Reports built by hand in sheets
Every week someone pulls ads, CRM and accounting into a spreadsheet. Figures diverge, and the report is stale before the meeting.
Data Analytics
One picture of the business, not sheets
We bring data from every system into dashboards of the key metrics. A leader sees the picture without manual reports.
Each team has its own numbers
Marketing, sales and finance calculate the same metrics differently, and meetings turn into arguments about the data.
Many dashboards, few decisions
Dozens of reports, but nobody knows which metrics to watch, and key signals get lost among the secondary ones.
A metrics system: definitions, formulas and owners
An audit of data sources and tracking quality
Data from ads, CRM, the website and accounting, in one place
Automatic refresh via Supermetrics, an API or BigQuery
Dashboards in Looker Studio or Power BI, by role
Alerts when key metrics move off course
Training the team to work with the dashboards
PROCESS
[ 01 ]free · 30 min
[ 02 ]14 business days
[ 03 ]first 30 days of work
[ 04 ]ongoing, monthly cycle
From request to result
[ 01 ]free · 30 minClarity Session
We review the decisions you make and which data is missing for them. You leave with the key metrics. No sales pitch.
[ 02 ]14 business daysMetrics and sources
A metrics system, plus an audit of sources and tracking. You get a dashboard plan and a list of data fixes.
[ 03 ]first 30 days of workDashboards
We set up consolidation and automatic refresh, build the dashboards and train the team.
[ 04 ]ongoing, monthly cycleSupport
We connect new sources, update dashboards for new jobs and watch data quality.
Frequently asked questions
and answers
How is Data Analytics different from Data Science?+
Data Analytics shows what is happening: metrics, trends and deviations, refreshed on a regular cycle. Data Science looks for causes and builds forecasts: why a metric changed and what comes next. Analytics is a daily management tool. A study answers the hard questions. Analytics is usually set up first.
Looker Studio or Power BI?+
It depends on your sources and your stack. Looker Studio fits marketing data from Google services and is free to start. Power BI is stronger for complex data models and suits companies on Microsoft products. We justify the choice at the metrics stage.
Why don't dashboard figures match the ad account?+
Because the systems count differently: attribution models, time zones, conversion windows and duplicate events. We fix which source is correct for each metric and explain the gaps in the dashboard notes, so the team stops arguing about the numbers.
Can we change the dashboards ourselves?+
Yes. We build them so your team can add filters and simple reports without us. Complex changes to the data model need an understanding of the structure, so we hand over the documentation.
THE FINAL STEP TOWARD A SYSTEMReady to turn growth
Ready to turn growth
into predictability?
Start with a free 30-minute Clarity Session.
