How to Create Effective Marketing Dashboards

I always recommend a simple playbook for building and refining dashboards: 1. What's happening 2. Why is it happening 3. How do we fix it 4. Fix the problem, a bunch of times 5. Automate the problem away 6. Delete the dashboard Why not just skip to step 5 (automating stuff)? Because you really need to get step 1 correct, otherwise steps 2-5 will be a waste. Let's drill down into each step: ___ 1. What's happening Most dashboards start with a bar chart and a table. A bar chart of a key metric over time, and a table below it showing the raw data (to double click into stuff). The goal of this step is to identify either an output metric (like revenue, sign ups, etc) or an input metric (emails sent, candidates reached out to) and watch it move. 2. Why is it happening Don't skip to step 2 too early. First make sure that looking at step 1 (what's happening, is actually worth double clicking into). For some KPIs, all you need is a bar chart and a table. But when you need to understand the why, I tend to start with a drill down into a row of the table from row 1. This could be a single customer view, an employee view, etc. Get more details (tables, charts, summaries) and make them available to users to try and figure out why things are happening. Don't try and skip to a solution. Just throw a bunch of raw data into one place. 3. How do we fix it Over time, you'll add data points in step 2 and remove them. The layout of the dashboard will change and evolve. This is because you're iterating towards a clear path to fix the problem in a repeatable way. The goal of this step is to find a model that flows naturally and works in a repeatable way to fix the problem. 4. Fix the problem, a bunch of times Now that you have a working approach, start using the dashboard to solve the problem. Use it again. And again. Make tweaks any time the solution is not perfect. Add toggles, optimize the layout etc. Make sure it flows, and works for edge cases. 5. Automate the problem away Now you know you're solving a real problem, you found the main data points to identify and address the issue, you've created a step-by-step workflow to resolve the issue, and you've battle tested the solution. At this point, start figuring out if there's a way to automate the solution. It might involve engineering effort. It might involve an automation tool or RPA solution. But just imagine. Once you automate the solution, you can finally... 6. Delete the dashboard This is always the best part :) If you find a solution to the problem, it's time to move onto the next problem. ___ Everything in the business doesn't warrant all of these steps. I've built dashboards that get to step 1, we build a bar chart and a table, use it to measure progress for a few months, and delete the whole thing when our priorities change. Priorities always change. Make sure you're only going deep on the problems that are absolutely critical to your business RIGHT NOW.

Dashboards should be designed for action, not data. Most dashboards contain plenty of data. Dozens of metrics and pretty charts. We've been taught that data drives action, but in practice, it rarely does. As you build your dashboards & and reports, consider the question: What is the user's "next best action"? Then, build solutions to prompt (or enable) that action. Some examples of "next best action": 1.) More Data Sometimes, the user will have more questions. That's ok! We build in self-service filters, segments, and drill-downs to dive in deeper. Self-service > fewer questions for the data team > faster time to action. 2.) Related Data Most businesses will have dozens of reports, often fragmented and disjointed. We can build links to bridge between the reports. Additionally, those links can be dynamic to carry through important filters (date ranges, segments applied) and help users keep their contextual flow. Less time hunting for reports > faster action. 3.) Sharing the data Once users find interesting data, they want to save it or send it to a coworker or client. Enable sharing via email, slack, raw export, etc. Sharing > More distribution > more action. 4.) Actions in another platform (Shopify, Meta, Salesforce, etc) Based on the data, users will need to make a change in another tool. Take someone in merchandising. They see product reports showing that certain products have low conversion rates, likely due to dwindling inventory levels. We can build a link in the dashboard that takes them DIRECTLY to the Shopify admin portal to the product setup and re-merchandise their collection. With one click, they've gone from data > to action. Fewer clicks > faster action. 5.) Alerts Users may see a number and wish they knew about it sooner. For this we setup alerts (email, slack, sms, webhook, etc.) Faster alerts > faster action. Our goal is to transform data-heavy dashboards into tools for action. Consider: - Can we make them more self-service? - Can users set up alerts? - Can they export and share the data easily? - Can we link tools and reports together to avoid context switching? - Can we automate the data to drive action? Are there any tricks you're using to make your dashboards more actionable? #businessintelligence #looker #ecommerceanalytics #measure

✈️ Most dashboards are designed like airplane cockpits…when what you really need is a Control Tower. Too many BI dashboards try to show everything at once: KPIs, segments, raw data — all mashed together. It overwhelms users and kills decision speed. Instead, think about your dashboards as a Control Tower. The top of the tower offers a clear, panoramic view. You’re scanning for major movements and disruptions. When needed, you can zoom in with instrumentation or speak directly to pilots, but that's not your default. By managing your information hierarchy in layers, you can start simple and progressively reveal complexity. Here’s how it works: 📊 L1: The Tower View – high-level KPIs, trends, and alerts. What’s happening? 🔍 L2: Segment View – explore segments and categories. Where is it happening? 🧾 L3: Transaction View – detailed records and raw data. Why is it happening? Each level is built for a specific cognitive mode. Mixing them forces your brain to multitask and that’s where insight gets lost. 🧠 Rule of thumb: Dashboards should optimize for low cognitive load at entry. Users should never have to reconcile different zoom levels simultaneously. Control Tower dashboards allow users to scan, zoom, and act without overwhelming them. By designing dashboards to reflect human cognitive modes and information hierarchy, you create tools that are not just insightful but usable. #dataviz #dashboards #BI #uxdesign #analytics #productivity