Analytics Review
Review available behavioural data to identify patterns, drop-offs and questions that deserve deeper investigation — without mistaking dashboards for full explanations.
Analytics review
Analytics review interprets existing usage and behavioural data to surface patterns and sharper research questions.
Funnels, paths, retention curves and event streams show where people go and where they stop. Careful interpretation distinguishes signal from tracking gaps, seasonality and product quirks.
UX Pacific pairs quantitative review with the product and research context so teams leave with prioritised investigation areas, not only charts. Findings often feed interviews, usability tests or measurement redesign.
What this method reveals
Where journeys drop off or loop unexpectedly- Segments or devices with divergent behaviour
- Events that correlate with success or failure
- Questions that need qualitative or task-based follow-up
How we run it
- Data access scoping and metric definition review
- Structured exploration of funnels, paths and cohorts
- Validation of tracking assumptions with stakeholders
- Insight briefs that propose next research or design moves
When to research
When analytics review is useful
Use analytics review when behavioural data already exists and you need to focus limited research capacity, diagnose a sudden change or prepare hypotheses for testing. It cannot alone explain motivation or prove that a redesign will work.
The right participants
Populations defined by the data you hold
Analysis covers the users or customers represented in the available datasets — by product, market, channel or time window. We state coverage and known blind spots, including people who never appear in digital events.
Our approach
How we run analytics review research
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01
Frame the decision
Agree the business questions, time windows and journeys that matter most.
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02
Map the data landscape
Inventory events, definitions, known gaps and ownership of metrics.
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03
Explore systematically
Examine funnels, paths, cohorts and anomalies against the agreed questions.
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04
Stress-test interpretations
Check alternative explanations such as tracking changes, seasonality or release effects.
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05
Synthesise priorities
Rank patterns by impact and readiness for deeper research or design response.
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06
Translate findings into action
Recommend follow-up methods, instrumentation fixes and near-term investigation paths.
Interpretation
Behavioural data needs a careful reading
We separate what the data shows from what it might mean. Correlation is not cause, and missing events can look like abandonment. Assumptions are documented so teams can challenge them.
Research in context
Product, channel and market lenses
Reviews account for release history, campaign spikes and market differences. Where data is siloed across platforms, we note how that limits end-to-end claims.
Research ethics
Responsible use of behavioural data
Access follows organisational permissions and purpose limitation. We avoid re-identifying individuals unnecessarily and treat sensitive journey data with the agreed safeguards.
The output
What your team can receive
Outputs may include annotated funnel views, priority opportunity lists, instrumentation recommendations and a research agenda linking patterns to methods that can explain them. Charts come with clear caveats.
Clear boundaries
Interpreting analytics evidence
Analytics describe observed digital behaviour within the tracked population and definitions used. They do not capture offline steps, untracked tools or motivations. We recommend complementary methods when decisions require explanation, preference or controlled task performance.
More in quantitative research
Surveys
Structured questions that help measure patterns across larger audiences.
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Concept & Preference Testing
Compare reactions to concepts, messages and design directions before build.
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Experience Measurement
Define and track experience measures with context, not vanity scores.
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Benchmarking Studies
Compare agreed tasks and measures across experiences or over time.
Explore
Let us help shape the next step
Read the patterns in the data you already have.
Decide what deserves deeper research.
Share the product, metrics and questions your analytics review should sharpen.