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Marketing Data · Warehoused, Modeled, Automated

AI Dashboards That Turn Marketing Data Into Weekly Decisions

Our team pipes your GA4, HubSpot, Salesforce, Google Ads, Meta Ads, and LinkedIn spend into a single warehouse, models it against your revenue, and ships a live Looker Studio or Metabase dashboard plus an AI-generated weekly decision brief every Monday morning. No more Sunday-night spreadsheet exports.

See sample dashboards and weekly briefs →
28Dashboards Live
14Sources Modeled
21Day Build
100%Your Warehouse
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We model on: GA4 BigQuery Snowflake Looker Studio Metabase Grafana HubSpot Salesforce Google Ads LinkedIn Ads Stripe Fivetran
Quick Answer
What is an AI dashboard for marketing?

An AI dashboard for marketing pipes every paid, organic, CRM, and revenue source into a single warehouse (BigQuery or Snowflake), models the data against your funnel stages and revenue, and renders it in Looker Studio, Metabase, or Grafana. On top of the visualization layer, a Claude-powered brief is generated every Monday morning that summarizes what moved, what caused it, and which three decisions the marketing team should make this week. The output replaces manual spreadsheet exports and ad-hoc analyst work.

Instant Proof

28 dashboards in production across B2B accounts.

Aggregate stats from AI dashboard builds our team has shipped for B2B SaaS, agency, and services clients over the trailing 12 months.

28

Live marketing dashboards under management

14

Median unified data sources per dashboard

21

Median days from scoping call to live dashboard

92%

of clients extend into a second dashboard within 90 days

“

We were spending 9 hours a week rebuilding the same channel-mix slide. MV3 shipped a single Looker Studio dashboard fed by BigQuery and gave our CMO a Monday morning brief that killed our weekly reporting meeting entirely. Analyst time went straight back into forecasting.

RK
Renee
Director of Marketing Operations at a Series C B2B SaaS platform

Composite illustration. Company name held under NDA. Metrics verified against client HubSpot, BigQuery, and Slack timestamps.

The Complete Build Package

What ships when you engage our analytics team.

Fixed-scope builds. Fixed price. Every deliverable ships to your warehouse and BI account with source SQL, dbt models, and 30 days of post-launch tuning.

Discovery + KPI scoping session (2 hrs) $1,497
Data source audit + warehouse architecture doc $1,997
Warehouse setup + ELT pipelines (Fivetran, Airbyte, or n8n) $3,997
dbt models + revenue attribution logic $3,497
Looker Studio, Metabase, or Grafana dashboard build $2,997
AI weekly decision brief generator (Claude powered) $2,497
Alerting layer + threshold-driven Slack notifications $1,497
Handoff documentation + Loom walkthrough library $997
30-day post-launch tuning + monitoring $1,497
Retail value if purchased individually $20,473
Setup one-time, then monthly $7,997 + $5,997/mo

Setup is a one-time project fee. Ongoing modeling, dashboard tuning, and the AI weekly brief run under Growth AI ($5,997/mo). Multi-dashboard programs run under Scale AI ($9,997/mo) or Enterprise.

What You Actually Get

A dashboard that survives your next reorg.

Six concrete deliverables ship on every AI dashboard engagement. You keep the warehouse, the SQL, the dbt models, and the dashboard.

01
Unified Marketing Warehouse

BigQuery or Snowflake with GA4, HubSpot, Salesforce, Google Ads, Meta Ads, LinkedIn Ads, Stripe, and any other source you nominate, piped via Fivetran, Airbyte, or n8n.

02
dbt Model Library + Revenue Attribution

Version-controlled dbt models that stitch touchpoints to opportunities to revenue. First-touch, last-touch, and multi-touch attribution modeled and reconcilable.

03
Live Dashboard on Your BI Tool

Looker Studio, Metabase, or Grafana with drilldowns by channel, campaign, funnel stage, segment, and cohort. Executive summary tab plus operator deep-dive tabs.

04
AI Weekly Decision Brief

A Claude-generated executive brief delivered to Slack and email every Monday morning. What moved, what caused it, and three specific decisions to make this week.

