Project Overview
A fast-scaling B2B SaaS company approached Cubet with a pressing challenge: while their CRM systems held a wealth of customer data, lead histories, campaign responses, and sales activity records, the insights were effectively trapped. The existing reporting mechanisms were fragmented, manual, and time-consuming. Teams were relying on filtered spreadsheets and assumed trends instead of data-backed decisions. Cubet was brought in to design and implement a robust Business Intelligence solution that could break down silos and bring clarity to complex data. The mission was clear—enable real-time insights across departments, turn raw CRM data into visual intelligence, and ensure each team, from sales to the C-suite, could align on performance and strategy using a shared reporting language.
Industry
B2B SaaS (Business-to-Business Software as a Service)
The Client
The client, a high-growth B2B SaaS company, was rapidly expanding and needed its internal systems to keep pace. With large volumes of customer and sales data stored in tools like HubSpot and Salesforce, they had all the information but none of the usability. Sales reps, marketers, and product teams were spending more time pulling and cleaning data than interpreting it. They needed visibility, agility, and simplicity—delivered through data.
Challenges Addressed
- CRM data was underutilized due to static and outdated reporting tools
- Departments worked in isolation, without a unified source of truth
- Time-consuming processes to extract meaningful insights from spreadsheets
- Sales forecasts were based on assumptions rather than real-time pipeline data
- Marketing lacked clear attribution for campaign ROI
- Product teams had limited visibility into feature usage or churn signals
- Leadership was relying on manually prepared decks for performance updates
Collaboration in Action
Cubet collaborated deeply with the client’s internal teams to design dashboards that weren't just visually compelling but functionally aligned with real-world business needs. By understanding how different departments consume and act on data, Cubet crafted a solution that could be used daily by team members at all levels. From the sales floor to the boardroom, the dashboards were tailored to how users work, think, and decide.
Every data point, filter, and KPI was selected to enable action—not just observation. Collaboration also extended to ensuring data security and compliance with strict access controls via Azure and Active Directory.
Technologies Deployed
Data Sources
- HubSpot and Salesforce were the primary CRM platforms providing sales and customer data
- Google Analytics and campaign platforms supported UTM tracking and user engagement metrics
- The client’s internal product usage database provided logs and activity data for user behavior insights
Data Pipeline
- Python-based ETL processes cleaned, enriched, and transformed raw data
- Apache Airflow scheduled data jobs to run at set intervals, ensuring freshness
- Snowflake acted as the central data warehouse, unifying disparate data streams into a single analytical repository
Visualization & Reporting
- Power BI served as the visualization layer, providing intuitive and interactive dashboards
- Custom DAX measures were created to handle funnel analysis, cohort tracking, and campaign attribution
- Power BI Row-Level Security (RLS) ensured that each team member saw only what was relevant to their role—Sales, Marketing, or Executives
Deployment
- The entire solution was deployed on Azure, with secure access governed by Active Directory
- Reports refreshed daily, with an override option for ad-hoc, real-time updates when immediate decisions were required
Innovative Feature
The standout innovation in this project was the delivery of fully interactive, role-based dashboards. These were not static reports—they were decision tools. Each report could be filtered by key dimensions such as time, region, product, team, and lead stage. This allowed users to toggle between a high-level executive overview and a detailed operational deep dive in seconds.Whether it was a sales manager tracking deal velocity in EMEA or a product lead exploring churn signals by feature set, the dashboards were built for context-rich exploration.
Value Delivered
- Sales teams gained immediate visibility into deal movements, pipeline velocity, and forecast accuracy.
- Marketing teams were able to link ad spend to actual conversions, optimising channel and campaign strategies.
- Product teams shifted from anecdotal feedback to behaviour-based insights, understanding which features drive engagement and retention.
- Leadership moved from retrospective slide decks to real-time dashboards reviewed live during strategy sessions.
The overall outcome was an ecosystem where every team operated with clarity and confidence, powered by unified and trusted data.
User Feedback
Feedback from across departments reflected a shift in how the organisation approached data. Time to insight, which previously took days, was reduced to mere minutes. Sales and marketing leaders appreciated the autonomy to explore their numbers without waiting on analysts. Product teams expressed increased confidence in their roadmap decisions, knowing they were backed by real usage data.Executive leadership praised the transformation as a step-change in organisational alignment and speed of decision-making.
Conclusion
What began as a reporting challenge turned into a strategic enabler. Cubet didn’t just add charts; it delivered clarity. By transforming CRM data into interactive intelligence, the company now operates in a faster, smarter, and more aligned way. Real-time insight became a shared asset—accessible to all, tailored to roles, and focused on action.
Impact Made
- Increased forecasting accuracy across sales teams
- Reduced marketing spend wastage through better attribution
- Product development guided by real-time usage and feedback loops
- Company-wide adoption of dashboards as central performance tools
- Significant reduction in data retrieval and reporting time
- Elevated collaboration between departments through a unified data language
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