Fivetran Case Studies Kuda Bank's Journey to Profitability through Data Visibility

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	Kuda Bank's Journey to Profitability through Data Visibility Fivetran

Kuda Bank's Journey to Profitability through Data Visibility

Fivetran

Technology Category	Analytics & Modeling - Predictive Analytics Infrastructure as a Service (IaaS) - Cloud Databases
Applicable Industries	Cement Finance & Insurance
Applicable Functions	Sales & Marketing
Use Cases	Behavior & Emotion Tracking Leakage & Flood Monitoring
Challenge	Kuda, a digital bank launched in Nigeria in 2019, experienced a fourfold increase in customers within six months. As a mobile-first digital company, Kuda recognized the need to be data-driven and identified the modern data stack as essential for achieving its goal. Initially, a five-person data team was manually building data pipelines and relied on SQL Server Reporting Services (SSRS) to extract insights from transactional databases. Their data was split into 12 disparate Azure SQL databases with no way to successfully join the data across their internal sources. The team was looking to move from running OLAP queries on an OLTP database, which was proving to be a challenging task. They needed a more scalable solution that would relieve them of having to build and manage data pipelines. Read More
About Customer	Kuda is a digital bank that was launched in Nigeria in 2019. It offers the country’s residents a simpler way to manage their personal finances through an app that includes tools for tracking expenditure and money management. The bank has been on a spectacular growth trajectory, with a fourfold increase in customers in just six months. As an agile, mobile-first digital company, Kuda recognized the need to be data-driven and identified the modern data stack as essential for achieving its goal. Read More
Solution	Kuda decided to use Fivetran for ELT, with dbt performing data transformations in BigQuery. Fivetran was chosen as the data pipeline, Google BigQuery as the data warehouse, dbt for data transformation, and Looker as the BI tool. The most valuable data came from Kuda’s customer-facing core banking transactional systems, which included metrics around customer acquisition, products, and service monitoring. Kuda also used advanced analytics for credit scoring, mitigating risk by making more informed overdraft offers to customers based on data. Data from Intercom, a customer conversation platform, was analyzed to help the support team allocate resources and plan for the type of questions they were most likely to be asked. The goal was to improve the customer experience and use insights to create better customer engagement models. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Predictive Analytics

Infrastructure as a Service (IaaS) - Cloud Databases

Applicable Industries

Cement

Finance & Insurance

Applicable Functions

Sales & Marketing

Use Cases

Behavior & Emotion Tracking

Leakage & Flood Monitoring

Challenge

Kuda, a digital bank launched in Nigeria in 2019, experienced a fourfold increase in customers within six months. As a mobile-first digital company, Kuda recognized the need to be data-driven and identified the modern data stack as essential for achieving its goal. Initially, a five-person data team was manually building data pipelines and relied on SQL Server Reporting Services (SSRS) to extract insights from transactional databases. Their data was split into 12 disparate Azure SQL databases with no way to successfully join the data across their internal sources. The team was looking to move from running OLAP queries on an OLTP database, which was proving to be a challenging task. They needed a more scalable solution that would relieve them of having to build and manage data pipelines.

About Customer

Kuda is a digital bank that was launched in Nigeria in 2019. It offers the country’s residents a simpler way to manage their personal finances through an app that includes tools for tracking expenditure and money management. The bank has been on a spectacular growth trajectory, with a fourfold increase in customers in just six months. As an agile, mobile-first digital company, Kuda recognized the need to be data-driven and identified the modern data stack as essential for achieving its goal.

Solution

Kuda decided to use Fivetran for ELT, with dbt performing data transformations in BigQuery. Fivetran was chosen as the data pipeline, Google BigQuery as the data warehouse, dbt for data transformation, and Looker as the BI tool. The most valuable data came from Kuda’s customer-facing core banking transactional systems, which included metrics around customer acquisition, products, and service monitoring. Kuda also used advanced analytics for credit scoring, mitigating risk by making more informed overdraft offers to customers based on data. Data from Intercom, a customer conversation platform, was analyzed to help the support team allocate resources and plan for the type of questions they were most likely to be asked. The goal was to improve the customer experience and use insights to create better customer engagement models.

Impact #1	Kuda's investment in a best-in-class data stack has more than met expectations. The company’s C-suite executives use dashboards to uncover wide-ranging insights, from customer acquisition to account activity. Sophisticated visualizations show when customers sign up, how they engage, and transactions categorized by merchants. Growth and marketing can see when prospects fall out of the sales funnel and when customers are eligible for an overdraft. Data made available also allows Kuda to satisfy its regulatory requirements, such as credit information that needs to be submitted to a national credit bureau on a regular basis. The bank is well advanced on its journey to being data-driven, with plans to move forward on more advanced analytics, specifically predictive and prescriptive analytics.

Impact #1

Kuda's investment in a best-in-class data stack has more than met expectations. The company’s C-suite executives use dashboards to uncover wide-ranging insights, from customer acquisition to account activity. Sophisticated visualizations show when customers sign up, how they engage, and transactions categorized by merchants. Growth and marketing can see when prospects fall out of the sales funnel and when customers are eligible for an overdraft. Data made available also allows Kuda to satisfy its regulatory requirements, such as credit information that needs to be submitted to a national credit bureau on a regular basis. The bank is well advanced on its journey to being data-driven, with plans to move forward on more advanced analytics, specifically predictive and prescriptive analytics.

Benefit #1	Fivetran replaced the work of 5 data engineers, allowing them to focus on other tasks.
Benefit #2	Kuda now benefits from valuable metrics including customer acquisition, products, and service monitoring.
Benefit #3	Company’s C-suite executives use dashboards daily for a wide range of insights including customer acquisition and account activity.

Benefit #1

Fivetran replaced the work of 5 data engineers, allowing them to focus on other tasks.

Benefit #2

Kuda now benefits from valuable metrics including customer acquisition, products, and service monitoring.

Benefit #3

Company’s C-suite executives use dashboards daily for a wide range of insights including customer acquisition and account activity.

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Overview

Kuda Bank's Journey to Profitability through Data Visibility

Operational Impact

Quantitative Benefit