Turn unexpected changes into decisions you can test.
Web and marketing analytics helps interpret change: what happened, which explanations the evidence supports and what your team should investigate next.
The report shows a change. What next?
drops, a channel brings more enquiries or adoption of a feature stalls. Before choosing an action, the comparison, definitions and data need to hold up. We investigate a specific question and show which conclusions the available evidence supports.
Connect analysis to the next action
The result is a reasoned recommendation with assumptions, measurement limits and a next evaluation step. Working with UX, , development or performance teams turns it into a deliverable task. Creative Engineering connects analysis to improvements in the actual .
Check the comparison before explaining it
We examine the period, event definitions and relevant changes to the website or campaigns. If CRM and web analytics count different enquiries, we first clarify the meaning of each metric. A precise calculation can still mislead when its definitions are inconsistent.
Illustrative example, not a client result: measured completion falls after a form redesign. Possible explanations include fewer completions, changed tracking or a different visitor mix. We examine these before treating the design as the cause.
An analysis assignment with usable deliverables
A bounded review provides a documented question, checked analyses, prioritised findings and recommendations for tests or corrections. Where the available evidence cannot answer the question, we identify the gap.
New source connections, tracking repairs and ongoing reporting are possible follow-on tasks with separate scope. Recurring analysis needs an agreed cadence, ownership and a process for changes in the underlying data.
Put attribution and predictions in context
An attribution model allocates recorded outcomes according to particular rules. It does not automatically establish what would have happened without an activity. Predictions also need suitable data and evaluation on data not used for training. We assess whether a method fits the question.
How we deliver
Set the question
Agree one decision and relevant metrics.
Check measurement
Review definitions, data quality and visible gaps.
Analyse the change
Use appropriate comparisons and examine alternative explanations.
Discuss findings
Explain conclusions, uncertainties and possible actions.
Plan the next step
Agree a test, correction or further measurement with the relevant team.
FAQ
Sources and technical context
- Google Analytics: Data freshness
Vendor documentation explains processing times and differences in available data freshness.
Which change can your team not yet explain?
Tell us the question, available systems and period. We will use these to define a focused analysis assignment.
Discuss an analysis