05
Alerting + Anomaly Detection

Thresholds and week-over-week anomaly rules that fire into your Slack. CAC drift, spend anomalies, pipeline coverage warnings, and revenue attribution gaps flagged automatically.

06
Handoff Documentation

A README, an architecture diagram, a Loom walkthrough, and a runbook. Any analyst or engineer on your team can extend the dashboard after our team hands off.

Strategy + Implementation

We define the decisions. Then we build the dashboards that answer them.

Consulting-only analytics shops hand you a KPI framework. Dev-shop-only teams build dashboards without a strategic thesis. MV3 does both: a scoped decision architecture paired with the engineering team that pipes, models, and visualizes the data on your stack.

Strategy: The Decision Architecture

Which decisions, which metrics, at what cadence.

Before we write a single SQL model, our Advertising and Analytics Lead scopes the decision loops against your revenue targets, marketing motion, and team ritual. You get a defensible KPI tree and a fixed price before you sign.

  • 1Decision inventory: which weekly and monthly decisions need data behind them.
  • 2KPI tree: revenue at the root, marketing inputs at the leaves, causality mapped between.
  • 3Data source map: every platform, API, refresh cadence, and identity resolution rule.
  • 4Attribution model: first-touch, last-touch, or multi-touch, picked against your motion.
  • 5Delivered as a fixed-scope SOW plus a Notion decision-architecture doc.
Implementation: The Build

Our team ships the warehouse, models, dashboards, and weekly brief.

Once the SOW is signed our analytics engineers build the pipelines, the dbt models, the dashboard, and the AI weekly brief inside your BI account. You own the warehouse and the runtime. We tune it for 30 days after go-live.

  • 1Warehouse provisioned in BigQuery or Snowflake, ELT wired via Fivetran, Airbyte, or n8n.
  • 2dbt models version-controlled in git, unit-tested against golden data.
  • 3Dashboard built in Looker Studio, Metabase, or Grafana with drilldowns and cohorts.
  • 4AI weekly decision brief generated by Claude, delivered to Slack every Monday.
  • 5Handoff to your analytics team with docs, Looms, and a runbook.
Book my scoping call →

Bundling multiple dashboards or a warehouse migration? Request a custom proposal →

Our Guarantees

Three guarantees on every dashboard engagement.

If we miss any of them, you pay nothing for that build cycle. Written into every SOW.

Fixed Scope, Fixed Price

Every dashboard ships against a signed SOW with fixed scope and fixed price. If our team needs more hours than scoped, we absorb the cost. Not you.

You Own The Warehouse

The warehouse, the dbt models, and the dashboards deploy to your accounts. You own the SQL, the source, and the runtime. No vendor lock-in. If you ever fire us, everything keeps running.

On-Time Delivery

Every SOW ships against a documented go-live date. Miss the date and the next month of monitoring is free. First slippage in 28 dashboard builds triggers a full retro.

Sample Deliverables

What real dashboard deliverables look like.

Sanitized artifacts from live builds. Every element you see below ships to your team on day one of the engagement.

Deliverable 01

The executive summary tab

One tab tuned for the CMO, CRO, and CEO. Six revenue-relevant KPIs, week-over-week deltas, channel mix, pipeline coverage, and CAC by segment. Every number links to an operator drilldown so nothing is a black box.

  • Six revenue KPIs at the root, weighted by pipeline contribution
  • Week-over-week and month-over-month deltas with anomaly flags
  • Channel mix by cost, pipeline, and closed-won revenue
  • Pipeline coverage against forecast, by segment and rep
  • CAC and payback by channel, quarter, and cohort
  • Every metric drills into an operator tab with row-level detail
Executive Marketing Dashboard, Q4
Pipeline / Q
$4.82M
+18% WoW
Closed-Won
$1.14M
+11% WoW
Blended CAC
$1,847
-8% WoW
Payback (mo)
7.3
-1.2 WoW
Channel Mix, Pipeline Contribution
Google Ads
38%
LinkedIn Ads
24%
Organic SEO
18%
Meta Ads
12%
Direct + Referral
8%
Deliverable 02

AI weekly decision brief

Every Monday at 8am your Slack gets a Claude-generated brief: what moved last week, what caused it, and three specific decisions to make this week.

