Our client is a fast-growing pet supplies brand based in the US. They sell everything from food and toys to grooming kits through their online store and retail partners. As the business scaled, they had set up multiple dashboards across departments. But they weren’t helping. The data looked fancy but didn’t actually guide decisions. Teams were overwhelmed or ignoring the reports altogether.
They came to us to clean things up and build dashboards that gave their team real clarity.
Task in Hand
The client didn’t need more reports. They needed the right ones. Our job was to clean up the mess, remove distractions, and build dashboards that actually helped their team take action. Everything had to be simple, focused, and built around how they worked day to day.
Approach
We kept things simple and focused on what the client actually needed.
1. Dashboard Audit
We went through every existing report and dashboard they were using. Most had duplicate metrics, outdated logic, or too much noise. We flagged what to keep and what to drop.
2. Aligning Metrics with Business Goals
We worked with their marketing and operations team to understand what decisions they made regularly. Then we built dashboards around those needs, not just generic KPIs.
3. Simplifying Views for Each Team
Instead of one-size-fits-all dashboards, we designed role-specific views. The marketing team got campaign-focused reports. Operations saw order trends, inventory, and delays.
4. Automating Data Updates
We connected their Shopify, Meta Ads, and Google Analytics with Looker Studio so reports always showed fresh data. No manual pulls or waiting on spreadsheets.
Results
Clarity Across Teams
Each team now had a clear view of their metrics and what actions to take. No more second-guessing or digging through raw data.
Faster Decision-Making
With simplified dashboards, daily decisions around campaigns, stock, and spend became quicker and more confident.
Time Saved Every Week
By automating data updates, the team saved hours every week that were earlier spent on pulling and cleaning data manually.
