4.9
Dashboards that pull your store data and your ad-platform spend into one place, built and maintained by an analytics team specialized in eCommerce.
575+
eCommerce BI dashboards delivered
300+
Clients trust our analytics team
60+
Full-time analysts and data engineers
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Dashboards fail for reasons that have nothing to do with the charts. The numbers come from systems that were never reconciled, and every new question means another manual export.
Your store reports one revenue figure and the finance export reports another, so every meeting starts by arguing about which number to believe.
A dashboard built around whatever was easiest to connect gets opened twice in its first week and then abandoned by the team it was meant to serve.
When metric definitions are not modeled, answering a new question means an analyst rebuilding an export by hand, and the answer comes after the decision.
We begin with the decisions your teams make each week, then work back to the data those decisions require.
Book a consultation with our analytics team
The first stage produces a written list of the decisions your reporting has to support, and every later stage is measured against it.
We interview each team on the decisions they make weekly, then write down the metrics those decisions need and how each one is defined.
Sources are connected into a warehouse and the metric definitions are modeled once, so every downstream view counts the same way.
Views are built for each audience, including an executive summary and the operational detail a category manager needs daily.
Every figure is reconciled against the source system, and thresholds are set so anomalies surface before anyone reports on them.
Your team is trained to build and edit views, and the full model documentation is handed over as part of the build itself.
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Looker Studio, Tableau, and Power BI are the three we deliver most often, and the choice follows what your team already has licensed. The warehouse underneath is usually BigQuery, Snowflake, or Redshift. We recommend against introducing a new tool when an existing one covers the requirement.
Requirements and the data model usually take a few weeks, and a first working view often follows shortly after. Timelines scale with the number of source systems involved, which the first stage establishes before any build work starts.
Store platforms, ERP, POS, and the major ad platforms, along with offline sources that usually sit outside reporting. Where event tracking is unreliable, that is corrected first by our data collection setup team as a separate stage.
Every metric is reconciled against its source system during QA, and the definition of each one is written down and shared. Where two systems disagree we close the gap at source, so the same figure appears wherever it is used.
Both. The build gives your team the views, and our analysts can stay on to interpret what they show and recommend action. Ongoing analysis is scoped separately from the build, and plenty of clients take the build alone.
Send us the list of systems your numbers come out of today, plus what the team reports on each week. A specialist responds with a requirements review scope and its price.
Prefer to talk now? Book a call straight away, or email us at: [email protected]
A specialist walks through a live example and what it would take to build the same on your data.