Data Architecture Redesign for a SaaS Platform — Single Source of Truth Across 6 Systems
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Case Study SaaS & Technology HubSpot

Data Architecture Redesign for a SaaS Platform — Single Source of Truth Across 6 Systems

A B2B SaaS platform with 2,200 customers had customer data spread across HubSpot, Stripe, their product database, Intercom, Zendesk, and a homegrown analytics platform. Revenue reporting took a week per month.

CRM data architecture SaaS data architecture HubSpot data architecture customer data platform SaaS

Project details

Client B2B SaaS Platform (2,200 customers)
Industry SaaS & Technology
Platform HubSpot
Duration 14 weeks
Churn rate reduced 18%
Key result
18%
Churn rate reduction
4 hrs
Monthly reporting time
6 systems
Connected
99.2%
Revenue data accuracy
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The situation

In their own words

"We stopped arguing about data and started making decisions. That sounds simple but it took three years and the right architecture partner to get there."

— Chief Operating Officer, B2B SaaS Platform (2,200 customers)

We had six systems that all thought they were the source of truth for customer data. HubSpot had the commercial history. Stripe had the billing data. Our product database had usage data. Intercom had support conversations. Nobody had the full picture and the weekly revenue call always started with fifteen minutes of arguing about whose numbers were right.

The challenge

What was going wrong

Each system held a piece of the customer picture with no reliable way to connect them. Customer health scores were guesswork because product usage and commercial data could not be joined. Churn was often detected only after cancellation because the warning signals in product data never reached the commercial team.

Common in SaaS & Technology: SaaS companies with customer data spread across multiple systems consistently struggle with churn detection and revenue accuracy. The solution is a data architecture that defines one authoritative source per data domain with integrations flowing downstream.

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Our approach

How we thought about it

We mapped every customer data point across all six systems and classified it by who owned it commercially, who needed to read it, and what the authoritative source should be. This produced a clear data ownership model: HubSpot as the commercial master, Stripe as the billing master, and the product database as the usage master — with integrations flowing downstream.

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The solution

What Celumai built

We designed and built a data architecture using HubSpot as the customer master, with bidirectional integrations to Stripe for billing events and the product database for usage signals. A customer health score combining commercial, billing, and usage data was built in HubSpot. Automated alerts flagged churn risk signals before they became cancellations.

"
"The churn reduction alone justified the project cost in six months. But the bigger benefit was giving the commercial team visibility they had never had."
VP
VP Customer Success
B2B SaaS Platform (2,200 customers)
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The results

What actually changed

Revenue reporting time reduced from one week to four hours. Churn rate decreased by 18% in the first year as the commercial team began acting on product usage signals. Monthly revenue accuracy improved to 99.2% as billing and CRM data were reconciled in real time.

18%
Churn rate reduction
4 hrs
Monthly reporting time
6 systems
Connected
99.2%
Revenue data accuracy

Is this familiar?

HubSpot challenges in SaaS & Technology — what we see most often

B2B SaaS companies almost always reach a point where customer data is too spread across systems to manage effectively. HubSpot holds commercial data. Stripe holds billing data. The product database holds usage data. Intercom or Zendesk holds support history. None of these systems talk to each other reliably.

The result is a revenue team that cannot answer basic questions about customer health, a CS team that discovers churn risk too late, and a monthly reporting process that consumes a week of senior time reconciling conflicting numbers.

Celumai designs customer data architectures for SaaS businesses that define clear ownership, authoritative sources, and real-time integration between commercial and operational systems.

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