Data analytics you can act on, starting with tracking you can trust

Tracking, dashboards or prediction?

The order matters and it is almost always the same. Start with analytics implementation and GA4, which define what counts as a conversion, distinguish a newsletter signup from a tender enquiry, and make sure phone calls and form submissions are actually recorded. Google Tag Manager is the plumbing that lets marketing add and remove tags without a developer, and it needs governance or it becomes the reason your site got slow.

Once the data is trustworthy, dashboards make it usable. Looker Studio suits marketing reporting and is quick to stand up. Power BI suits organisations already using Microsoft 365 and needing to blend operational data from several systems with proper modelling behind it. Predictive analytics comes last, because forecasting churn or demand requires a couple of years of clean history. Building the forecast on broken tracking produces confident nonsense.

What an analytics engagement looks like

We begin with a measurement plan: a written list of the questions the business needs answered, then the events and properties required to answer each one. That document is short and it prevents the usual outcome, which is tracking everything and answering nothing. Implementation and validation for a typical business site takes 1 to 3 weeks. Dashboard work is another 1 to 2 weeks once the data is confirmed.

Validation is the part cheaper providers skip. We test every event against real interactions, reconcile totals against the source system, and check the numbers again a fortnight after launch when real traffic has exercised the edge cases. We also configure consent handling, because under the Australian Privacy Principles you need a defensible position on what you collect and on what basis, and retrofitting that after a complaint is unpleasant.

Most engagements in Australia start with GA4 and GTM setup, because nothing downstream survives bad tracking. From there the work moves into business intelligence: warehouse modelling first, then Power BI or Looker Studio reporting on top of it. If you already have a Power BI consultant, we are happy to hand them a clean model rather than replace them.

• A written measurement plan tied to decisions, not to available metrics
• Events validated against real interactions and reconciled to source systems
• Conversion values differentiated so optimisation chases the right outcome
• Consent configuration documented for your privacy obligations
• Dashboards built for the person who has to decide, with one screen per audience

The expensive mistake: a dashboard on data nobody trusts

An organisation commissions a reporting dashboard, it looks impressive, and within two months nobody opens it because the sales figure disagrees with the accounting system and no one can explain why. Trust is the whole product. Once a leadership team has caught a dashboard being wrong, it will never be used for a decision again, and rebuilding that confidence costs more than doing it properly first time.

The other failure is measuring what is easy instead of what matters. Sessions, impressions and bounce rate are cheap to collect and rarely change a decision. Cost per qualified enquiry by channel, drop off by checkout step and revenue by customer cohort do. If you cannot name the decision a metric would inform, it does not belong on the report. And if your reporting counts every conversion as equal, your marketing spend will be optimised toward whichever cheap action happens most often, which is usually not the one that pays your wages. Assign a value to each conversion type, even a rough one, and the reporting starts arguing for the right things.