custom-metrics

Custom Metrics for Dashboard & Reporting on the Platform

Charles Higgins

Charles Higgins

— 10 min read
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In the original video produced by our tutorial channel, we walked through how to build, update, and display custom metrics on the platform’s dashboard and reports. Our goal was simple: show how to turn raw data into meaningful, actionable insights that your team can use every day. We touched on a few quick clicks—what we called “First Click” and dashboard setup—then showed how to update metric definitions and confirm counts in reports. Throughout this article we expand those steps into a complete, practical guide so you can implement custom metrics reliably and confidently.

Table of Contents

Why custom metrics matter for growing teams

Dashboards are only useful if they show the right numbers. Too often dashboards present generic, noisy data that doesn’t answer the real questions a team has. Custom metrics let us tailor measurement to the outcomes that matter: lead flow, appointment booking rate, revenue by campaign, support ticket resolution time, and more. When we control what’s measured and how it’s displayed, the dashboard becomes a strategic tool instead of a distracting information dump.

  • Focus on outcome, not activity: Custom metrics let us measure conversions and outcomes (e.g., booked appointments, paid invoices) rather than raw activity counts (e.g., emails sent).
  • One source of truth: By standardizing how metrics are calculated, teams avoid arguing over different numbers and can make decisions faster.
  • Time savings: With the right metrics on the dashboard, we spend less time digging for data and more time acting on it.
  • Accountability and clarity: Clear metrics make goals measurable and progress visible across teams.

Overview: What we mean by custom metrics

When we say "custom metric," we mean a definition we create that transforms data into a KPI tailored to our business. Instead of relying only on the platform’s built-in reports, we build a metric that:

  • Has a clear name and purpose (e.g., "Weekly Booked Appointments").
  • Uses a specific data source (contact events, invoices, pipeline stages, form submissions).
  • Includes filters and rules (date range, campaign, tag, team member).
  • Chooses an aggregation (count, sum, average, percentage).

Once defined, a custom metric can be added to a dashboard widget, included in recurring reports, and compared across time periods or segments.

Step-by-step: Create and add custom metrics to the dashboard

We’ll break this into a repeatable process. The steps below reflect the practical clicks and decisions we demonstrated in the video—what we referred to as “First Click und Dashboard”—and expand them with context so teams can replicate the flow.

1. Decide what to measure

Start with the business question. Some examples:

  • How many qualified leads did we get this week?
  • What is the conversion rate from form submission to booked appointment?
  • How much revenue closed did our campaign generate this month?

Write the metric in one sentence: "Number of booked appointments in the last 7 days, for Campaign X." This sentence will drive the data source and filters you choose next.

2. Open the dashboard builder (our "First Click")

From the main menu, we click into the Dashboard area—our "First Click" into where visuals live. In the dashboard builder we can add new widgets or edit existing ones. For a new custom metric widget, we typically choose a single KPI widget, a trend chart, or a table depending on the use case.

3. Create the metric definition

Inside the widget configuration we define the metric:

  • Name: Clear, short, and consistent (e.g., "Booked Appts — Sales Team").
  • Data source: Choose where the data comes from (appointments, form submissions, invoices, leads, pipeline stages).
  • Aggregation type: Count, sum, average, or percentage. For example, count = number of appointments; sum = total revenue; percentage = conversion rate calculated from two events.
  • Filters: Apply date range, campaign, team member, location, tag, or stage. Filters ensure the metric measures the exact cohort we care about.

We showed a quick example in the video where we clicked "update" after adjusting a filter. That "click on update" step is critical: every change needs to be saved to refresh the widget’s number on the dashboard.

4. Confirm counts and sanity-check data

After saving the metric, the dashboard should show a count or value. We recommended checking the number in a raw report (or by cross-referencing a table view) to ensure the aggregation and filters match expectations. If the number looks off, re-open the metric configuration, verify the filters, and click "update" again to refresh.

5. Place the widget on the dashboard and configure visuals

Choose how you want the metric presented:

  • Single KPI: Great for top-line numbers like active pipeline value or appointments today.
  • Trend chart: Best for showing movement over time (daily, weekly, monthly).
  • Table or breakdown: Useful to compare segments like campaigns or team members.

Set labels, comparison periods (e.g., vs prior week), and thresholds (colors for good/ok/bad). Then click to save the widget. In the video we walked viewers through a simple "update" click to apply the visual changes and confirm the metric displays as expected.

