Case study #4
How I Turn Data Into Marketing Analytics Reports
Project Summary: AI Marketing Analytics Report
The challenge
Marketing data sitting inside Google Analytics is only useful if someone can read it, interpret it, and act on it. Most businesses have the data but turning it into a clear, decision-ready report takes significant time and analytical skill.
The solution
A structured AI workflow using Claude for data analysis and Claude Design for presentation building, taking raw GA screenshots to a finished seven-slide executive report in under two hours.
Tools used
Claude Design, Claude
The Problem I was Solving
Producing monthly marketing reports is a common task that I’ve completed for many businesses. But here’s the challenge: pulling the data, identifying what actually matters, and presenting it in a way that a senior leader can absorb in two minutes is a time-consuming process. It requires analytical thinking, good judgement about which metrics tell the real story, and design skills to make the output look credible.
I wanted to show that AI can handle a significant part of that process. Not by replacing the analyst, but by compressing the time between raw data and finished report considerably.
My Approach
Step 1 — Gathering the data
I started by accessing the Google Analytics of the Google Merchandise Store account, a real e-commerce dataset that Google makes freely available for anyone wanting to practice working with live analytics data.
I captured eleven screenshots covering the key areas a marketing director would want to see: traffic acquisition, revenue by channel, e-commerce purchases, purchase and checkout journeys, device breakdown, geographic performance, audience segments, and paid search campaign results.
Step 2 — AI-powered analysis
I fed the screenshots into Claude and asked it to analyse the data as a senior marketing analyst would. Rather than summarising what the numbers said, I asked it to identify what the numbers meant — the risks, the opportunities, and the priorities. Claude identified five key insights including a significant mobile conversion gap, dangerous revenue concentration in a single channel, and a product mix imbalance where high-volume items were not the highest revenue generators.
Step 3 — Building the presentation in Claude Design
I briefed Claude Design with the five insights, the raw data, and a clear instruction to build a seven-slide executive presentation with a recommended action on every slide. Rather than producing a generic template, Claude Design produced a fully designed output with custom charts, consistent typography, and a clean visual hierarchy throughout. The result was a presentation that looked professionally designed rather than AI-generated.
Step 4 — Review and refinement
The first version was strong but not perfect. I reviewed every slide critically and identified where the framing needed sharpening for a non-technical audience. I then briefed Claude Design with specific revision instructions and it produced an updated version. That review step matters. Although AI shortens the time between data and finished report, human judgement is still what makes the output genuinely useful.
The Results
Here is the completed monthly report in presentation format:
The workflow produced a seven-slide executive presentation built directly from raw Google Analytics data. The finished report covers the five most important insights from the data, with a clear recommended action on every slide and a visual that makes each finding immediately obvious to a senior audience.
The five insights the report identified were:
Revenue is healthy but concentrated. $165k in March is a strong result, but 88.5% comes from the US and 45.7% from direct traffic. Either dependency represents a significant business risk.
Mobile is the biggest revenue opportunity. 64% of users arrive on mobile but generate less than 5% of revenue. Desktop users are 19 times more valuable per session. A mobile UX audit is the single highest-priority action.
Volume is not a reliable guide to revenue. The bestselling products by units are not the highest revenue generators. High-value items like the Chrome Dino Collectible Figurines generate 18 times more revenue per unit than sticker packs.
Product engagement is the funnel problem, not checkout. Only 16% of visitors view a product page, but 68.6% of those who reach checkout complete their purchase. The drop-off happens before the funnel begins.
Traffic concentration is a strategic risk. With nearly half of all revenue coming from a single channel, any disruption to direct traffic would have an immediate and significant impact on the business.
The full presentation is available to download below.
Download the report >>
What You Could Do With This
If you’re like many business owners and marketers who dread the first week of the month knowing a reporting cycle is looming, this workflow is worth exploring. A clear brief, a Claude analysis pass, and a Cowork presentation build can take you from raw data to a finished executive report in under an hour.
If you’d like help building a reporting workflow for your own business, get in touch here.
