Tutorial #5 – How to Add Scorecards and KPIs to Your Looker Studio Report

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Tutorial #5 – How to Add Scorecards and KPIs to Your Looker Studio Report

The fastest way to make any dashboard readable is to give people the key numbers first. In this article I walk you step by step through building a clean KPI strip at the top of a Looker Studio report: adding scorecards, choosing metrics, configuring comparisons and date ranges, and styling the tiles so they look consistent and useful.

If you prefer to follow along with the screencast I recorded, watch the full tutorial here:

Dashboard header and the blank section where KPIs will be added

Step 1: Add a section title and a subtitle for context

When I start a new page or section, I always add a title and a short subtitle before placing scorecards. If you drop six numbers into the canvas without context, people will pause and wonder what they are looking at. A simple title like KPI’s or Top-level Metrics plus a one-line subtitle such as “Website performance — last 28 days” helps the viewer immediately understand the scope.

To add a title I use a text box, center it, and set the font size to something readable (I often use 24px for section titles). Then I add a subtitle beneath it with a smaller font and a short sentence describing the section. Keep text concise. People rarely read long paragraphs on dashboards, and the header should do the explaining for you.

Adding text for section title and subtitle in the dashboard editor

Step 2: Insert your first scorecard into your Looker Studio report

Click Add a chart and choose a scorecard. There are several scorecard variants: the classic scorecard, the compact one, and scorecards with a dimension. Any of them will work — you can change the type later. I usually pick the basic scorecard for my main KPIs because it gives the biggest number and supports comparison and sparkline options.

At this stage you simply place the tile on the canvas. Don’t worry about final sizing yet. Focus instead on the metric you want to surface: active users, total sessions, revenue, conversions, average session duration — whatever answers the business’s core questions at a glance.

Scorecard configuration showing the fields panel in Looker Studio

Step 3: Choose metrics and understand fields vs metrics

Scorecards expect a metric as the primary field. In GA4 the typical metrics you will use are active users, sessions, conversions, purchase events and revenue. Click the metric picker and choose the metric you want. If your data source supports it (for example Google Sheets or BigQuery), you can also choose a different auto-aggregation such as max or min — GA4 does not expose those aggregations in the same way.

Two things I always check:

  • Is this metric an actual metric or an inappropriate dimension? Scorecards are meant for numbers, not strings.
  • Do I need a calculated field? If you need to filter or transform the metric (for example count only organic sessions), create a calculated field with case when logic or conditional formulas. That is a more advanced step and often deserves a separate tile.
Creating a calculated field example in the fields panel

Step 4: Optional metrics — when to give viewers control

Looker Studio lets you define optional metrics for a scorecard. If you turn this on, the viewer can switch the scorecard metric in view mode between the options you provide (for example Active users 1 day, Active users 28 days). Optional metrics add interactivity, and I use them in client dashboards where viewers want to toggle between close variations of the same KPI.

If you prefer to keep control over the presentation, disable optional metrics and only expose what you want people to see. For most reporting dashboards I remove optional metrics so the KPI strip remains consistent.

Scorecard with sparkline and optional metrics visible in view mode

Step 5: Add a sparkline and set date granularity

A sparkline is a compact mini-chart inside the scorecard that gives temporal context. I always enable it for KPIs that benefit from trend information, such as active users, conversions or revenue. Set the sparkline to the appropriate granularity: by day if the scorecard shows a short period like 30 days; by month if the date range covers a year.

A few sparkline options to consider:

  • Fill vs line and smooth vs straight — I usually keep a simple filled or thin line; smooth curves can look nice but be careful when accuracy matters.
  • Missing data handling — choose line to zero, line break, or interpolation depending on whether you want gaps to be visible.
  • Color — sparklines follow the dashboard palette, so choose your first palette color for the main KPI and keep consistency across tiles.
Sparkline styling options in the scorecard style tab

Step 6: Filters and date range behavior

Filters and date ranges affect a scorecard at three levels in Looker Studio: report-level, page-level and chart-level. Use this to your advantage.

  1. Report-level filter: applies to every page and chart by default (useful for global exclusions like internal traffic).
  2. Page-level filter: applies only to the current page (handy for multi-page reports that each focus on a different channel or region).
  3. Chart-level filter: applies to a single tile (perfect when you want one KPI to show Paid traffic while the others show an aggregate).

