Introduction: Defining the Challenge
Many high-growth SaaS brands—including this Leading Creative SaaS—face a startling reality: up to 80% of new users drop off within months. Despite a flood of top-of-funnel traffic, the true value of their product went unseen, resulting in a leaky funnel that threatened long-term growth.
As a data engineering agency and consulting partner, Daverse specializes in data strategy services, data engineering services, and data analytics services. In this use case, we’ll show how our data management consulting expertise and outsourced data engineering capabilities were instrumental in transforming this Creative SaaS’s funnel performance.
Defining Goals and Metrics
To address these challenges, we set out with two key objectives:
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Reduce Churn: Extend the time of active engagement of new users from 1-3 to 6 months, reducing the churn in 10%.
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Boost Conversion: Raise free-to-paid conversions from 2% to 10% among thousands freemium users.
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Drive ARR: Tie engagement metrics directly to net new ARR growth.
Success would be measured through:
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Retention Rate & Cohorts: Monthly active users tracked via well-defined data pipeline architecture and robust data modelling.
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Conversion Funnels: Usage patterns surfaced by our data analytics statistics revealed the most engaging functionalities that translates into the relevant offering at the right time.
Hitting these targets meant users gained deeper value from the product and received personalized experiences based on their behavior—driving stronger, more sustainable retention.
Exploring Solutions and Innovations
Through already in place tracking mechanisms in web products we can gather data from different sources that our data engineering services team can pull together and model building a cloud-first data pipeline.
Data Extraction & Pipeline Construction
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Ingest & Stitch: Integrated sign-up flows, usage logs, and campaign data using Airflow and Spark.
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ETL Automation: Automated transformation layers to enrich raw events—our outsource data engineering model ensured rapid deployment.
Note: We leveraged a centralised data lakehouse on Azure Databricks (already set up) to store and model all analytics data. To learn more about centralized data storage, see our deep dive on data warehouses here: Looking into Datawarehouses
Analytics & Feature Engineering
With raw data consolidated, we applied data analytics services to:
- Identify Drop-off Triggers: Correlation analysis surfaced usage traits most predictive of churn. With customer journey analysis and segmentation we answer key questions as to “Who” is doing “What”, identifying friction points.
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Predictive Modeling: Feature engineering powered models that suggested which freemium users would convert, to which offerings and the likelihood of this to happen using their usage patterns.
Operationalising Insights
- Data Management Consulting: We defined processes for ongoing monitoring, documentation, and versioned data models—ensuring best-practice data governance.
- Continuous Delivery: Databricks notebooks and CI/CD pipelines enabled updates to key stakeholders to monitor the progress on the new initiatives and strategic plans verifying the needle was moving in the right direction.
Assessing Impact and Insights
The new data initiatives triggered a new approach an key indicators to shape the monetisation strategy in the long term.
- Conversion Rate Uptick: Free-to-paid conversions climbed from 2% to 3%. Additionally, with our new line of cohort-analysis and monitoring in place, we pinpointed the drivers behind this growth and translated those insights into targeted product experiments and product and offering roadmap initiatives—optimising the customer journey along more profitable paths.
- Churn Reduction: By analysing peak engagement periods and the high-value actions of both converters and power users, we crafted tailored in-product experiences that further drove engagement—lifting sign-ups and rMAU (Repeated Monthly Active Usage) by 15% over three months.
Lessons Learned & Future Outlook
- Pipeline Architecture Drives Scale: A well-designed data pipeline architecture is the backbone of reliability and speed. Without well designed , explainable processes stakeholders will be reluctant to take action on any produced data, stalling the decision process and missing opportunitites to delays and uncertainty.
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Analytics + Strategy = Growth: Aligning what you build with its underlying purpose is the key to driving adoption and achieving real impact—this is “How to make impact through data 101”.
- Integration and Cohesion Matters: A centralised data storage (which was already given this time) lays the foundation, but aligning processes and people around data is what delivers real impact. Fostering trust, ownership, and accountability keeps teams moving quickly and confidently in an ever-changing environment.