Week of Nov 18, marketing brief
1. LinkedIn CAC up 22% WoW. Cause: two ad sets scaled past efficient frontier. Recommend cutting spend on Campaign C7 and C11.
2. Pipeline coverage 3.2x, on track for Q. No action needed.
3. Organic /geo-audit/ up 47% MoM. Recommend accelerating the AI SEO retainer to compound the trend.
Deliverable 03

Alerting + anomaly detection

Threshold and week-over-week anomaly rules route to Slack. Your team hears about a broken tracker or a runaway ad spend before it costs a full quarter.

GA4 events dropped 40% critical
Google Ads spend anomaly warn
HubSpot form conversion up 22% ok
Attribution gap on Q3 cohort warn
CAC drift on LinkedIn (+18%) warn
Pipeline coverage under 3x critical
Data Source Map: Platforms × Funnel Stages
GA4
HubSpot
Google Ads
LinkedIn Ads
Salesforce
Stripe
Awareness Engage MQL SQL Revenue
Blue = source contributes to that funnel stage. Every touchpoint reconciled in dbt.
Deliverable 04

Data source map

Every platform your dashboard reads from, at every funnel stage. Refresh cadence, identity resolution rule, and reconciliation logic documented so your analytics team can audit and extend the models independently.

Why this matters:

A dashboard with 12 sources and no map is a dashboard nobody trusts. The source map is the artifact your analytics team uses when a vendor deprecates an endpoint, when your CFO asks why two numbers disagree, or when a new platform enters your stack.

Deliverable 05

Handoff runbook and Loom library

A written runbook, an architecture diagram, and a Loom walkthrough for every dbt model, dashboard tab, and alerting rule. Any analyst on your team can extend or debug the dashboard after handoff without a call to us.

Book my scoping call →
README Warehouse setup, credentials, refresh schedule
Architecture Full diagram + data source map (PDF)
dbt Model Docs Every model, test, and lineage graph
Loom Walkthroughs Tab-by-tab dashboard handoff library
Runbook Common failures, fix steps, and escalation
Methodology

How we build every AI dashboard.

Five stages. Same protocol every engagement. Reproducible so your team can audit any number, from any dashboard tab, at any time.

01
Scope

Decision loops, data sources, and success metrics locked with your team in a 2-hour scoping call. Fixed SOW ships in 3 business days.

02
Architect

KPI tree, warehouse schema, data source map, and attribution model signed off by your analytics lead.

03
Build

Warehouse provisioned, ELT pipelines wired, dbt models version-controlled in git. Dashboard built in Looker Studio, Metabase, or Grafana.

04
Test

Every model runs against golden data. Row-level reconciliation to source of truth. Alerting rules validated against 90 days of historical data.

05
Ship + Tune

Dashboard goes live, weekly brief starts firing on the first Monday. Our team monitors and tunes for 30 days. Handoff docs and Looms delivered.

Composite Outcomes

What clients report after the dashboard goes live.

Outcomes reported by AI Dashboards clients in the 90 days post go-live. Company identities are protected under NDA. Personas are composite illustrations of role, stage, and category.

“

Our CMO used to spend Sunday nights rebuilding channel-mix slides. MV3 shipped a Looker Studio dashboard fed by BigQuery in three weeks and replaced the weekly reporting meeting with a Monday morning Slack brief. Analyst time went back into forecasting.

RK
Renee
Director of Marketing Operations at a Series C B2B SaaS platform
“

The AI weekly brief caught a runaway LinkedIn spend anomaly on a Tuesday that would have burned $18K by Friday. That single catch paid the whole build back in month one.

AS
Aditya
VP Growth at a mid-market fintech platform
“

We consolidated Google Ads, LinkedIn, Meta, HubSpot, and Stripe into a single warehouse and finally know our real CAC by channel. Our CRO now runs the forecast off one dashboard instead of five spreadsheets.