For date ranges, I strongly recommend leaving the scorecards on Auto so they follow the report or page date control. Only use Custom date ranges on an individual tile when you need that tile to always show a fixed comparison period regardless of the page filter. Auto gives consistent behavior: when the viewer changes the page date range, the KPI tiles update automatically.

Filter menu showing report, page and chart levels

Step 7: Comparison options — previous period, year over year, or target

Comparison settings are one of the most powerful parts of scorecards. You can compare the current period to the previous period, the previous year, or define an advanced custom comparator. The comparison appears as a small percentage or numeric delta.

Two comparison modes I use frequently are:

  • Period comparison: Compare the selected period to the previous period. This is the default and often the most useful for monthly performance checks.
  • Value comparison (target): Use this when you want to show progress against a fixed target. For example, if the KPI target is 50,000 active users you can show a progress bar and percentage to indicate how close you are to the goal.

Note that you can also compare to another metric. This is useful when you have custom columns (for example planned vs actual in a Google Sheets source). Be careful not to compare unrelated metrics — that makes the comparison meaningless.

Comparison settings showing previous period, previous year and value comparisons

Step 8: Style, layout and multiple tiles

Once your scorecard is configured, it is time to style it and replicate a consistent tile across the row. I recommend the following approach:

  • Keep the number of scorecards between four and eight. Six is a sweet spot: enough metrics to cover priorities but not so many that priorities blur.
  • Use the same background, border radius and padding across tiles. I like a subtle transparent gray background and rounded corners for each tile.
  • Align titles and numbers to the center for a clean, balanced look. Hide the tile title where it’s redundant, and use the section header to describe the group.
  • Use compact numbers for large values (e.g., 1.2M instead of 1,200,000) so tiles stay readable.

To duplicate tiles quickly: format one perfectly, then copy and paste it. Replace the metric for each copy and use the distribute horizontally and distribute vertically controls to space them evenly.

Six styled KPI tiles laid out evenly across the dashboard

Step 9: Final checks and practical tips

Before you call the KPI strip done, run a quick checklist I always use:

  • Do the metrics match stakeholder priorities? If unsure, ask stakeholders directly and offer a default set you think they need.
  • Is every tile using Auto date range (unless it intentionally uses Custom)?
  • Are sparklines readable and set to the right granularity?
  • Have you removed unnecessary optional metrics or enabled them intentionally?
  • Are colors consistent with your theme so green and red align with positive and negative changes?

Small details matter. I once spent time trying to tone down a bright comparison label but found that Looker Studio did not allow a separate color for the month-over-month label. Sometimes you hit product limitations, so pick the clearest, simplest visual that communicates the number without extra fuss.

Example set of scorecards I build for a typical website dashboard

  • Active users (with sparkline) — primary health metric
  • New users — acquisition snapshot
  • Average session duration — engagement signal
  • Engagement rate or conversions — interaction / quality
  • Purchases (total purchases) — e-commerce action
  • Revenue (compact number) — business outcome

Each of these tiles can include a period comparison and a sparkline. Keep titles short so they fit within the tile; if a default GA4 name is too long, edit the label to something concise.

“If you have too many priorities, you don’t have priorities.” I follow that rule when choosing which scorecards to surface.

Summary

A clear KPI bar turns a dashboard from a data container into a decision tool. Add a title and subtitle for context, use 4-6 focused metrics, enable sparklines for trend context, keep date controls on Auto, and use comparisons thoughtfully. Style tiles consistently and keep the interface simple for viewers. When in doubt, prioritize clarity over clever visuals.

If you want to follow the exact steps in action, watch the tutorial video: https://www.youtube.com/watch?v=5dW6F9gQ2rM

Further reading and resources

For related guides and templates on building Looker Studio dashboards, check out these posts on my site:

Looker Studio Connectors I recommend:

  • Supermetrics — my go-to when budget allows. It’s polished, well-documented, renames fields into readable labels, and has fast support.
  • Windsor.ai — my favorite recent pick for projects that need BigQuery without the enterprise price tag. They grow quickly, support many connectors, and their documentation is improving. Use promo code “gaillereports” for a 10% discount.