How to Build an E-commerce KPI Dashboard

If you’re in the e-commerce business, you’re aware of how important data is. However, you’ll be just as aware of the sheer amount of data, and have some trouble finding out what is relevant and what isn’t. To get a grasp, it’s important to define the most important KPIs for your e-commerce business, and implement them in a way that is clear and concise. E-commerce dashboards are the perfect way to achieve that.

But why would you use e-commerce dashboards? The short answer is that insightful dashboards will help e-commerce businesses optimize their revenue.

Benefits of an e-commerce KPI dashboard

Discover strengths and weaknesses

Using a data-driven approach will reveal what you’re excelling at and which specific things might be holding you back. This might confirm things you already thought, or it could also help you find out things that might not be obvious with other approaches.

Optimize your content or ads

Having well-defined KPIs helps you to improve the content you produce or the advertisements you publish to guarantee a maximum return on your efforts. Understanding the inbound traffic they generate is vitally important for any e-commerce business. A KPI dashboard is one of the tools that can help with that.

How to gather e-commerce data

As with any visualization project, you’ll first need to gather your e-commerce data. Your data can be spread across different platforms such as Google Analytics for website statistics, or e-commerce platforms such as Shopify, Magento or WooCommerce. Or maybe you already store some of your e-commerce data in a database. Thankfully, there are numerous tools available to help you streamline this process, including Shopify blog apps.

If you’re gathering data from multiple sources for reporting purposes, a data warehouse can be a good solution. You could look into data warehouses that integrate with your specific e-commerce stack, like a data warehouse for Shopify.

Once you have a clear idea of your data stack, you can start thinking of the exact e-commerce metrics you would like to measure and track over time. Also you can use the best email marketing platform for ecommerce to track your metrics and manage your campaign accordingly.

Common e-commerce KPIs

As it became clear in the introduction, ecommerce KPIs are useful to optimize your webshop, but which are the metrics you should be using? That depends on the specifics of your business of course, but here are a couple to get you started

1. Bounce rate

The bounce rate is a very important metric for any website; it means how many of the users on your site visit just one page and then leave. If you keep an eye on the bounce rate of the different pages on your e-commerce site you can use that information to keep users on your site for longer – which translates to more sales. However, analyzing bounce rate data and implementing effective strategies to improve it can be complex. Consider partnering with an e-commerce agency like Swish DM.

2. Incoming traffic sources

The visitors of your site can come from many possible websites and portals, and understanding how they find you is important. A low share from search engines might mean your SEO isn’t up to snuff. A high share of a certain website might show you an audience you previously weren’t aware of. Information like this can massively help you optimise your webshop, and e-commerce dashboards can help you to understand it. It may be worth going after long-tail keywords instead of primary keywords. For example, going after "home phone service in Georgia" will be much easier to rank for in comparison to "home phone service.

3. Average time on page

The average time on page is a KPI that helps you understand which pages that are more engaging to visitors. Using this information in your e-commerce dashboards can help you optimise your website – and get more sales.

4. Pages per session

This KPI can tell you a lot about how well you keep visitors on your site. A low number of pages per session can mean you’re missing out on cross-selling opportunities or that the way your site is structured doesn’t entice people to visit another page.

5. Cart abandonment rate

Cart abandonment rate is the percentage of people who add an item to their cart who don’t end up buying it. Because your revenue comes from people purchasing things from your platform it is important to minimise this as much as possible. A KPI dashboard will help you keep an eye on this and enables you to optimise your site for a lower cart abandonment rate. To minimize the cart abandoment rate, consider using a hosted shopping cart that offers simplified checkout processes, guest checkout options, saved carts, and abandoned cart recovery mechanisms – especially helpful for solo creators, like selling Notion templates or digital downloads.

6. Average value per order

The average value per order is something to keep an eye on – a slight dip can cause a significant drop in revenue. On the other hand, a small increase could mean a lot more income and profit. If you notice a slump in the average value per order that might mean cross-selling or up-selling should be higher on your priority list.

9 signals your data analytics needs an UX overhaul

There’s no shortage of analytics dashboards in business. But just because you have analytics, doesn’t mean people are using them—or getting value. Too often, organizations invest in powerful BI tools, only to see logins drop and teams go back to spreadsheets or “gut instinct.” The culprit? Bad UX: confusing flows, cluttered screens, or tools that simply weren’t built for the way real people ask questions and make decisions.

How do you know if your analytics is helping, or if it’s time for a rethink? Watch for these nine red flags.