Configuring reports and exporting insights

Dashboards let us see snapshot views. Reports let us package and deliver those snapshots to stakeholders. We talked briefly about reports "in the particular form"—here’s how to design reports that match your audience and goals.

Designing useful reports

Think of a report as a story with three parts: context (what period and audience), evidence (the data and charts), and conclusion (what action we recommend). Practical steps:

  1. Select metrics: Choose 3–6 KPIs that support the report’s purpose. Too many metrics dilute attention.
  2. Use supporting charts: Add trend lines, breakdown tables, and conversion funnels to explain changes.
  3. Add notes or annotations: When an unexpected spike appears, add a short note to explain why (campaign launch, system outage, etc.).
  4. Choose distribution method: Schedule a PDF summary to be emailed weekly, or allow team members to view the dashboard live depending on permissions.

Scheduling and automation

Set recurring reports to be generated and shared automatically. For example, a weekly summary every Monday morning helps the team start the week aligned. When scheduling, confirm:

  • Recipients and permissions (who should receive the report).
  • Format (PDF snapshot, CSV export, or in-platform view).
  • Timezone and frequency (weekly, monthly, daily).

Automated reports reduce manual work and keep everyone informed without extra meetings.

Best practices for naming, organization, and governance

Custom metrics are powerful, but without governance they become messy. We follow a straightforward set of conventions to keep dashboards tidy and trustworthy.

  • Consistent naming: Use prefixes like "KPI —" for top-level metrics or "Campaign —" for campaign-specific metrics. This makes it easy to find metrics in the selector.
  • Description field: Always add a short description explaining the data source and filters (e.g., "Count of appointments booked where source=organic and status=confirmed").
  • Owner and last updated: Assign an owner who is responsible for the metric’s accuracy and note when it was last modified.
  • Version control: If you change the definition of a metric, keep the old definition as "archived" and create a new metric rather than silently changing a live KPI.
  • Limit top-line widgets: Keep dashboards focused—4–8 key widgets per dashboard is a practical guideline for clarity.

Common pitfalls and how to avoid them

We regularly see a few recurring issues when teams start using custom metrics. Here’s how we troubleshoot and prevent them.

1. Incorrect counts due to wrong filters

Problem: The dashboard number doesn’t match manual counts.

Fix: Open the metric editor, review all filters (including hidden or default filters), and run the raw query or table for the same date range. Often a default filter like "status != archived" or a mismatched timezone explains discrepancies. Click "update" after correcting filters, then re-check the number.

2. Delayed data or synchronization lag

Problem: Recent transactions don’t appear immediately.

Fix: Understand the platform’s data refresh intervals. For most systems, some events update in real-time while others batch overnight. If real-time numbers are required, design metrics around real-time event sources (like direct appointments or webhooks) and note any expected latency to stakeholders.

3. Overly complex metric definitions

Problem: A metric tries to do too much (e.g., combine five conditions and two sources), which makes it hard to validate.

Fix: Break complex metrics into smaller, testable components. Create separate metrics for each condition and a final metric that references or combines them. This modular approach makes validation easier and reduces accidental errors.

4. Visual clutter and cognitive overload

Problem: Dashboards have too many widgets and stakeholders ignore them.

Fix: Create role-based dashboards—one for executives (top KPIs), one for marketing (campaign performance), one for sales (pipeline). Hide advanced or technical widgets in a separate admin dashboard.

Practical examples and templates we use

Below are concrete metric definitions and dashboard widget templates we use across teams. You can copy these ideas and adapt them to your processes.

Sales/Revenue

  • Closed Revenue — Monthly: Data source = invoices, Aggregation = sum, Filter = status=paid, Date range = monthly, Visual = KPI with trend vs prior month.
  • Opportunities in Pipeline — Active: Data source = pipeline stages, Aggregation = count, Filter = stage != won/lost, Visual = table grouped by stage.
  • Avg Deal Size — Quarter: Data source = closed deals, Aggregation = average of invoice totals, Filter = closed date in quarter, Visual = KPI.

Marketing

  • Leads from Campaign X — Weekly: Data source = form submissions, Aggregation = count, Filter = campaign = X, Date range = last 7 days, Visual = trend chart.
  • Landing Page Conversion Rate: Data source = visits and form submissions, Aggregation = percentage (forms/visits), Filter = landing page URL, Visual = KPI with funnel visualization.

Customer Support

  • Tickets Resolved within SLA — 30 Days: Data source = tickets, Aggregation = percentage, Filter = resolved time <= SLA, Date range = last 30 days, Visual = KPI.
  • Average First Response Time: Data source = ticket events, Aggregation = average wait time, Visual = KPI and trend.

