Aptologics
Insurance / SaaS

How Did an Insurance SaaS Company Turn CRM Data Into Actionable Business Insights?

This case study highlights how an Insurance SaaS company set out to gain deeper visibility into its marketing performance and operational metrics. With HubSpot as their core CRM, the client needed a solution to transform raw customer and campaign data into clear, actionable insights.

To meet these goals, we leveraged Databricks for scalable data processing, dbt for building a reliable transformation layer, and Power BI for intuitive visualizations. The result was a unified analytics framework that helped the business make decisions it could trust across deals, accounts, and marketing initiatives.

Key Results

80%
Reporting Time Reduction
From several hours to under 30 minutes
95% eliminated
Data Inconsistency Errors
Due to dbt tests and transformation logic
30%
Cost Savings
Achieved using open-source dbt-core over commercial solutions
High
User Adoption
Due to intuitive Power BI dashboards and tailored KPIs

The Challenge

The client's growing data needs required a more advanced, flexible analytics approach, but they were constrained by siloed systems and inconsistent reporting:

  • Lack of real-time visibility into deals and marketing campaigns
  • Data inconsistencies stemming from CRM (HubSpot) exports and transformations
  • No central reporting structure for accounts, marketing attribution, or sales funnel health
  • Manual workflows caused reporting delays and confusion among stakeholders
  • Cost concerns around existing tooling limited the scope of implementation

Our Solution

We implemented a modern and cost-efficient analytics solution using open-source and cloud-native tools:

  • Developed a custom PySpark-based extractor in Databricks to handle HubSpot edge cases
  • Leveraged Databricks' notebooks and scheduled jobs to create reliable daily syncs
  • Utilized dbt-core (open-source) for transformations, ensuring low-cost scalability
  • Applied Kimball dimensional modeling to align data with business logic
  • Created Power BI dashboards tailored to track marketing campaigns, sales pipeline, and account performance

Implementation Process

1

Data Extraction

2 weeks

Built a custom PySpark pipeline in Databricks to extract and sync HubSpot data daily

2

Data Warehousing

1 week

Configured Databricks Data Warehouse with appropriate storage and compute layers

3

Transformation Layer

3 weeks

Used dbt-core to clean, model, and test the data with a Kimball-style dimensional structure

4

Business Logic & Metrics

2 weeks

Defined and implemented key metrics such as lead conversion, campaign ROI, and deal progression

5

Dashboarding

3 weeks

Developed interactive Power BI dashboards for marketing, sales, and operations teams

6

Testing & Deployment

2 weeks

Tested visualizations with stakeholders and deployed into production environment with access controls

Technologies Used

Databricks
PySpark
dbt-core
Power BI
HubSpot
"The clarity and confidence our business teams now have is incredible. From campaign tracking to sales performance, we finally have a unified view of what’s working and where to focus. And it’s all done efficiently without bloated tooling costs."
Rachel Mendez
Director of Business Insights
Esteemed SaaS Company for Insurance Brokers & Carriers

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