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Supercharge Customer Onboarding with Event-Driven Microservices and Confluent Cloud

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In today's rapidly evolving digital age, organizations across various sectors are leveraging technology to provide great customer experiences. One of the most important touchpoints in this journey is the 'Customer Onboarding' process. Often perceived as cumbersome and time-consuming, customer onboarding presents a prime opportunity for digital innovation.

In this blog, we will follow the journey of a leading financial institution that prides itself on its customer-centric approach. With an ever-growing customer base of tech-savvy users, they turned to Confluent Cloud to reinvent and streamline their onboarding process. Here’s how they tackled key challenges, the decisions they made, and the outcomes they achieved.

Business Challenges: Overcoming Hurdles to Seamless Onboarding  

Navigating the modern landscape of customer expectations, this organization faced numerous business challenges that needed to be addressed in order to deliver a digital platform that streamlines the customer onboarding process:

  • Heightened Customer Expectations: With the digital revolution reshaping consumer behaviors, customers now expect instant and seamless onboarding processes. Delays or cumbersome steps led to lost business, as customers would abandon the process halfway or opt for competitors.

  • Regulatory Compliance: Stringent regulations around data protection and identity verification, especially in sectors like finance, add layers of complexity. Ensuring compliance without compromising on speed became a challenge.

  • Operational Inefficiencies: Manual processes and legacy systems resulted in longer processing times, higher costs, and a greater likelihood of human error. This not only impeded the onboarding speed but also affected the operational bottom line.

  • Data Silos: Multiple departments often held pieces of the onboarding process ‚ÄĒ from sales to verification to support. Lack of a unified view meant that teams operated in silos, leading to disjointed customer experiences and inefficiencies.

  • Scalability Concerns: With an expanding customer base and the associated growth in data, the existing infrastructure struggled to handle peak loads, especially during promotional periods or product launches.

  • Security & Fraud: Ensuring the security of customer data during onboarding, while also keeping an eye out for potential fraudulent activities, became a tightrope walk. Traditional systems were not adept at real-time fraud detection, leaving the organization vulnerable.

  • Delayed Feedback Loops: With the absence of real-time analytics, gauging customer drop-offs during onboarding or understanding areas of friction was challenging. This hindered the organization's ability to iteratively improve the process.

Technical Challenges: The Need for Digital Transformation

As this organization tried to align their processes with the ever-evolving business landscape, the limitations of their existing technical framework became evident. Beneath the surface of business challenges lay a web of technical hurdles, each intricately tied to the other, demanding not just attention but a holistic reevaluation:

  • Legacy Systems: Outdated IT infrastructures were not only slow but also lacked the flexibility to integrate with newer, digital tools. This hampered automation and real-time data processing.

  • Data Fragmentation: Customer data was scattered across various systems ‚ÄĒ from CRMs to transaction databases. This fragmentation made a consolidated view of customer information a challenge, impacting decision-making and verification processes.

  • Lack of Real-time Processing: Traditional systems operated in batch modes, leading to delays. This was particularly problematic for steps like instant identity verification or credit checks, essential for rapid onboarding.

  • Scalability Issues: As the customer base grew, the existing IT setup struggled to handle the increased volume of onboarding requests, especially during peak times.

  • Integration Challenges: With multiple third-party systems, like credit check agencies or digital ID verification services, smooth integration became a significant pain point.

  • Inadequate Security Protocols: While the business demanded faster onboarding, the existing systems didn't have robust enough security measures for quick yet secure data access and storage.

  • Limited Analytical Capabilities: The absence of advanced analytical tools meant that insights into drop-offs, bottlenecks, or customer behavior during the onboarding process were lacking, making optimization difficult.

  • Manual Processes: A significant portion of the onboarding steps, especially verifications and approvals, were manual, leading to inconsistencies and inefficiencies.

  • System Downtime & Reliability: Frequent system outages or maintenance breaks further added to the onboarding delays, creating a negative impression on customers.¬†¬†

Business Outcomes: Confluent Adoption Leads to Real-World Benefits

Given the business and technical considerations, the organization recognized the paramount importance of real-time data processing. With customer onboarding being a multi-step process that demands instant feedback and swift transitions between stages, data streaming emerged as the obvious choice. It not only offered real-time data handling but also catered to the need for scalability, reliability, and efficient data integration across multiple systems.

While evaluating different solutions to find the right fit, one of the technologies that came up was RabbitMQ. While it satisfies the need for a point-to-point messaging system, its traditional messaging paradigm lacks the native support for high-volume data streaming and event sourcing that Apache Kafka¬ģ offers. Kafka's distributed nature inherently supports real-time data processing, offers storage for event replayability, and scales to manage massive data influxes. Additionally, Kafka's rich ecosystem ensures seamless integration across varied data platforms. For the organization‚Äôs dynamic, real-time customer onboarding system, Kafka's capabilities were more comprehensively aligned with their complex requirements, making it the preferred choice.

The adoption of data streaming with Confluent Cloud brought about a transformative business impact:

  • Reduced TCO (Total Cost of Ownership): Fully-managed Kafka, streamlined processes, and reduced dependency on multiple systems led to cost savings in maintenance and operations.

  • Improved ROI: Faster customer onboarding led to quicker customer acquisition and increased revenue.

  • Faster Time-to-Market: With the ability to process data in real time, new features or changes in the onboarding process could be implemented and rolled out more swiftly.

  • Enhanced Customer Experiences: Real-time feedback and quicker onboarding processes led to more satisfied customers, reducing drop-offs.

