Data analytics services

User behaviour tends to leave a digital trail that we can piece together to form valuable insights. Page views, navigation paths, search queries, form completions, and drop-offs all tell a story if you know where to look. We use data analytics to map these metrics into real, actionable findings.

For example, we might discover that a high number of users are exiting a key page without taking action, or that a specific feature is rarely used. Rather than relying on assumptions or feedback from a vocal minority, we can assess what’s really happening across your entire user base.

Access to these data points allow us to make better informed, evidence-based decisions. This leads to better overall outcomes, smarter use of resources, and clearer alignment between user behaviour and product strategy.

Every analytics engagement begins by defining what we’re trying to understand. This is shaped by product goals, current challenges, or questions that have come out of earlier design work.

From there, we can explore a range of behavioural signals to identify patterns and surface issues. This often includes:

• How users move through key journeys, such as sign-up, onboarding or purchase
• Where and when users drop off, abandon tasks, or backtrack
• Which content or features receive high engagement or are being overlooked
• What devices, screen sizes or traffic sources users are arriving from
• Differences in behaviour between user segments, locations, or cohorts
• Repeat usage and engagement over time to track retention

These patterns help us build a baseline understanding of how the product is being used right now. Once we understand that baseline, we can identify where change is needed, and later, measure the impact of changes made.

We start by defining the questions the data needs to answer. These might be about conversion, engagement, drop-off points, or feature usage. Once the questions are clear, we move into identifying available data sources, extracting relevant data, and cleaning it for analysis.

Depending on the maturity of the product and analytics setup, this may involve:

• Reviewing existing tools such as Google Analytics, GA4, Mixpanel or Hotjar
• Auditing event tracking, funnels, or goal completions
• Mapping real user journeys and comparing them to intended flows
• Filtering and segmenting data to reveal hidden trends or outliers
• Consolidating insights into a format that’s easy to interpret and act on

Where necessary, we’ll also recommend changes to how data is captured. A product’s analytics setup often evolves over time, so we ensure that tracking is aligned with the questions stakeholders care about most.

The goal of our analytics work isn’t just to produce dashboards or charts. It’s to support decision-making.

We summarise findings into concise, actionable insights that guide product direction. These may include:

• Prioritised problem areas in the user experience that need attention
• Features that are underused or misunderstood
• Opportunities to simplify journeys and remove friction
• Evidence to support or challenge existing design assumptions
• Recommendations for what to track or test next

We aim to make findings clear and relevant for both design and business teams, not just data specialists. In some cases, we visualise key findings through annotated flows or journey maps to clearly link behaviour back to real-world usage.

Whilst data analytics is effective on its own, it often sits alongside other UX methods, especially in later-stage or live product work. While early research helps shape what to build, analytics helps track how well it’s working once it’s in users’ hands.

This is why it’s particularly valuable in mature products where incremental improvement is the goal, and where large user bases make it difficult to rely solely on qualitative feedback.