Filtering, dimensions & comparisons

Every report has a date range set at the top, and changing it updates every widget at once — there's no per-widget date juggling for the common case. Pick a preset — from last 7 days, last 30 days, this month or last month, right through to last quarter, last 12 months, year to date or all time — or set a custom start and end date. Because the builder runs on live data, moving the range re-queries each source immediately, so the preview always reflects the window you've chosen. Match the range to the story you're telling: a monthly client report sits naturally on "last month," while a half-year or full-year performance review can run on last 12 months, year to date, or a custom window that spans several months to show a trend properly.

When a report covers a long window — a half-year, a year, a custom multi-month range — a chart plotted day by day becomes an unreadable wall of ~180 bars. That's what the Time scale control fixes. Click any line or bar widget and, under Chart display, switch its Time scale between Daily, Weekly and Monthly. Set it to Monthly and a twelve-month range collapses to twelve clean bars — "Mai 2026 → Juni 2026 → …" — instead of one bar per day; Weekly gives you calendar weeks for something in between. The setting is per widget, not global to the report, so you can show a monthly half-year or yearly trend at a chosen spot on the page while other charts stay daily. And it stays honest: each bucket is aggregated exactly like the KPI card over that period — a monthly bar equals that month's scorecard — with base metrics (spend, clicks, conversions) summed and rates (ROAS, CTR, cost per conversion) recalculated from the totals rather than averaged. So a longer time scale changes only how the trend reads, never the underlying numbers.

A number on its own rarely tells a client much; what changed is what matters. Add a comparison period and every scorecard and chart gains a change indicator showing movement against the baseline. You can compare to the previous period (the equivalent window immediately before), the same period last year (which controls for seasonality), or a custom window of your choosing. Previous-period comparisons are great for spotting recent momentum; year-on-year is the fairer read for seasonal businesses. Pick whichever frames the result honestly — the goal is to give context, not to flatter the numbers.

Dimensions are the ways you break a metric down — by campaign, ad group, landing page, device, country, and so on, depending on what the source exposes. A secondary dimension adds a second layer to a table so you can read two attributes at once: campaign by device, for example, or landing page by country. It's the difference between "this campaign spent X" and "this campaign spent X, and most of it went to mobile." Reach for a secondary dimension when a single breakdown raises an obvious follow-up question. Keep it to one extra layer in a client-facing table, though — two dimensions illuminate, three usually just clutter.

Filters narrow a widget to just the rows you care about. You can apply several conditions at once and choose how they combine. AND logic requires every condition to be true — "campaign contains Brand AND device is Mobile" — which tightens the focus. OR logic matches rows meeting any condition — "country is Germany OR country is Austria OR country is Switzerland" — which is how you group a region or a set of related campaigns. Combine filters with a secondary dimension and a comparison period and you can answer a precise question on a single widget — say, how this quarter's branded mobile campaigns in the DACH region performed against last quarter — without building a separate report for it.

A common client question is "which products did a given channel actually sell?". You can answer it on a single GA4 widget. Build a table on a GA4 data source, set its dimension to an item breakdown — Item name, Item category or Item brand — and choose e-commerce metrics such as Items purchased, Item revenue, Items added to cart or Items viewed. Then add a filter on the traffic source, using Session source / medium (for example "google / cpc" for Google Ads, or "google / organic" for organic search), Session source, or Session medium. The result is a product breakdown scoped to that channel — for instance, the items purchased by visitors who arrived from Google Ads. Swap the filter value to compare channels, or drop the filter to see products across all traffic. It's a filtered breakdown rather than a separate pre-built metric, so you keep full control over which items, metrics and source you're looking at.