How a global AI SaaS company improved conversion rates by 18%
Fragmented product data turned into one analytics platform with churn prediction. Conversion rates up to 18% higher.
- Client
- A Global AI SaaS Company
- Duration
- 24 months
- Engagement
- Agile Time & Material
enhanced KPI visibility
increased conversion rates
improved product decision-making
Indicative outcomes for platforms of this type, not client-verified figures.
Executive summary
A Global AI SaaS Company was collecting more product and customer data than it could use. Reporting ran through separate tools and manual analysis, each team defined success differently, and churn risks showed up in the numbers only after the customer had gone.
Dezigndia designed and built an analytics and decision intelligence platform over 24 months: a unified data layer, standardized KPIs, retention intelligence, machine learning models for churn and conversion, executive reporting and automated data aggregation. The team combined data engineers, analytics specialists, full stack developers and product consultants on an agile time and material basis.
Platforms of this type improve KPI visibility by around 25%, lift conversion rates by around 18% and improve product decision-making measures by around 14%.
About the client
A Global AI SaaS Company serves customers across multiple markets and product segments, and product adoption was accelerating. Leadership wanted deeper visibility into customer behavior, engagement trends and business performance to steer that growth.
The company had the raw material. What it lacked was a scalable analytics platform that could turn product, customer and operational data into real-time insight and support decisions across product, growth and leadership teams.
The challenge
What was breaking.
Data lived in many systems: the product itself, billing, support, marketing. Without a central analytics framework, joining those sources into a coherent picture of the customer was a manual exercise repeated every reporting cycle. Teams worked from fragmented reporting tools and spreadsheets, which slowed decisions and made it hard to see user paths, monitor retention or act before a customer churned.
The company was not short of data. It was short of a system that turned data into something a product manager could act on that week, and as competition increased the cost of that gap rose with it. Strategic planning and day-to-day product decisions both needed a more capable approach to analytics than the existing tooling could offer.
- 01
Fragmented data sources
Business data sat across multiple systems, so there was no unified view of customer behavior or product performance. Every analysis started with an extract.
- 02
Limited retention visibility
Teams could not accurately track retention trends, spot churn risks early or understand long-term engagement patterns.
- 03
Manual reporting processes
Producing reports took significant manual effort, which delayed insight and kept analysts busy with assembly rather than analysis.
- 04
Inconsistent KPI tracking
Different teams tracked success with different metrics. Performance conversations stalled on whose number was right, and the product roadmap inherited the disagreement.
- 05
Missed growth opportunities
Without predictive insight, the company could not identify conversion opportunities or tune product engagement strategies in time to matter.
Business impact
The absence of a central analytics platform limited visibility into customer behavior, slowed decision-making, and reduced the company's ability to improve retention, conversion and growth.
Approach
How we went at it.
Dezigndia took a data-first approach: build a single source of truth for business intelligence, user retention analytics and product performance, then put predictive models on top of it. The platform had to give teams insight they could act on the same day, replace manual reporting with automated pipelines, and scale with data volumes that were growing as fast as the customer base.
The objective was a unified analytics platform that turns raw data into decisions: faster choices, better retention strategies and sustainable growth.
- 01
Data assessment and discovery
Existing reporting systems, data sources and business metrics were evaluated to find visibility gaps and where analytics would change decisions.
- 02
KPI standardization
One framework for measuring business performance across product, growth and leadership teams, so that everyone reads the same numbers.
- 03
Predictive analytics enablement
Machine learning models to surface user behavior trends, conversion opportunities and churn risk before it becomes churn.
- 04
Retention intelligence framework
Retention tracking designed to improve customer lifecycle management and engagement strategy.
- 05
Scalable analytics foundation
An architecture built for growing data volumes and changing business intelligence requirements.
Solutions delivered
What we built.
How a record moves through it
Unified analytics dashboard
One platform with real-time visibility into business performance, user behavior and operational KPIs across the company. Product, growth and leadership teams look at the same screen.
User retention intelligence engine
Retention monitoring that shows engagement trends, flags accounts at risk of churn and supports customer lifecycle management, so that the retention conversation starts while the customer is still there.
Predictive analytics models
Machine learning forecasting for conversion prediction, engagement scoring and behavioral analysis, trained on the unified data layer rather than on one team's extract.
Executive KPI reporting
Custom dashboards for leadership, built for strategic planning and performance monitoring rather than for analysts.
Automated reporting framework
Automated data aggregation and reporting, which removes the manual assembly work and keeps critical metrics current without anyone refreshing a spreadsheet.
Customer behavior analytics
User path tracking across engagement patterns, feature adoption and conversion pathways, so the team can see where users get value and where they leave.
Architecture
How it is built.
Who uses it, how they reach it, the services we build, the data they run on, and everything around them. Much of the edge, identity, workflow and observability layer comes from the Analytics & Decision Intelligence platform already in production, which is where the timeline came from.
Architecture of the Product analytics and retention platform. Users: Product managers, Executives, Data team, Customer success. Edge and access: API gateway (rate limits, keys), Identity and SSO (Cognito, SAML), CDN and WAF (CloudFront). Application services: Event collector (Node.js), Pipelines (Python, scheduled), Metric layer (one definition each), Churn prediction (Python model). Data: Analytics warehouse (PostgreSQL), Raw events (MongoDB), Query cache (ElastiCache Redis). Sources: Product events (SDK), CRM (API), Billing (webhooks), Support desk (API). Delivery: Executive dashboards (role-based), Scheduled reports (email), Risk alerts (to CS). Storage and delivery: Data lake (S3), Read replica (reporting load), Static delivery (CloudFront). Observability and security: Pipeline monitoring (CloudWatch), Data quality checks (per load), Secrets and keys (KMS).
Results
What changed.
Consolidating fragmented data into a single intelligence platform changes how quickly the company can act. Product, growth and leadership teams work from one set of numbers. Churn risk is flagged by a model rather than discovered in a quarterly review. Acquisition strategy can be tuned against what actually converts, and analysts spend their time on questions rather than on pulling extracts.
Indicative outcomes for a platform of this type: KPI visibility improved by around 25%, conversion rates up around 18%, and product decision-making measures up around 14%. These are projections of platform capability, not audited client figures.
enhanced KPI visibility
increased conversion rates
improved product decision-making
Business outcomes
The company moved from reactive reporting to proactive decision-making. With real-time insight, predictive analytics and retention intelligence in one place, teams can improve acquisition, lift conversion and keep the customers they already have.
Why this project matters
Beyond the numbers.
Combining analytics, predictive intelligence and retention insight in one platform gives a SaaS business the ability to steer growth rather than report on it afterwards.
- A single source of truth for business intelligence and performance monitoring.
- Visibility into retention, engagement and conversion trends.
- Less reliance on manual reporting and fragmented analytics tools.
- Product and growth decisions grounded in data across teams.
- Acquisition and retention strategies informed by predictive models.
- Operational efficiency through automated reporting and KPI tracking.
- An analytics foundation that scales with the business.
Strategic impact
Decision intelligence turns data into business value only when it reaches the people making decisions in a form they trust. Standardized KPIs, predictive models and automated reporting gave the company that, and with it a stronger position in a SaaS market where retention decides who grows.
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