Technology Category
- Application Infrastructure & Middleware - Middleware, SDKs & Libraries
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Buildings
- Cement
Applicable Functions
- Sales & Marketing
Use Cases
- Building Automation & Control
- Time Sensitive Networking
Services
- System Integration
About The Customer
Lendi is an Australian mortgage broker that has helped thousands of Australians secure their property dreams with more than $12 billion AUD in home loan settlements. The company's proprietary technology allows borrowers to search over 2,000 loan products from more than 40 lenders, delivering the best possible customer outcomes while driving competition and transparency in the market. The company's success is largely dependent on delivering the right experience to the right person at the right time on the right digital platform, which requires reliable, accurate insights into borrowers' needs and preferences.
The Challenge
Lendi, an Australian mortgage broker with over $12 billion AUD in home loan settlements, was facing a significant challenge in its data management. The company's proprietary technology allows borrowers to search over 2,000 loan products from more than 40 lenders, making it a competitive player in the market. However, the industry's competitiveness and the need to deliver the right experience to the right person at the right time on the right digital platform required reliable, accurate insights into borrowers' needs and preferences. The problem was that building an accurate profile of each customer required tapping into behavioral data on third-party engagement platforms such as Facebook, Google, and Bing. This data was readily available to Lendi, but the insights from each platform were siloed and didn't integrate easily. Even when the data could be brought into the same repository, the data structure was often inconsistent, creating the need to clean the data before it could be put to use.
The Solution
To overcome this challenge, Lendi's Data Architect, Daniel Deng, relied on Fivetran, a data integration service with an extensive library of hundreds of connectors to Software as a Service (SaaS) platforms. Fivetran automatically pulls data from Facebook Ads, Bing Ads, Google Adwords, Google Analytics, and other engagement platforms in near-real time, loading it into a data warehouse managed in the cloud by Snowflake. From there, Lendi marketers can pull the data into business analytics tools to measure and compare campaign success across platforms. Accountants, brokers, customer service representatives, and other business units can also pull in data for analysis. Setting up a new connector is easy and takes less than 30 minutes, compared to a week to several weeks if an engineer custom-builds a connector. This approach has helped Lendi realize significant time and cost savings.
Operational Impact
Quantitative Benefit
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