A national ecommerce brand with data coming from every direction — Meta, Google, TikTok, Shopify, Klaviyo, CRM, attribution tools, inventory systems. Every platform had its own numbers, its own definitions, its own version of what was working. Marketing says CAC is $42. Finance says it’s $67. They’re both right — they’re just measuring different things with the same name.
Every Monday was the same ritual. Pull reports. Reconcile numbers. Debate discrepancies. Build slides. By the time the team agreed on what was happening, half the week was gone.
The head of marketing was spending 6–8 hours a week doing data reconciliation. Not strategy. Not creative direction. Not the work she was hired to do. She was stitching together spreadsheets from eight platforms, trying to get everyone to agree on what the numbers meant before they could talk about what to do about them.
She wasn’t the only one. The most experienced people across marketing, finance, and operations were all doing some version of the same thing — maintaining their own truth, defending their own numbers, and losing days to a process that shouldn’t require a human at all.
Decisions weren’t slow because the team was slow. They were slow because nobody trusted the data enough to act on it without double-checking. And by the time they’d double-checked, the window for action had passed.
Before we proposed anything, we embedded with the team for a week. Attended their standups, sat through their Monday planning sessions, watched how data moved — or didn’t move — through the organisation.
Instead of building “better reports,” we designed a decision pipeline: a unified data layer connecting all eight platforms into one structured source, automated metric reconciliation so “CAC” means the same thing everywhere, AI-generated weekly focus briefs that surface what matters before anyone opens a spreadsheet, and anomaly detection that flags problems before meetings — not during them.
The goal wasn’t to give the team more data. They had plenty. The goal was to give them less — less noise, less ambiguity, less time between signal and action.
The system worked. But the first sign wasn’t a metric — it was a meeting.
The first Monday after deployment, the team walked in and the focus brief was already waiting. No spreadsheet ritual. No “which number is right?” debate. The conversation went straight to “What do we change this week?”
That was the easy part. The harder part was trust. For the first two weeks, the head of marketing kept her old reconciliation spreadsheet open alongside the new system, cross-checking. That’s not resistance — that’s a rational response to years of unreliable data. She stopped checking around week three, when she realised the system was catching discrepancies she’d been missing manually.
The anomaly detection worked well for spend and CAC. It was less reliable for margin signals — the inventory data feeding in had its own consistency issues that we had to clean up before the alerts became trustworthy. The system is only as good as its inputs.
Campaign adjustments started happening mid-week — not as a post-mortem the following Monday. Spend decisions that used to take a full reporting cycle now happened in hours, backed by data the team trusted because they could see exactly where it came from.
The head of marketing got her week back. The Monday meeting halved. And for the first time, the entire team was looking at the same numbers, defined the same way, updated in real time. There was nothing left to argue about — only things to act on.
The team didn’t hire more analysts. They didn’t replace tools. They didn’t overhaul their tech stack. They removed the friction between data and action. That’s not a dashboard upgrade. That’s a different way of operating.
| A 65% reduction in weekly hours spent on data reconciliation and report building |
| Time from data to action on campaign changes went from days → hours |
| ~0 metric disputes — unified definitions meant the team stopped arguing about which number was right |
| 8+ platforms unified into one structured layer — Meta, Google, TikTok, Shopify, Klaviyo, CRM, and inventory |
| Monday meetings halved — from status reporting to strategic decision-making |
A national ecommerce brand’s marketing team was drowning in data from eight platforms that didn’t agree with each other. We built a unified data layer and decision pipeline that turned Monday’s spreadsheet ritual into a one-page focus brief. The team stopped arguing about numbers and started acting on them.