions you and your team ask. For ideas on maintaining a dialogue centered on a dashboard and accountability table, check out my post on tips for better communication for analytics teams and on how to lead a remote analytics team.

Overall, dashboards are no longer a static item on a checklist. The dynamism of data has transformed them into a more programmatic framework. A good choice will do more than present dimensions and metrics. It will focus on indicators that spark good decisions from the team.

About the author

Pierre DeBois

Pierre DeBois is the founder of Zimana, a small business digital analytics consultancy. He reviews data from web analytics and social media dashboard solutions, then provides recommendations and web development action that improves marketing strategy and business profitability.

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