How custom metrics save time and reduce tech headaches

We hear this from teams all the time: they want fewer tools, less context switching, and dashboards that tell them what to do next. Well-configured custom metrics deliver that. Here’s how:

  • Less manual reporting: Automated dashboards and scheduled reports remove the need for weekly exports and manual consolidation.
  • Faster decisions: With agreed-upon KPIs, meetings focus on action rather than number reconciliation.
  • Reduced tech overhead: When metrics are defined clearly and centrally, engineers and analysts spend less time re-creating the same reports.
  • Clarity for new team members: Well-documented metrics speed up onboarding—new hires understand what matters and how it’s measured.

We emphasize transparency and simplicity: no hidden fees, no mysterious charges for reporting features. When teams trust that the dashboard reflects reality and that reports are delivered on time, they can focus on growth instead of firefighting data problems.

Troubleshooting checklist before escalating

Before reaching out for support, run through this quick checklist. It often saves time for both you and the support team.

  1. Confirm the metric’s date range and timezone match your manual check.
  2. Check for additional filters or default settings that might exclude data (e.g., archived records or test entries).
  3. Validate the underlying data source directly (open the raw table or event feed) to confirm the records exist.
  4. Verify whether the platform uses batch processing for certain events and if a refresh or re-sync is needed.
  5. If a number still seems wrong, duplicate the metric with fewer filters to isolate where the discrepancy appears.

Testimonials from teams who implemented our approach

"After consolidating our KPIs into a single dashboard and applying the naming conventions you recommended, weekly meetings are half the length and twice as productive." — Marketing Lead
"The step-by-step checklists made it easy to validate numbers. We now trust reports across the company and have eliminated duplicate spreadsheets." — Operations Manager
"Scheduling automated weekly reports saved our small team hours each week and kept clients informed without manual effort." — Agency Owner

FAQ

Q: How do we pick which metrics belong on the main dashboard?

A: Start with your top goals. The main dashboard should answer: "Are we on track to meet our primary objectives this period?" Choose 3–8 metrics that reflect those objectives—revenue, conversion rate, pipeline value, and a performance metric for a strategic campaign are typical choices.

Q: How often should metrics be updated or reviewed?

A: Review metric definitions quarterly or whenever a process changes (new funnel, pricing, or product offering). For dashboard visuals, update frequency depends on stakeholder needs—executive dashboards might refresh daily, while operational dashboards could be near real-time.

Q: What do we do if stakeholders disagree on a metric’s definition?

A: Use documentation and a simple governance process. Capture the proposed definition, data source, filters, and rationale. Bring stakeholders together to agree on a single, documented definition. If a different view is needed, create a separate metric rather than changing the agreed KPI.

Q: Can we combine metrics from different sources?

A: Yes, but proceed carefully. Combining sources requires consistent keys (like contact IDs) and clear rules for deduplication. Test combined metrics thoroughly with sample data and add a description explaining how data from each source is matched.

Q: What visualizations are best for which metrics?

A: Use single KPIs for top-line numbers, trend charts for change over time, tables for detailed breakdowns, and funnel charts for conversion steps. Always match the visualization to the question stakeholders are asking.

Q: How do we ensure data privacy and access control?

A: Use role-based access for dashboards and reports. Limit sensitive metrics to authorized users and avoid including personal data unless necessary. Anonymize or aggregate where possible to protect privacy while still delivering insights.

Final checklist before you go live

  • Metric name and description added for every custom metric.
  • Owner and last-updated notes included.
  • Filters verified and documented.
  • Widget visuals configured and tested for different screen sizes.
  • Scheduled reports set up with recipients and formats confirmed.
  • Governance process agreed for changes and versioning.

Conclusion

Custom metrics transform dashboards from static displays into decision-making tools. By following the steps we showed in the video—starting with a clear question, doing the "First Click" into the dashboard, carefully defining and updating metrics, and validating counts in reports—we can create reliable, actionable KPIs that save time and reduce friction across teams.

We encourage you to start small: pick one business question, build a single metric around it, and place it on a dashboard. Confirm the count, document the definition, and schedule a simple recurring report to share the insight. From there, expand the system gradually and use the governance practices above to keep everything tidy and trustworthy.

If you’d like help designing your first set of metrics or reviewing an existing dashboard, we can walk through your setup together and provide practical recommendations tailored to your processes. Our approach focuses on simplicity, transparency, and real outcomes—so teams spend less time wrestling with tools and more time moving the business forward.

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