Cross-bank data set including customer accounts and transactions data from more than 30 institutions, spanning $2.9 trillion in deposits
Third party data
50 percent reduction in marketing cost execution for a large US bank
Up to $30 million in savings identified for a large US bank by identifying target customers for specific promotional rates
More than $15 million in new opportunities identified for every $1 billion in deposits at several large financial institutions
Improved customer targeting for profitable revenue growth
Big data scale
More than 1,000 business metrics per customer analyzed with sub-second response time
Novantas helps banks gain millions in profitable revenue growth with deeper insight into customer journey analytics.
Novantas is a leader in analytic advisory services and technology solutions for financial institutions, working with 80 percent of the largest global banks, payment networks, and wealth managers.
“We help our clients solve practical, pragmatic business problems, such as identifying pricing and prospecting opportunities that can improve customer acquisition,” said Hank Israel, director of Marketing, Propositions, and Products at Novantas. “As the market evolved, we had to expand the customer attributes we use and leverage AI [artificial intelligence] to find ways to bring raw data together and derive new insights.”
However, to take its analytics to the next level, Novantas had to modernize its data platform. Staff needed a platform capable of analyzing massive data sets—magnitudes larger than before—and a greater variety of data, including unstructured data such as audio from call center recordings and unstructured text in payments transactions data. For example, by using natural language processing (NLP) to analyze call center recordings, Novantas can gain insight into customer sentiment on products and promotions.
Many analyses had to be conducted in real time or near real time, and often required simultaneous evaluation of many periods. “We hit a roadblock with more data coming in, and could not use traditional systems,” said Kaushik Deka, director and CTO at Novantas. “They would become prohibitively expensive.”
Novantas built a self-service customer journey analytics solution, called MetricScape, on Cloudera’s modern data platform. The platform integrates customer accounts and transactions data from more than 30 institutions with third party data, and applies machine learning models to operationalize customer scores, such as customer potential value (CPV), for a variety of use cases, including offer optimization, customer retention targeting, and cross-sell and upsell activities.
More than 1,000 business metrics per customer can be analyzed with sub-second response time. “We can look at five years of data for six million customers and obtain insights in minutes,” said Israel.
Novantas deployed Cloudera both on Amazon Web Services and on prem, helping it cost-effectively manage variable workloads as needed. The organization was able to monetize its new solution in only six months, 18 months earlier than originally projected, thanks to Cloudera’s ease of administration and maintenance, and speed of provisioning.
While all machine learning models and metrics are ultimately stored in Novantas’ MetricScape solution, IT staff wanted to give their data scientists the flexibility to choose the tools they used to create the models. “Our data scientists use Cloudera Data Science Workbench as their core development environment,” said Deka. “They can use whatever language they want to develop metrics and models and plug in their own libraries into this secure environment. They can easily pull in all the metrics and models they need, and once the models are production-ready, they can then easily move them into MetricScape for use by clients.”
With MetricScape, powered by Cloudera, Novantas has helped its clients more precisely and profitably deploy pricing, marketing, sales, and retention initiatives.
“Often broad campaigns can result in banks paying incentives when they are either not required or where they will not result in long-term value, as in the case of promotion hoppers,” said Deka. “By helping our clients target a select group of customers for an offer, banks can significantly change their cost structure.”
For example, with the ability to more precisely target which customers to offer rate incentives, one large US bank reduced its promotional marketing spend by 50 percent to increase its profitability. And there are many more examples.
We've identified up to US$30 million in savings for one client by identifying targeted depositors.
-Hank Israel, Director, Marketing, Propositions, and Products, Novantas
“We’re also working with several large financial institutions to optimize pricing based on customer preferences for features and products. We’ve identified opportunities for these banks in excess of US$15 million for every US$1 billion in deposits, which is huge,” said Israel.