Dashboard Features Explained
Simple explanations of what each quoter option means
When building a marketing dashboard, there's often more involved than just connecting data sources and displaying charts. This guide explains the key features and add-ons you might need, and why they matter for your reporting.
Campaign classification adds custom grouping labels to your campaigns that don't exist in the raw data. It lets you categorise campaigns by business logic like division, product line, funnel stage, or any other grouping that makes sense for your reporting.
Ad platforms only know what you tell them. If you have 50 campaigns across different business units, the platform doesn't know which belong to "Recruitment" vs "Brand Awareness" vs "Product Sales". Classification adds that intelligence layer.
You run campaigns for both volunteer recruitment and emergency alerts. By adding a "Division" classification, you can filter your dashboard to show only Volunteer campaigns, or compare performance across divisions - even though Google Ads has no idea these categories exist.
We create a lookup table that maps campaign names (or IDs) to your custom categories. When a campaign matches a pattern, it gets tagged with the appropriate label. These labels become filterable dimensions in your dashboard.
2024_FB_VolunteerRecruit_Perth
2024_FB_FireAlert_Metro
2024_GADS_VolunteerRecruit_Rural
Volunteer Recruitment → Perth
Emergency Alerts → Metro
Volunteer Recruitment → Rural
Conversion grouping standardises and consolidates the different conversion actions tracked across your platforms. It ensures consistent naming and allows you to combine related conversions into meaningful totals.
Different platforms name things differently. Google Ads might track "Form_Submit_Contact_Page" while Meta tracks "lead" - but they're the same action. Without grouping, your dashboard shows fragmented, hard-to-compare data.
You track 15 different conversion actions across Google and Meta. Some are variations of the same thing (Android app install vs iOS app install). Grouping lets you see "Total App Installs" as one number, while still being able to drill down to platform-specific details.
We create a naming lookup that:
actions_app_site_visit
mybushfireplan_visitors
Fire Danger Rating Scale - Catastrophic
META - App Site Visit
META - MyBushFirePlan Visitors
GADS - FDRS Catastrophic
Data unification combines data from multiple ad platforms into a single, consistent table structure. This "flat table" or "master table" becomes the single source of truth for your dashboard.
Each platform structures its data differently. Google Ads exports in one format, Meta in another. Without unification, you'd need separate charts for each platform and couldn't easily compare or total across them.
You want to see "Total Ad Spend" across Google, Meta, LinkedIn, and TikTok. Without a unified table, you'd have to manually add up four different numbers. With unification, it's one metric that automatically totals everything.
The unification process:
The result is one table where you can filter by platform, compare platforms side-by-side, or see totals across everything.
Additional data structure exports from the same platform type. This is when you need a different table or report format from a platform you already have connected - requiring a separate data source in Looker Studio.
Sometimes the standard data export from a platform doesn't contain everything you need. You might require a separate table with different dimensions, a different granularity level, or supplementary data that can't be combined in the same export.
Your Google Ads connection pulls campaign-level data with standard metrics. But you also need ad group-level data with different dimensions, or a separate conversion breakdown table. Each distinct data structure requires its own data source configuration and Looker Studio connection.
Note: Multiple ad accounts within the same platform are typically handled by the data connector (Power My Analytics) automatically - you don't need extra data sources just because you have multiple accounts.
Extra pages or views within your dashboard beyond what's included in your package. Each page typically focuses on a specific aspect of your data or a particular audience's needs.
Different stakeholders need different views. Executives want high-level summaries, campaign managers want detailed breakdowns, and finance needs budget tracking. More pages means more tailored views.
Executive Summary (KPIs only) → Campaign Performance (detailed metrics) → Channel Comparison (platform vs platform) → Budget Pacing (spend tracking) → Demographics (audience insights) → Conversion Analysis (funnel view)
A custom-built connection to a data source that doesn't have a standard connector. This involves API development, data transformation, and ongoing maintenance.
Not every data source has a plug-and-play connector. Proprietary systems, internal databases, or niche platforms often require custom development to extract and format data for reporting.
You want to include data from your custom CRM, a legacy inventory system, or a specialised industry platform. There's no off-the-shelf connector, so we build one using APIs, database queries, or file imports.
Calculated fields that don't exist in the raw data. These are formulas that compute new values from existing metrics - like conversion rates, cost ratios, weighted scores, or business-specific KPIs.
User-selectable controls that change what the dashboard displays. Parameters let viewers switch between different metrics, toggle conversion types, or choose comparison periods without editing the dashboard.
• Cost Per Qualified Lead (Spend ÷ MQLs)
• Blended ROAS (Revenue ÷ Total Spend across all platforms)
• Efficiency Score (Conversions × 100 ÷ Clicks)
• YoY Growth % ((This Year - Last Year) ÷ Last Year)
• Conversion selector: "Show me [App Installs / Form Submits / All Conversions]"
• Platform filter: "Compare [Google vs Meta / All Platforms]"
• Date granularity: "View by [Day / Week / Month]"
• Goal toggle: "Show performance vs [Target A / Target B]"
Raw platform metrics only tell part of the story. Custom metrics translate data into business language. Parameters make dashboards interactive and self-service, reducing requests for "can you make me a version that shows X?"
An intermediate data processing layer using Google Sheets between your raw data sources and Looker Studio. Data flows through Sheets where it can be transformed, combined, and enriched before reaching your dashboard.
Direct connections to Looker Studio are limited. Sheets provides a flexible transformation layer where you can:
You have conversion data from Meta that needs to be pivoted (transformed from rows to columns), joined with campaign data, enriched with classification labels, and combined with Google Ads data - all before it reaches your dashboard. Sheets makes this possible with formulas that run automatically.
Foundation Package: Uses direct Looker Studio connectors. Simpler setup, but limited transformation capability. Best for straightforward reporting needs.
Growth/Transformation Packages: Include Google Sheets layer by default. Enables advanced data manipulation, cross-platform unification, and complex business logic.