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Transforming Customer Support with IoT: A Freshworks Case Study
Technology Category
- Analytics & Modeling - Machine Learning
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Education
- Equipment & Machinery
Applicable Functions
- Maintenance
- Sales & Marketing
Use Cases
- Construction Management
- Predictive Maintenance
Services
- Data Science Services
- Training
The Challenge
Freshworks, a provider of CRM and customer experience solutions, was facing challenges in improving the performance of its customer support organization due to a legacy Hadoop infrastructure and an assortment of data tools. With over 60,000 enterprise customers and multiple product lines, the company was struggling to maintain exceptional customer satisfaction due to the high volume and level of support required. The manual approach to managing help desk tickets was not sufficient to keep up with the demand. The company's internal enterprise data platform, powered by Hadoop, was composed of multiple data and analytics tools, which incurred massive IT overhead to manage upgrades and monitor performance. This environment created performance bottlenecks as data volumes increased, slowing down the customer support team’s ability to efficiently service customers.
About The Customer
Freshworks is a technology and software company that provides a wide range of CRM and customer experience solutions. Its mission is to help its clients delight their employees across various job functions, including IT, customer service, sales, marketing, and HR. With over 60,000 enterprise customers and multiple product lines, Freshworks generates a large volume of data from its customers, which it uses to optimize processes and accelerate time to resolution. The company was previously using a legacy Hadoop infrastructure and an assortment of data tools, which were causing performance bottlenecks and slowing down customer service.
The Solution
To tackle these challenges, Freshworks migrated to the Databricks Lakehouse, a platform that offered flexibility, multicloud support, and a unified approach to data, analytics, and AI. The migration involved moving 500+ TB of data and 40+ data sources and tools across multiple clouds, a process that was completed within seven months. The company worked with Databricks to identify dependencies and patterns, which allowed them to map out a migration plan across data sources, integrations, and endpoints. The Databricks Lakehouse replaced the Hadoop-based platform, enabling Freshworks to democratize data access and improve operational analytics. The company also used Delta Lake to ensure data reliability and consistency across all layers of data as it built pipelines to support analytical and machine learning workloads. With MLflow, the data science team was able to streamline the machine learning lifecycle from training and experimentation to versioning and deployment.
Operational Impact
Quantitative Benefit
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