Jaja Finance unlocks smarter marketing with RudderStack + Snowflake

TL;DR

Jaja Finance, a UK digital lender, centralized behavioral data from iOS, Android, and web into Snowflake using RudderStack, creating a single, trusted source of truth for marketing and product analytics. With clean, governed events flowing in real time, Jaja improved the credit-card application journey, refined rewards engagement, and connected insights from its AI chat assistant back into product tuning. The stack includes RudderStack sources (Android, iOS, JavaScript, HTTP) and destinations (Snowflake, Amazon S3, Braze). “With RudderStack and Snowflake together, we’re far more agile and can make better decisions more quickly,” says Wayne Cavanough, CTO of Jaja Finance.

Company overview

Jaja Finance is a UK-based digital lender focused on simple, mobile-first credit experiences, including the Jaja Vanta credit card for UK residents. With a growing customer base and mobile-first approach, Jaja needed to deliver seamless onboarding and personalized engagement while modernizing its data infrastructure.

In early 2025, Wayne Cavanough moved from CIO into the role of Chief Technology Officer at Jaja, after holding senior technology posts since 2020, highlighting the company’s continued investment in engineering leadership and modern data capabilities.

The challenge: Fragmented data and delayed insights

Prior to modernizing, Jaja’s data lived across devices and platforms, obscuring the end-to-end customer journey. This fragmentation made it difficult to measure drop-offs, assess campaign effectiveness, and develop accurate personas, while legacy constraints slowed real-time decision-making.

For a digital lender competing in a fast-moving UK market, the inability to see and act on real-time customer signals limits marketing performance, application conversion, and customer satisfaction. Jaja set out to fix this with a cloud-first data foundation.

The solution: A unified, governed pipeline with RudderStack and Snowflake

Jaja standardized event collection from iOS, Android, web, and server-side sources through RudderStack, then delivered that data into Snowflake as a single source of truth. From there, it activated downstream tools and closed the loop with governance and quality at the source. The stack components called out publicly include:

• Sources: Android, iOS, JavaScript, HTTP
• Destinations: Snowflake, Amazon S3, Braze

RudderStack’s SDKs, transformations, and governance controls ensured events were clean, compliant, and available in real time, so teams could reliably build profiles, evaluate journeys, and power targeted engagement without black box intermediaries.

Why Snowflake + RudderStack for customer data infrastructure

• Data cloud-first architecture: Snowflake serves as the operational hub for customer data, enabling scalable storage and performant analytics. RudderStack streams trustworthy behavioral data into Snowflake to create a single source of truth.
• Open, developer-friendly delivery: RudderStack routes events to the warehouse and operational destinations (e.g., Braze, S3) while maintaining quality and control—an approach highlighted across RudderStack’s customer library and partner ecosystem.
• Operational optionality: With events centralized, Jaja can model personas, analyze funnel friction, and orchestrate timely messaging—without re-instrumenting each tool.

Implementation highlights (what changed in practice)

1) Clean, consistent collection across apps and web

By standardizing the schema for key events—application steps, identity milestones, rewards milestones—Jaja gained a common language for journey analytics and activation. RudderStack’s transformations and governance features helped normalize payloads and enforce consistency before delivery. 

2) Warehouse-centered “single source of truth”

Events land in Snowflake in near real time, where analysts and marketers can segment, profile, and build dashboards. Because all channels stream into the same tables, Jaja can compare iOS vs. Android vs. web patterns without stitching data post-hoc. 

3) Streamlined activation to downstream tools

With canonical data in Snowflake, Jaja can push the right segments into tools like Braze and maintain parity with warehouse logic. Amazon S3 provides additional flexibility for archival and data exchange needs. 

4) Feedback loop from AI chat assistant to product and marketing

As Jaja scaled its AI chat assistant (“Airi”)—built using Anthropic’s Claude 3 on Amazon Bedrock—the team measured frustration signals, resolution rates, and conversion-adjacent behaviors. Those signals flowed into Snowflake via RudderStack, where they informed funnel tuning and content improvements.

Business impact (what Jaja improved)

Application journey: Fewer drop-offs, faster completion

Capturing granular events across the application funnel helped Jaja identify friction and reduce drop-offs, while shortening the time required to complete key steps. The public case study also notes increased approval rates as a downstream effect of better data and journey clarity (directional claim, no specific percentage published).

Reward campaigns and lifecycle engagement

By tracking milestone achievements (e.g., direct debit setup, on-time payments), Jaja tailored rewards and messaging to the right segments, measured results in Snowflake, and adapted campaigns in near real time.

AI chat assistant insights → higher resolution and better CX

By correlating chatbot interactions with journey stages, the team spotted where customers struggled and fed those insights back to improve flows and knowledge bases. Jaja has publicly reported major response-time gains from its GenAI assistant—cutting response times by over 65%—which aligns with the broader effort to tune service with real, unified data.

