What is marketing analytics, and how do you build it in Metabase?

Marketing analytics turns ad spend, traffic, search, product activation, and email signals into shared metrics about how you acquire and keep customers — and what that costs. In Metabase, build it by syncing daily aggregates of those signals into a SQL database, modeling clean ad-performance, traffic, product-event, and email layers, and shipping dashboards that put acquisition next to the revenue data everyone already trusts.

What does marketing analytics measure?

• What does a customer cost, per channel and blended?
• Which campaigns return their spend — and under whose attribution?
• Where does the funnel leak between visit, signup, and purchase?
• Is organic search visibility growing, and on which queries?
• Which acquisition channels send users who activate and stay?
• Is the email program engaging people or burning the list?
• How does spend pace against plan, before the month ends?

Which tools feed marketing analytics?

What shared data models should you build?

Build clean models on top of raw source tables so each dashboard uses the same definitions.

• ad_performance_daily — one row per campaign per day per channel, with spend, impressions, clicks, conversions, and conversion value
• campaigns — one row per campaign, with objective, channel, and status
• traffic_rollups — daily sessions, engaged sessions, and key events by channel, source, and landing page
• gsc_performance_daily — organic clicks, impressions, CTR, and position by query and page
• users — one row per product user, with signup date, activation date, and acquisition channel
• event_rollups — daily product event counts and unique users
• email_campaign_stats — one row per campaign send, with delivered, clicks, bounces, and unsubscribes
• leads and customers — the funnel's bottom, with UTM attribution and acquired-at timestamps
• budgets — planned spend by channel and month, for pacing

Which marketing metrics matter most?

• ROAS — conversion value over ad spend, by campaign and channel.
• Customer acquisition cost — spend over new customers, blended or paid-only.
• Cost per lead — spend over leads, raw and qualified.
• Cost per click — the price of attention, compared within a channel.
• Click-through rate — clicks over impressions or delivered emails.
• Landing-page conversion rate — conversions over sessions, per page.
• Organic clicks & impressions — search visibility and what it earns.
• Activation rate — signups reaching the product's value moment.
• List growth rate — net subscriber growth for newsletters and lifecycle programs
• Sync success rate and data freshness — the reliability of the customer-data pipelines underneath
• Conversion rate and LTV — the funnel and value pair that closes the loop.

How do you connect tools to Metabase?

1. MCP + CLI — use MCP for a scoped, summarized live export, then mb upload csv for quick analysis.
2. Warehouse-backed pipeline — sync daily stats, rollups, and entities with APIs or connectors for durable dashboards.
3. Modeled layer — map each source into the shared ad-performance, traffic, product, and email models while preserving source-specific extension tables.

Which dashboards should you build first?

• Paid channel performance — spend, ROAS, and efficiency across platforms.
• Ad spend pacing — budget burn against plan, with projections.
• Marketing funnel — visit to signup to customer, by channel.
• Organic search performance — queries, pages, and branded vs. non-branded.
• Paid vs. organic mix — the acquisition balance over time.
• Product retention — activation, cohorts, and channel quality.
• Email engagement — campaign performance, deliverability, and list health.
• CDP sync health — whether the pipelines feeding every other dashboard actually run.
• Newsletter growth — net subscriber growth by source, engagement, and revenue per send.

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