JV
Julia
Head of Revenue Ops at a Series B B2B SaaS platform

Company names withheld under NDA. Metrics verified against client HubSpot, BigQuery, and Slack timestamps. Initials avatars used because per-NDA no client likeness is displayed.

Recent Work

Anonymized wins from recent dashboard builds.

Six recent AI dashboard engagements, sanitized of client identifiers. Full un-redacted case studies are available under NDA on request.

B2B SaaS · Series C
Killed the weekly reporting meeting with a Monday AI brief.
9 hrs
Analyst hours reclaimed per week
AI Dashboards→
 Fintech · Mid-Market
AI brief caught a runaway ad spend anomaly in 6 hours.
$18K
Ad spend saved on a single anomaly
AI Dashboards→
 DevTools · PLG
Unified 8 sources into one warehouse and one CAC number.
8 → 1
Data sources consolidated into a single view
AI Dashboards + Warehouse→
 MarTech · Series B
Weekly board-ready pipeline report auto-published to Slack.
6 hrs
Board-prep hours saved per quarter
AI Dashboards→
 Healthtech · Growth
CAC by channel model exposed a 41% inflation vs. reported number.
41%
CAC gap between platform and warehouse truth
AI Dashboards + Attribution→
 Cybersecurity SaaS
Multi-touch attribution model closed the reporting gap to CRO.
100%
Pipeline reconciled between marketing and sales
AI Dashboards→

Metrics tracked in client HubSpot, BigQuery, Looker Studio, and Slack. Individual outcomes vary by data quality, source coverage, and reporting complexity.

What AI Dashboards Typically Uncover

Aggregate findings from 28 production dashboards.

Data pulled from MV3’s AI dashboard portfolio, trailing 12 months. No client identities.

33%

Median CAC gap between platform-reported and warehouse-modeled truth

71%

of clients discover a broken tracker inside the first 30 days

9 hrs

Median analyst hours reclaimed per week per dashboard

3.4x

Median return on build cost, year 1, from spend reallocation

Individual results vary by data quality, source coverage, and existing tooling.

AI Dashboards Programs
$7,997 setup + $5,997/mo

One dashboard, one warehouse. Growth AI tier. Multi-dashboard programs run under Scale AI ($9,997/mo) or Enterprise.

  • Unified marketing warehouse (BigQuery or Snowflake) with all sources piped in
  • dbt models with revenue attribution reconciled to closed-won
  • Live dashboard on Looker Studio, Metabase, or Grafana
  • AI weekly decision brief in Slack every Monday morning
  • Alerting layer with anomaly detection routed to your Slack
  • Handoff runbook, Loom walkthroughs, and architecture diagram
  • 30-day post-launch tuning and monitoring included
Everything Included, At Retail
Discovery + KPI scoping (2 hrs)$1,497
Data source audit + warehouse architecture$1,997
Warehouse setup + ELT pipelines$3,997
dbt models + revenue attribution$3,497
Dashboard build (Looker, Metabase, or Grafana)$2,997
AI weekly decision brief generator$2,497
Alerting + anomaly detection layer$1,497
Handoff runbook + Loom walkthrough library$997
30-day post-launch tuning + monitoring$1,497
Retail value if purchased individually$20,473
Your price$7,997 + $5,997/mo
Book my scoping call →

Free scoping call. Fixed SOW inside 3 business days. No payment until you sign.

Qualification

This is not for you if…

We hold a hard fit bar on AI dashboard engagements. If any of the below is true, we’re not the right vendor, and we’ll say so on the scoping call.

