A dashboard is only worth trusting when it reads from approved metrics. We build the reporting and the automation on top of KPIs your team has already agreed.

Some of the 500+ brands we've worked with

See all references
  • BMW
  • Enerjisa
  • ETS Tur
  • QNB Finansfaktoring
  • Babylon
  • Desa
  • Akşam

Approved metrics and models become dashboards, automated reports, and narratives that people can use to make a decision.

Dashboards use approved KPIs. Measurement Strategy defines those KPIs.

Every engagement has named inputs, owners, human approval gates, reproducible QA, explicit limitations, and an operating handover.

We design executive dashboards and operational reporting tools that convert complex data into clear business signals. Zeo specialists build semantic data models, visual interfaces, and automated reports, while your stakeholders approve report layouts and KPI thresholds.

A user-centered dashboard development process from requirements gathering to automated delivery.
  1. Audit reporting requirements

    We interview executive and operational stakeholders to map core decision questions, report update cadences, and key performance indicators.
  2. Design semantic data layer

    We connect reporting tools directly to BigQuery data models or aggregated tables, preventing slow dashboard loading and query timeouts.
  3. Build Looker Studio dashboards

    We construct clean, brand-aligned reporting interfaces with intuitive date filters, drill-down capabilities, and clear visual hierarchy.
  4. Document & automate

    We configure scheduled email distributions, user access permissions, and maintenance guides for internal team ownership.

A client describing the planning and reporting problems we started from.

Dr. Kadir Kırmızı
Turna

Before working with Zeo, we had serious problems, especially with planning and measurement. Because we couldn't get complete measurements and reports, we struggled to evaluate our work. Evaluating the data correctly, in line with scientific rules, was the most important thing for us.

Dr. Kadir Kırmızı - General Manager

Collection, tagging, product analytics and reporting are separate problems with separate tools. These are the ones we build measurement on.

Core web analytics platforms

  • Google AnalyticsDuring the audit-reporting-requirements step, we check every metric a stakeholder wants on a dashboard against what GA4 actually tracks and how, since a requested number sometimes turns out to be undefined or inconsistently configured at the source. That check is what keeps the semantic data layer honest before a single chart gets built.

BI, dashboards & reporting

  • Looker StudioWe build the dashboards themselves in Looker Studio, the step this service names as build Looker Studio dashboards specifically, only after the semantic data layer underneath it is designed and agreed. That sequencing is the point: a dashboard built on unapproved metric definitions just automates the argument about what a number means.
  • Power BIWhen a client's organization already runs its reporting stack on Power BI, we design the same semantic data layer and build the dashboards there instead of migrating them to Looker Studio. The audit-and-design steps stay identical regardless of which platform the final build lands on.
  • TableauFor a client whose enterprise agreement already covers Tableau, we build the dashboards there instead of adding a second BI license. Which of the three platforms we build on is a client-fit decision made after the semantic layer design is already locked, not a change to that design step itself.
  • SupermetricsFor a dashboard pulling from several platforms at once, we route the feeds through Supermetrics into the semantic data layer rather than having the dashboard live-query each source directly. That pre-aggregation is what keeps a multi-source report loading quickly instead of timing out against a slow upstream API.
  • DataboxFor the document-and-automate step, we set up scheduled goal and threshold alerts in Databox so a stakeholder gets notified when an approved metric moves outside its expected range, rather than only discovering it at the next dashboard review. That is a narrower, alert-focused job than the full dashboard build itself.
  • DomoFor a client whose dashboard audience spans many departments with different access rights to the same underlying data, we build on Domo instead, since its governed permissioning handles that role-based split more directly than the other platforms in this group. That access-control need is what decides this platform choice, not the metrics themselves.
  • KlipfolioWhen the semantic data layer defines a metric that no platform's default connector calculates out of the box, a blended ratio spanning two data sources, we build the custom formula in Klipfolio directly against that definition. That flexibility is what keeps an unusual but genuinely agreed KPI representable at all.
  • WhatagraphFor engagements where the dashboard's audience is an external client rather than internal stakeholders, we automate the recurring white-labeled report through Whatagraph as part of the document-and-automate step. That client-facing framing is what separates this from Databox's more internal, threshold-alert-focused automation.

Attribution & e-commerce analytics

  • Windsor.aiWhen a required marketing data source is not among Supermetrics' supported connectors, we route it into the dashboard's data layer through Windsor.ai instead. Both tools do the same connector job in this service; which one handles a given feed depends on which source it needs to reach.
Share your data stack and tracking challenges. We will design a clean collection architecture and actionable reporting infrastructure.
Brief us