Analytics
One dashboard. Every channel. Decisions, not downloads.
The average marketing team has five separate platform dashboards, two Slack channels sharing screenshots of metrics, a weekly Excel file someone builds manually, and a Monday morning meeting where the first 20 minutes is reconciling why the numbers differ between systems. A marketing dashboard is not a vanity project. It is the infrastructure that makes faster, better decisions possible. Without it, marketing is managed by whoever shouted the loudest number last.
The reporting problems a dashboard solves.
Most marketing reporting is reactive, manual, and contested. Here is what that looks like in practice.
The weekly report takes half a day to build.
Someone downloads data from Meta Ads Manager, Google Ads, GA4, and HubSpot every Monday morning. They paste it into an Excel sheet, calculate blended numbers manually, and build a slide. This takes 3-4 hours. It is late by Wednesday. The data is from last week. Decisions made on it are already 10 days behind.
Every platform tells a different story.
Meta says ROAS is 3.8x. GA4 attributes 40% of that revenue to organic. The CRM shows 60% of the deals came from referrals. All three numbers are true within their own attribution window and definition. Without a single view that normalises the attribution, the budget discussion is a debate about whose number to trust rather than where to allocate spend.
There is no blended CAC view.
The Google Ads team reports Google CPL. The Meta team reports Meta CPL. Neither includes agency fees, creative production costs, or tool costs. The actual blended cost per acquired customer, across all channels and all costs, is unknown. The business is making channel allocation decisions without knowing the total cost of acquisition.
Leadership is looking at lagging indicators.
The board deck shows last quarter's numbers. The CEO asks about this quarter's pipeline and the answer is a number from the weekly call, not a live dashboard. Decisions about budget allocation, headcount, and channel investment are made on data that is 30-90 days old.
There is no connection between marketing spend and pipeline.
The marketing dashboard shows CPL and ROAS. The sales dashboard shows pipeline and close rate. Nobody has connected the two. Marketing cannot see which campaigns are producing pipeline. Sales cannot see where the leads came from. The two teams operate on different data and have different views of what is working.
How we build a marketing dashboard.
A dashboard is only as good as the data feeding it. We fix the data connections before building the visualisation.
Measurement requirements
- Stakeholder review, who uses the dashboard, what decisions do they make, what do they need to see
- KPI hierarchy, which metrics are primary (decision-driving) and which are diagnostic (context-providing)
- Data source inventory, every platform that needs to be connected listed with connector availability
- Attribution model decision, which attribution model the dashboard uses and why, agreed before build
- Refresh cadence, how often data needs to update for each source
- Access requirements, who can view and who can edit the dashboard
Data connections
- GA4 connector, sessions, users, conversions, and channel data connected
- Google Ads connector, spend, clicks, conversions, and CPA by campaign
- Meta Ads connector, spend, reach, results, and ROAS by campaign and ad set
- LinkedIn Ads connector, spend, MQLs, and CPL from campaign data
- CRM connector, pipeline, MQL-to-SQL rate, and close rate by source
- Blended cost data, agency fees and tool costs added as manual or spreadsheet inputs
- Data freshness monitoring, each connector tested for update latency and failure alerting
Dashboard build
- Executive summary page, blended CAC, total spend, total pipeline, and MoM trend in one view
- Channel comparison page, spend, CPL, pipeline contribution, and close rate by channel side by side
- Paid media detail page, campaign-level data for each active paid channel
- Organic and content page, GA4 organic sessions, top content, and conversion by landing page
- Pipeline attribution page, CRM deal source matched to marketing campaign
- Date comparison controls, current period vs. previous period and year-on-year
- Brand colour and formatting, dashboard designed to match brand guidelines for presentation use
Training and maintenance
- Dashboard walkthrough, 60-minute session on how to read and use each dashboard page
- Interpretation guide, written notes on each metric definition and how to act on it
- Filter training, how to use date range, channel, and campaign filters
- Anomaly response guide, what to check when a metric moves unexpectedly
- Monthly check-in, 30-minute monthly call for the first 3 months to answer questions and refine
What a marketing dashboard engagement includes.
Data Connections
- GA4 connector
- Google Ads connector
- Meta Ads connector
- LinkedIn Ads connector
- CRM connector (HubSpot / Zoho)
- Spreadsheet blended cost input
Dashboard Pages
- Executive summary
- Channel comparison
- Paid media detail
- Organic and content
- Pipeline attribution
- Date range comparison
Functionality
- Date range controls
- Channel filter
- Campaign filter
- Brand-matched design
- Mobile-responsive layout
- Exportable PDF report
Handover
- Dashboard walkthrough
- Metric definition guide
- Filter training
- Anomaly response guide
- 3-month monthly check-in
- Connector maintenance
This is right for you if:
- Marketing teams managing 3+ channels with no unified performance view
- Businesses where the weekly marketing report is built manually every Monday
- CMOs who need to present marketing performance to the board and are stitching together platform screenshots
- Companies where marketing and sales are using different data and arguing about lead quality
- Businesses scaling paid spend who need a blended CAC view before increasing budgets
Not the right fit if:
- Businesses running a single marketing channel, a native platform dashboard is sufficient at that stage
- Companies without clean underlying data sources, a dashboard built on bad data is a bad dashboard built faster
Frequently asked questions.
Why Looker Studio over a custom BI tool?
Looker Studio is free, has native connectors to Google Ads, GA4, Search Console, and YouTube, and has a large library of community connectors for Meta, HubSpot, LinkedIn, and Salesforce. For most marketing use cases, it is the fastest to implement, easiest to share, and requires no infrastructure to maintain. When the business outgrows Looker Studio, we migrate to Looker (paid) or a data warehouse setup. Starting with Looker Studio is the right call for 90% of businesses.
How do we handle attribution when every platform claims credit?
Multi-touch attribution is genuinely hard and the right model depends on your sales cycle and channel mix. For most businesses, we recommend building the dashboard with two attribution views: last-click (which is what most platforms default to) and a custom first-touch or linear model from GA4. The goal is not to find the one true attribution model, it is to have a consistent model applied across all channels so the comparison is apples to apples.
How often does the data refresh?
Google Ads, GA4, and Search Console data refresh every 15 minutes to 1 hour via native connectors. Meta Ads data refreshes every 4-12 hours depending on the connector. CRM data from HubSpot and Salesforce typically refreshes daily. For most operational decisions, daily refresh is sufficient. If you need real-time data for specific metrics, we can discuss custom connector options.
Can we share the dashboard with investors or clients?
Yes. Looker Studio reports can be shared as view-only links, embedded in websites, or exported as PDFs. Access can be restricted to specific Google accounts or made public. For investor reporting, we typically build a separate cleaner version with the metrics investors care about, separate from the operational dashboard the marketing team uses daily.
Ready to replace Monday morning downloads with a live dashboard?
Book a 30-minute call. We will review your current reporting setup and map out exactly which data sources need to be connected.
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