Stop rebuilding the same marketing report every week

The monthly report eats a week you do not have

Reporting is the most automatable job in marketing and the one most teams still do entirely by hand. Someone opens Google Ads, exports last month's spend and conversions, then does it again in Meta Ads Manager, then LinkedIn Campaign Manager, then GA4, then Search Console, then the CRM. Each platform names things differently, uses a different attribution window, and counts a conversion its own way, so before any analysis happens there is an hour of copy-paste and another hour of reconciling mismatched labels in a spreadsheet.

Then the real work starts: building the same charts you built last month, writing the commentary, and formatting it into a deck for the leadership meeting. By the time it lands, the data is a week old and nobody can drill into a surprising number without going back to the raw exports. Worse, the process is fragile. The one person who knows how the master spreadsheet is wired goes on holiday and reporting quietly stops. This is assembly work, not analysis, and assembly is exactly what a machine should own.

What we automate, and how the pipeline works

We build a reporting pipeline that runs on a schedule with no human in the loop until the insight stage. Connectors pull from every channel through their official APIs (or a managed connector layer where one exists), land the raw data in a warehouse like BigQuery or a Postgres database, and a transformation step normalizes it: one campaign taxonomy, one currency, one date grain, one definition of a lead. From there the numbers feed live dashboards in Looker Studio or Metabase and a scheduled summary that gets delivered where your team already works.

The last mile is where it gets genuinely useful. Instead of a wall of charts nobody reads, an LLM step writes a plain-language summary grounded strictly in the pipeline's own numbers: what moved, by how much, and against target, with the anomalies flagged first. That narrative lands in Slack or your inbox every Monday, and the full dashboard sits behind it for anyone who wants to drill in. We orchestrate the whole thing in n8n or Make, with custom code for any source that has no clean connector, so the pipeline never stops at a gap.

• Ad platforms: Google Ads, Meta, LinkedIn, TikTok, and Microsoft Ads for spend, ROAS, and conversions
• Web and SEO: GA4, Google Search Console, and rank trackers for traffic, rankings, and engagement
• CRM and revenue: HubSpot, Salesforce, Pipedrive, and Stripe for pipeline, deals, and actual revenue
• Warehouse and transform: BigQuery or Postgres, with dbt-style normalization into one clean model
• Delivery: Looker Studio or Metabase dashboards, plus written summaries pushed to Slack or email

The reports and alerts we build most often

Most teams start with the report that hurts the most, usually the weekly channel roundup or the monthly leadership deck, then widen once the pipeline proves out. Because everything runs off one normalized model, adding a new view is cheap: the data is already clean, so a new report is a new query and a new layout, not a new export ritual. You are not buying a rigid template, you are buying an engine you can point at any question.

The highest-value builds tend to be the ones that combine sources nobody had time to join by hand. Blended CAC needs ad spend from five platforms next to closed revenue from the CRM. True ROAS needs Stripe revenue matched back to the campaign that drove it. Those cross-channel numbers are where the real decisions live, and they are exactly the ones a spreadsheet makes too painful to maintain.

• Weekly channel performance: spend, leads, and cost per result across every paid and organic source
• Monthly leadership deck: the board-ready view, generated and refreshed on a schedule
• Blended CAC and true ROAS: total acquisition cost and return, matched to real revenue
• SEO and content reporting: rankings, traffic, and conversions tied back to the pieces we publish
• Anomaly alerts: a Slack ping when spend spikes, a campaign craters, or conversions drop off a cliff

How we build it, and what you own at the end

We start by rebuilding your existing report, not inventing a new one. For the first few cycles the pipeline runs in parallel with your manual process and we reconcile every figure against your current numbers, because a reporting automation that is subtly wrong is worse than no automation at all. Once the numbers match to the decimal and you trust them, we cut the manual version and the pipeline becomes the source of truth. Then we tune it: which anomalies matter, what the summary should lead with, who gets which view.

You own the whole stack. The warehouse is in your cloud account, the dashboards are on your logins, and the automation logic is documented and handed over, so there is no lock-in and no black box. We build it, run it, and monitor it, which means a changed API or a rate limit is our problem to fix before Monday, not a surprise for your team. And because reporting sits downstream of everything else on the marketing line, it plugs straight into the rest of what we build: the same pipeline that reports on SEO traffic feeds our content and SEO automation, and the ad numbers it tracks are the ones our ad automation acts on, so measurement and action close into one loop.

Time saved, cost, and when a spreadsheet is still fine

The math is simple because the time cost is so visible. A team losing a day a week to reporting is losing forty-plus hours a quarter to copy-paste, and that is before the meetings spent arguing about whose number is right. A first reporting pipeline for a handful of core sources is usually live in two to four weeks: about a week to build and connect, then reconciliation against your real reports and a monitored rollout. Because we work outcome-first, you are paying for a pipeline that matches your numbers and runs itself, not for a proposal describing one.

It is not always the right first move, and we will tell you when it is not. If you report once a quarter, run a single channel, or have no consistent UTM tagging and no agreed definition of a conversion, automating first just automates the mess. In that case the fix is upstream: clean the tracking and agree the definitions, then automate the reporting on top of a foundation worth measuring. Automation multiplies whatever you feed it, so we would rather get your data trustworthy than help you ship the wrong number faster and more often.

• Live in two to four weeks for a first pipeline across your core channels
• A day a week of manual reporting handed back, per person doing it today
• Outcome-first pricing: you pay when the numbers match and the pipeline runs itself
• No lock-in: the warehouse, dashboards, and logic live in your accounts and are documented