Architecture snapshot (publicly referenced components)

• Event sources: iOS, Android, JavaScript/web, HTTP
• Core data cloud: Snowflake
• Operational destinations: Braze (marketing), Amazon S3 (storage/exchange)
• Governance & quality: RudderStack transformations and governance features to keep data clean and compliant in real time

This design lets Jaja maintain a consistent identity and event layer across channels, use Snowflake for analytics and audience logic, and deliver those audiences and events to engagement tools without losing fidelity.

How the unified pipeline improves day-to-day work

For product & growth teams

• Clear visibility into step-level drop-offs and intent signals
• Rapid testing of message timing and eligibility criteriaShared, documented definitions for “application started,” “application submitted,” and other critical events

For marketing & CRM

• Warehouse-defined segments synced to Braze for consistent targeting
• Faster iteration loops based on Snowflake-side KPIs
• Ability to design campaigns around milestone behaviors

For support & service

• Chat assistant telemetry visible in the warehouse alongside application and account events
• Better identification of repeated friction points and “hot paths” to escalation
• A clear measure of resolution outcomes and time to resolution, as publicly reported by Jaja for its GenAI assistant initiative

Results summary (directional, as publicly stated)

• Onboarding: Higher success rates by reducing friction in the card application journey (directional outcome listed in the customer story). 
• Marketing performance: More precise campaigns and personas, informed by governed, real-time events in Snowflake. 
• Self-service: Improved self-service conversion supported by AI assistant insights; Jaja’s public posts describe 90% reductions in response time as part of a broader CX program.

Why this matters for financial services in the UK

Credit issuers operate under exacting standards for security, privacy, and customer clarity. A cloud-first approach that keeps behavioral data accurate and explainable—while activating it in tools like Braze—enables:

• Auditability and consistency: A single warehouse truth that sales, marketing, product, and service can reference
• Faster iteration with guardrails: Transformations and governance at collection time to prevent downstream issues
• Customer clarity: The ability to tune journeys and service experiences with real signal, not guesswork

The bottom line

Jaja’s results point to a simple pattern: When clean behavioral data lands in Snowflake quickly and predictably, marketing, product, and service all move faster. By standardizing collection with RudderStack, enforcing governance at the source, and activating the warehouse as the single source of truth, Jaja turned scattered signals into repeatable growth levers: shorter application cycles, smarter campaigns, and a continuously improving self-service experience. That foundation is durable. As channels evolve and new tools enter the stack, Jaja can keep the data model stable, route events where they’re needed, and measure the impact in one place. This is what customer data infrastructure should do: collect, transform, and deliver trustworthy data so teams can focus on building better customer experiences.

If you’re ready to make that shift, book a demo to learn more

FAQs

What problem did Jaja Finance solve with RudderStack and Snowflake?

Jaja moved fragmented behavioral data from iOS, Android, web, and server-side into Snowflake to create a single source of truth. With governed, real-time events, the team improved the credit-card application journey, optimized rewards engagement, and connected AI chat insights back to product tuning.

Why choose RudderStack for Snowflake customer data infrastructure?

RudderStack is customer data infrastructure built for the data cloud. It streams trustworthy events into Snowflake, enforces tracking plans and schema consistency at the source, and lets teams deliver clean data to tools like Braze and Amazon S3 without black-box lock-in.

How does RudderStack improve data quality for financial services?

By enforcing tracking plans, schema validation, drift detection, and PII classification at collection time, RudderStack prevents bad data from propagating. That makes audits easier and keeps downstream analytics, modeling, and messaging reliable.

What data sources and destinations did Jaja use?

Sources: Android, iOS, JavaScript, and HTTP.
Destinations: Snowflake (single source of truth), Amazon S3 (storage/exchange), and Braze (marketing engagement).

How does Snowflake act as the single source of truth?

All channel events land in Snowflake in near real time. Analysts and marketers use the same governed tables for segmentation, journey analysis, dashboards, and reverse activation—eliminating one-off exports and inconsistent definitions.

What marketing outcomes did the unified pipeline enable?

Cleaner funnels with fewer drop-offs, faster application completion, and better rewards-based lifecycle campaigns. Teams could iterate quickly because definitions and KPIs lived in Snowflake, not scattered across tools.

How did AI assistant telemetry factor into the stack?

Chat assistant signals (e.g., frustration, resolution, conversion-adjacent actions) flowed through RudderStack into Snowflake. Jaja correlated those signals with journey stages to improve knowledge bases, content, and product flows.

Can RudderStack deliver real-time data to Braze while keeping Snowflake authoritative?

Yes. RudderStack streams events to Braze for timely engagement while keeping Snowflake as the system of record. Warehouse-defined segments can stay in parity with activation logic.

How does governance work in this design?

Tracking plans define event and property contracts. RudderStack enforces those contracts at the edge, normalizes payloads, flags drift, and classifies PII so Snowflake receives clean, compliant data.

What makes this architecture scalable for future channels?

Because contracts, transformations, and destinations are defined in code, Jaja can add new apps, events, or tools without re-instrumenting each system. The Snowflake model remains stable while destinations change.

Who benefits day to day?

Product and growth get step-level funnel clarity and faster tests. Marketing and CRM sync warehouse-defined segments to Braze with confidence. Support teams see assistant telemetry next to journey data to improve resolution.