One of the major challenges facing financial institutions is customer attrition, or churn. Studies suggest it costs five to seven times more to attract new customers than it does to retain current customers. Studies have also shown that companies are more likely to retain customers who engage frequently with a product or service.
In India, the landscape is further complicated as the number of mobile internet users was estimated to reach around 420 million by 2017, and financial institutions are undertaking the mammoth job of becoming both omnichannel and paperless while encouraging thousands of customers to utilize digital channels as their primary means of engagement.
In our latest webcast which aired May 22, 2018, Anirudh Shah, Founder & CEO, 3LOQ Labs discusses how 3LOQ is solving the problem of customer churn with open source big data and machine learning technology. 3LOQ addresses this challenge by deploying proprietary machine learning algorithms to analyze billions of data points and map out dynamic feature recommendations to reinforce repeated usage of a product. The end results? 3LOQ’s Habitual AI platform reduces churn while building loyalty and customer engagement with digital banking services available through online and mobile platforms.
3LOQ Labs recently partnered with a leading Indian banking institution to increase adoption of their digital channels. The project yielded impressive results for the client, including a:
- 45% reduction in customer churn
- 145% increase in digital banking transactions
- 75% increase in users who made four or more transactions per month
One of the key tech tools that contributes to 3LOQ’s success is the completely free, open source HPCC Systems big data platform. Anirudh shares how 3LOQ Labs leverages this platform to:
- Analyze four terabytes of data combined with built-in analytics libraries to create personalized recommendations
- Utilize efficient coding in an implicitly parallel platform that allows prototypes to be developed and iterated quickly
- Enable horizontal scaling on commodity hardware, with the flexibility to deploy both on premises and in the cloud
Links to the webcast and other related information and sites are listed below: