Novantas helps financial institutions develop a deeper understanding of their customer needs and behaviors, and translate those insights into rapid and sustainable improvement in revenue, growth, credit quality, and profitability with proprietary tools, analytics, technology solutions, and scoring models.
Novantas is a leader in analytic advisory services and technology solutions exclusively for financial institutions.
Traditionally banks have offered unprofitable “honeymoon rates” to attract new deposits with the hope that the customer will keep their monies with the bank for many years to come to thereby recoup their investment based on customer inertia. Until now there has been little science applied to personalizing the price premium at an individual customer level.
Novantas has developed an analytical model that maps individuals’ personas by looking at shopping behavior, rate sensitivity, and retention period. This model, or more accurately series of models, enables banks to better predict which of their customers are likely to be profitable to attract for the bank’s deposit products, and which groups can be repriced in a manner (and at what rate) where the gains from additional rate overcome any incremental balance losses.
To conduct the customer journey analysis in real time, Novantas has built an application called MetricScape to run on top of a Cloudera-based Apache Hadoop and Apache Spark cluster that can crunch over a thousand business metrics per customer in sub-second response time. Processing speed in their pre-Cloudera relational database environment was 100 times slower.
Novantas is building out a hybrid architecture with Cloudera Enterprise at the center. The company used Cloudera Director to deploy and configure its production cluster on Amazon Web Services. Novantas also has plans to run on-premise Cloudera Enterprise clusters as well to support different customers’ requirements.
Novantas’ models, combined with Cloudera Enterprise’s and Apache Spark’s ability to store and process massive amounts of information at a low cost, have enabled banks to granularly target customers based on their responsiveness to price and the likely retained value from that offer.
The results have been outstanding at one major US bank with 10 million+ customers where this solution has been implemented, including:
Marketing execution costs reduced by 50 percent by focusing on high potential customers
Focus on incremental accounts reduced, lowering promo expense by as much as 60 percent and 12 month deposit growth by less than 25 percent
Retention offers limited, which reduced promotion expense by 10 percent and balance retention by only 3 percent with nominal change in customer retention
- Help clients develop a deeper understanding of their customer needs and behaviors, and translate those insights into rapid and sustainable improvement in revenue, growth, credit quality, and profitability
- Apache Hadoop Platform: Cloudera Enterprise, Data Hub Edition
- Cloud Platform: Amazon Web Services (AWS)
- Reduced marketing execution costs at one US bank by 50 percent
- Lowered promotional expense by 60 percent while maintaining 75 percent of deposit acquisition at one US bank
Big Data Scale
- Crunches over 1,000 business metrics such as rate sensitivity, behavioral propensity, lifetime value and product usage per customer in sub-second response time