  • You want a $49/month templated dashboard. Our builds are custom warehouses, dbt models, and BI tools with real revenue attribution. If a templated SaaS dashboard solves your problem, buy that.
  • You need it live next week. Real dashboards ship in 21 to 45 days. Rushed builds skip source reconciliation and attribution validation, then produce numbers your team stops trusting.
  • You want us to run the warehouse forever. Every build ships to your accounts with full SQL, dbt code, and docs. If you want ongoing operations we do that too, but the warehouse and models are yours to keep.
  • You have no defined decision loops. We need concrete decisions to scope the dashboard against. If you want a KPI framework consult before you know what to decide, start with the GA4 Audit or a marketing operations discovery instead.
  • Your source data is chaos. Dashboards compound value on top of clean tracking. If GA4 is broken, HubSpot properties are inconsistent, and UTMs are unenforced, we recommend a tracking hygiene sprint first.

If you’re a fit, keep scrolling. Or book the scoping call now →

Frequently Asked

Questions buyers ask us.

What exactly is an AI marketing dashboard?
A unified warehouse (BigQuery or Snowflake) fed by every marketing platform in your stack (GA4, HubSpot, Salesforce, Google Ads, LinkedIn Ads, Meta Ads, Stripe, and any other source you nominate), modeled in dbt against your funnel and revenue, and visualized in Looker Studio, Metabase, or Grafana. On top of the visualization layer, a Claude-powered weekly brief summarizes what moved and which decisions to make.
How is this different from Looker Studio or Metabase on their own?
Looker Studio and Metabase are excellent visualization layers and we build on both. The difference is the warehouse, the dbt models, the attribution logic, the alerting rules, and the AI weekly brief that sits on top. A raw dashboard connected to GA4 with no warehouse and no attribution model is a dashboard your team stops trusting inside a quarter.
Do you use Looker Studio, Metabase, or Grafana?
All three. Looker Studio when your team is already on Google Workspace and wants free access at scale. Metabase when you want SQL-friendly self-service and open source hosting. Grafana when you need real-time operational dashboards and Prometheus-style monitoring. We pick the BI layer that fits your stack, not ours.
How much does an AI dashboard cost?
Setup is $7,997 one-time for one warehouse and one dashboard. Ongoing modeling, dashboard tuning, and the AI weekly decision brief run under Growth AI at $5,997/month. Multi-dashboard programs run under Scale AI at $9,997/month. Custom enterprise programs with multiple warehouses and dedicated analytics engineering start at $15,000/month. Every SOW is fixed scope, fixed price. Scoping call is free.
How long does a dashboard build take?
21 to 45 days from signed SOW to live dashboard. Scoping and architecture sign-off happen in the first 5 to 10 days. Warehouse setup, dbt modeling, and dashboard build run over the next 15 to 30 days. Post-launch tuning runs for 30 days after go-live.
Which data sources can you pipe in?
GA4, HubSpot, Salesforce, Google Ads, LinkedIn Ads, Meta Ads, Stripe, Snowflake, Postgres, BigQuery, Amplitude, Mixpanel, Segment, Google Search Console, and any REST API. Fivetran, Airbyte, and n8n cover the ELT layer. If a source is not on the list, we can typically wire it in a week.
What happens if the numbers disagree between platforms?
This is the most common reason clients hire us. Platforms count differently (attribution windows, session definitions, deduplication rules, refresh cadences). Our dbt models reconcile every source against a single source of truth and document every reconciliation rule. When numbers disagree post-launch, the source map tells your team exactly why in under 5 minutes.
Who owns the warehouse and the dashboards?
You. The warehouse deploys to your BigQuery or Snowflake account. The dbt code sits in your git repo. The dashboards live in your Looker Studio, Metabase, or Grafana account. You own the SQL, the models, the runtime, and the data. No vendor lock-in on our platform because there is no MV3 platform.
Program Lead
Blagovest Iordanov · Advertising and Analytics Lead, MV3 Marketing

Google and Meta certified. Manages $15M+ in annual ad spend across B2B accounts. Expert in server-side tracking, GA4 attribution, and performance reporting. Blagovest signs off on every dashboard architecture and reviews every AI weekly brief before it goes live.

4 dashboard slots opening next quarter

Get your marketing data off spreadsheets. For good.

Free scoping call. Fixed SOW in 3 business days. Live dashboard in 21 to 45. Your team owns the warehouse.

Book my scoping call →