Solution Implementation and Architecture Deep Dive

At the heart of the financial services organization’s modernized customer onboarding system is Confluent Cloud, ensuring real-time event streaming and processing. This dynamic infrastructure leverages Kafka as its backbone, interconnecting a series of specialized microservices. Here's a snapshot of their orchestrated flow:

  • User Registration Microservice: Manages the foundational step of customer sign-up. Upon a successful registration, it broadcasts a "New Registration" event into Confluent Cloud.

  • Identity Verification Microservice: Actively listens on Confluent Cloud for "New Registration" events. It processes and validates user credentials and identifies details. Depending on the verification outcome, it dispatches either an "Identity Verified" or "Identity Rejected" event back to Confluent Cloud.

  • Credit Check Microservice: Tuned to consume the "Identity Verified" events from Confluent Cloud. It performs a comprehensive credit score assessment for the customer. Upon assessment completion, it produces either a "Credit Approved" or "Credit Rejected" event into the cloud.¬†

  • Account Creation Microservice: Waits for the green signal in the form of a "Credit Approved" event from Confluent Cloud. It initiates and finalizes the customer account creation procedure. It also pushes an "Account Created" event into the cloud.¬†

  • Welcome Kit Microservice: Listens for the "Account Created" event on Confluent Cloud. It crafts and dispatches a digital welcome package tailored for the customer. Commemorates this gesture by introducing a "Welcome Kit Sent" event to the cloud.

In concert with these specialized services:

  • Notification Microservice: This omnipresent service is the sentinel of Confluent Cloud, perpetually attentive to a myriad of events. Whether it's a new registration, a credit approval, or a welcome kit dispatch, this service ensures that the customer remains informed in real time through tailored notifications.

  • Customer Service Microservice: Constantly tuned into Confluent Cloud, this service acts as a guardian, ensuring smooth onboarding. Upon detecting any adverse events like "Identity Rejected" or "Credit Rejected," it prompts the customer in real time to take the necessary corrective actions, ensuring a seamless experience.

This streaming architecture with Confluent Cloud offers an agile, responsive, and customer-centric onboarding process. With Kafka at the core, each microservice functions autonomously while still being harmoniously synchronized, demonstrating the prowess of event-driven architectures.

In the culmination of the onboarding journey, the organization also implemented a real-time data synchronization process. Seamlessly integrated with Confluent Cloud, this process ensures that once the customer is onboarded, their comprehensive data is methodically dispatched downstream. The company’s Salesforce CRM receives the latest enriched profile, enhancing sales and support strategies. Simultaneously, a centralized PostgreSQL database is updated, cementing the foundation for transactional operations. Additionally, the data is streamed to a Snowflake data warehouse, where it is used for analytical models and insights, driving agile decision-making and developing a deep understanding of the customer journey.

This organization decisively embraced Confluent's comprehensive suite of offerings to elevate its onboarding process. At the core of their system lies fully managed Kafka, which serves as the robust backbone for event streaming, ensuring scalability, reliability, and real-time data processing. 

Complementing this is the fully managed Schema Registry, which acts as the guardian of their data structure. This registry ensures that the evolving schemas of their messages maintain compatibility, thus safeguarding the integrity and consistency of the data being exchanged between microservices.

Recognizing the need for seamless data flow, the company also utilizes Confluent’s fully managed connectors. These connectors simplify the task of integrating diverse data sources and sinks, allowing for effortless data ingestion from, and dissemination to, various platforms without the hassle of custom integrations.

Lastly, for more intricate data processing tasks, the organization’s teams have harnessed the power of streaming processing tools such as Confluent's ksqlDB. This event streaming database empowers them to run continuous, interactive SQL-style queries on their Kafka streams. It facilitates real-time analytics, data enrichment, and complex event-driven applications, ensuring that the customer onboarding experience is not just smooth, but also informed by instantaneous insights.

What’s Next

In summary, by leveraging Confluent's cutting-edge features, the leading financial institution has constructed a sophisticated, efficient, and adaptable onboarding architecture that's ready to meet the demands of the modern digital era.

Confluent Cloud serves as the central nervous system in this organization’s digital ecosystem, bridging diverse platforms and supporting the addition of new ones. Their Java microservices, vital for onboarding operations, both produce and consume data in real time. Salesforce, their CRM, interfaces seamlessly with Confluent Cloud for instant data updates, while their relational data is housed in AWS RDS with PostgreSQL. Furthermore, all analytical needs are catered to by Snowflake, which through Confluent, ingests real-time data for deep insights. This tight-knit integration delivers streamlined operations and data-driven decisions for the business, which continues to see greater user growth and revenue along with lower total cost of ownership.  

Looking ahead in their transformative journey, this organization will continue to rely on Confluent Cloud for innovating their customer onboarding. By interlinking intricate microservices, streamlining data flow, and ensuring real-time responses, Confluent Cloud has been instrumental in enhancing user experiences. In the ever-evolving digital era, the synergy between data streaming platforms like Confluent Cloud and visionary enterprises across all industries will unlock new real-time use cases, delivering seamless and enriched interactions at each touchpoint to delight every customer. 

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  • Shashwat is Senior Solutions Engineer at Confluent. With previous experience at Twilio, Cambridge Semantics, EY, and Deloitte, he has strong skills in Data Science, Java, Leadership, Microsoft Excel, and Data Analysis. He has an engineering background and an MS in Business Analytics focused in Data Science from Bentley University - McCallum Graduate School of Business.

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