1. Users avoid the analytics tool or only use it to export raw data

You invested in a powerful dashboard, but the usage logs tell a different story: almost everyone is exporting CSVs or copying numbers into Excel. Sound familiar? This is one of the loudest signals that your analytics UX isn’t doing its job.

Why this matters:
If users prefer spreadsheets, it’s rarely about “habit.” It means your dashboards are too rigid, too hard to filter, or not delivering insights in a usable format. When analytics is just a jumping-off point for manual work, you lose out on automation, data integrity, and the collaboration benefits dashboards should provide.
Worse, critical decisions get made outside your platform, which means you have no visibility—and mistakes multiply as data is transformed, re-saved, or misunderstood.

What to do:

• Interview your users to find out why they’re exporting. Is it a missing feature, a trust issue, or confusing navigation?
  
  
• Redesign dashboards to surface the exact metrics users need most often, with filters and views tailored to common workflows.
  
  
• Consider building or enabling more “actionable” elements—such as exportable charts, shareable links, and built-in annotation tools—to make the platform stickier and streamline collaboration.
  
  

2. Your support queue is full of “Where is…?” and “How do I…?” questions

Support tickets and onboarding sessions keep getting stuck on the same issues: users can’t find key metrics, don’t know how to set filters, or keep missing important features. Instead of discovering insights, your users are trapped in a scavenger hunt.

Why this matters:
Analytics tools should empower, not frustrate. When basic navigation is confusing, users either give up or bombard your help desk with repeat questions. This slows adoption, drains support resources, and fosters a reputation that your analytics tool is “hard to use”—which spreads fast by word of mouth.

What to do:

• Audit your help desk logs and onboarding scripts. List the top recurring “how do I…” or “where do I find…” questions.
  
  
• Map user journeys for key roles, and run usability tests with non-expert users to observe where they get lost.
  
  
• Streamline the UI: rename ambiguous labels, reduce menu clutter, and add clear, step-by-step guides or tooltips for essential actions.
  
  
• Consider a “first-time user” guided walkthrough and a persistent help button or chatbot in-app for on-demand support.
  
  

3. It takes too many clicks (or endless scrolling) to answer basic questions

Imagine a sales manager who needs to check monthly pipeline trends. If it takes five clicks, three nested menus, and lots of scrolling just to find the data, they’ll likely give up. The longer it takes users to get what they want, the less likely they are to stick with your platform.

Why this matters:
Too many steps turn every data lookup into a chore. It also increases the risk of user error—clicking the wrong filter, missing a submenu, or landing on the wrong date range. Busy people need “at-a-glance” answers, not digital obstacle courses.

What to do:

• Study heatmaps or analytics to see common paths—and where users drop off or backtrack.
  
  
• Redesign your dashboards to bring “north star” metrics, key filters, and most-used actions up front.
  
  
• Collapse rarely used sections, provide one-click shortcuts, and use smart defaults so the most common questions are answered in a single screen.
  
  
• Regularly survey users to discover which metrics and workflows matter most, then optimize for those.
  
  

4. Your dashboards are pretty, but nobody trusts (or understands) the numbers

A slick UI won’t fix trust issues. If users don’t understand where the numbers come from, what they mean, or how a metric is calculated, your dashboard becomes an expensive screensaver. When there’s confusion, users revert to their own calculations—or worse, ignore the tool completely.

Why this matters:
Unclear or unexplained metrics breed suspicion. Users may interpret numbers incorrectly, or stop using analytics altogether. Even worse, if teams are pulling reports with conflicting definitions, there’s no single source of truth—undermining business alignment.

What to do:

• Add info icons, tooltips, or hover text to every metric, explaining its meaning, source, and calculation method in plain language.
  
  
• Make sure every chart or table has a clear label and time range.
  
  
• Consider a “definitions” sidebar or glossary, and display data lineage or “last updated” info where possible.
  
  
• Encourage users to submit questions directly from dashboards (“Not sure what this means? Click to ask.”) and update documentation based on real confusion.
  
  

5. Everyone gets the same dashboards, regardless of role or goals

If your analytics treats a CFO the same as a customer support agent, it’s missing the mark. Role-based dashboards are a must for modern tools; otherwise, users are forced to sift through irrelevant data or miss out on metrics that matter for their daily work. Even insights from an exit survey can point to frustration with dashboards that aren’t tailored to user needs.

