The company generated substantial data through its platform — user interactions, feature engagement, conversion events, and support signals — but lacked the analytical architecture to transform that data into actionable product and commercial intelligence. Metrics existed in fragmented systems, reporting was manual and delayed, and the product team was making decisions based on intuition rather than evidence.
The commercial consequence was material: without understanding which user behaviours predicted conversion or churn, the product team could not prioritize development investments effectively, and the commercial team could not identify and act on the customers most at risk of attrition before they left.
“The transformation required establishing a product intelligence operating model — one that unified data architecture, automated KPI governance, and embedded analytical thinking into the product and commercial decision-making process.”
Product analytics, commercial metrics, and customer success data existed in separate systems — preventing a unified view of the customer lifecycle, product performance, or the relationship between product behaviour and commercial outcomes.
Key performance indicators were tracked inconsistently, reported with delay, and defined differently across product, commercial, and customer success functions — creating misalignment and impeding evidence-based decision making.
Churn was identified retrospectively — after customers had already decided to leave. Without a predictive retention framework, the customer success team had no mechanism to intervene before churn became inevitable.
Significant product team time was absorbed by manual data extraction, report production, and stakeholder updates — capacity that was unavailable for product development, customer analysis, or commercial strategy.
Evaluate readiness across people, process, technology, data, and AI.
Prioritize initiatives aligned to business goals and measurable outcomes.
Convert assessment insights into a structured execution plan.
Map the platforms, AI solutions, and products required for success.
Enable enterprise-wide adoption, optimization, and long-term value realization.
Five product intelligence capabilities operationalized across product, commercial, and customer success functions — transforming data into actionable business intelligence.
Each capability was scoped, governed, and operationalised independently — ensuring value was delivered before the next layer was introduced. Combined, they form an integrated partner intelligence system.
A consolidated analytics foundation integrating product engagement, commercial, and customer success data — providing the first unified view of the customer lifecycle and the relationship between product behaviour and commercial outcomes.
A unified KPI framework with automated tracking, consistent definitions across functions, and real-time performance visibility — eliminating manual reporting overhead and enabling faster, more consistent performance management.
A behavioural analytics capability identifying early signals of customer churn risk — enabling customer success teams to intervene proactively rather than reactively, at the point where intervention has the highest probability of success.
An analytical capability identifying the user behaviours, product moments, and engagement patterns most predictive of conversion — enabling product and commercial teams to optimize the customer journey with evidence rather than assumption.
An automated intelligence layer giving product and commercial leadership a real-time view of product performance, commercial metrics, and customer health — replacing manual reporting with continuous, governed intelligence.
Achieved through unified KPI architecture and automated tracking — giving product and commercial leadership consistent, real-time performance intelligence for the first time.
Behavioural intelligence and targeted intervention in the conversion journey — enabled by the conversion analytics capability — drove material improvement in trial-to-paid conversion.
Measured through decision cycle time reduction and outcome tracking — reflecting the shift from intuition-based to evidence-based product decision-making across the product team.
Automated KPI tracking and reporting infrastructure eliminated the manual data extraction and report production that had previously absorbed significant product and analytics team capacity.
“We had always known we were making decisions without the intelligence we needed. Dezaris built us the capability to change that — and the impact showed up immediately in our commercial metrics.”
Most analytics platform implementations produce dashboards without producing intelligence — because the underlying data architecture is fragmented, KPI definitions are inconsistent across functions, and there is no governance model to ensure analytical outputs drive decisions rather than simply informing them.
Accountability & decision rights established first
Workflow redesign before platform selection
Platform operationalises a ready organisation
Dezaris brought SaaS product intelligence expertise — including user behaviour analytics, conversion funnel design, and retention model architecture — that went beyond standard business intelligence implementation.
Establishing consistent KPI definitions and governance across product, commercial, and customer success functions requires organizational design as much as data architecture — a dimension most analytics implementations overlook.
Building a retention intelligence capability that customer success teams can act on requires designing intervention workflows, escalation thresholds, and success metrics alongside the predictive model — not just the model itself.