Why this matters:
Irrelevant dashboards lead to cognitive overload—users get lost in data that doesn’t help them, or they ignore analytics completely. Personalized, role-based views make analytics feel like a helpful assistant, not a generic database.

What to do:

• Develop different dashboard templates for different user personas—sales, marketing, support, execs, etc.
  
  
• Allow users to customize their own dashboards: pin favorite reports, hide unused sections, or subscribe to personalized alerts.
  
  
• Implement user permissions so sensitive data is only visible to those who need it.
  
  

6. Mobile or accessibility complaints are stacking up

If users report that your dashboards are hard to use on mobile, or people with visual or motor impairments can’t navigate your tool, you’re leaving both value and revenue on the table.

Why this matters:
Analytics decisions are increasingly made on the go. If your dashboards break on smartphones or tablets, busy users will skip them entirely. Even worse, lack of accessibility isn’t just a usability flaw—it can become a legal risk and signals you’re not committed to serving your whole audience.

What to do:

• Invest in responsive design that adapts layouts, font sizes, and navigation for mobile.
  
  
• Audit your analytics for accessibility: check color contrast, enable keyboard navigation, and add ARIA labels for screen readers.
  
  
• Test with users who have accessibility needs—not just automated checkers.
  
  
• Provide alternate ways to receive insights (e.g., email summaries, scheduled reports).

Additionally, consider leveraging front end development services to ensure that your dashboards are both user-friendly and accessible across all devices.

7. Onboarding is a slog—and only power users thrive

If it takes hours (or days) of training to get new users up to speed, or if only data analysts are getting real value, your analytics experience is too complex for the majority of your audience.

Why this matters:
High learning curves kill adoption. Users will resist using a tool they find intimidating or overwhelming, especially if their day job isn’t “analyst.” This shrinks the return on your BI investment and breeds resentment.

What to do:

• Build interactive, bite-sized onboarding flows tailored to each role or department.
  
  
• Add in-app walkthroughs, sample data, and “try it yourself” sandbox modes so users can learn by doing.
  
  
• Offer “easy” and “advanced” modes—let new users start simple and unlock more power as they grow comfortable.
  
  

8. Insights don’t drive action—or usage drops sharply after onboarding

You rolled out analytics, ran everyone through training, and… engagement flatlined. Dashboards are rarely referenced in meetings, and no one acts on trends or outliers surfaced by the tool.

Why this matters:
Analytics only adds value if it inspires action. If usage drops after onboarding, your UX might be burying the “so what?” or failing to surface critical insights at the right time and place.

What to do:

• Highlight actionable items directly in dashboards: flagging anomalies, sending alerts for threshold breaches, or suggesting next steps (“Sales dipped—view top loss reasons”).
  
  
• Make sharing and annotating dashboards frictionless—one click to send, comment, or create a task.
  
  
• Set up recurring digests, summaries, or alerts that bring analytics into users’ workflows, not just as a static destination.
  
  

9. The most common feedback: “It looks outdated,” “It’s busy,” or “I always feel lost”

When users describe your analytics tool as “cluttered,” “outdated,” or “overwhelming,” it’s not just about style. Visual overload—too many charts, dense tables, inconsistent colors—makes it hard to focus, erodes trust, and leads users to disengage.
Worse, if users never know where they are or what to do next, they’ll eventually stop logging in at all.

Why this matters:
Modern, clean design isn’t about aesthetics alone—it directly influences trust, usability, and adoption. A confusing, unattractive dashboard makes it harder for people to believe the data (and share it with stakeholders).

What to do:

• Audit your layouts for visual hierarchy: what’s most important should be big, bold, and front-and-center.
  
  
• Remove or consolidate rarely used widgets. Use whitespace, consistent colors, and typography.
  
  
• Regularly user-test prototypes with fresh eyes—ideally from outside your team—to catch design drift and fatigue.
  
  
• Refresh the look and feel every few years to keep up with best practices and user expectations.
  
  

Final thoughts: A better UX means more adoption, trust, and ROI

If you spotted yourself in a few of these red flags, you’re not alone. Most analytics projects hit a wall—not from lack of data, but from ignoring the human side of dashboards. The good news? Every UX fix brings you closer to analytics that actually delivers business value.

Start by talking to your users, watching them work, and treating UX not as “polish,” but as a core requirement. Because data only changes organizations when people can actually use it.

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Feel inspired, but don’t know where to start? Check out our sample app and look for the e-commerce demo dashboards. Do you just want to jump in head first? Start a free trial of Luzmo and create your first dashboard today!