Difficulty predicting customers’ needs; disparate business systems
Delivered a Big Data powered customer data integration platform; built a customer propensity model infused with predictive analytics capabilities
Unified customer view; improved customer loyalty; augmented customer experiences; enhanced risk assessment capabilities
- In order to grow profitably and consistently, the bank’s systems must be connected. They should have the ability to effectively collect, aggregate, and analyze customer data in order to reduce overall costs and better serve customers. Our client was at the same crossroad. They were facing difficulty in gleaning customer data with respect to demography, buying patterns, identity, address, preferences, and more, hindering their ability to understand customer needs.
- Although our client was spending a lot of money to maintain its legacy systems, their systems were not agile enough to keep up with increasing market demands. The bank’s customer information was stored in multiple, disconnected data silos, crippling their efforts to obtain a 360-degree view of their customer.
- Our client’s lack of analytical capabilities made it difficult for them to analyze their customers’ needs and prevented valuable data insights from getting discovered. This resulted in customer attrition and redlined the client’s customer-centric initiatives.
With $273 billion in deposits, 8 million customers, and employees spread across 40 states, our client is one of the largest diversified financial services providers in the US. Founded in 1845 and headquartered in Pittsburgh, our client provides high-end retail, corporate, and institutional banking services and asset management solutions.
The bank collaborated with Kellton Tech to implement a Big Data platform which offered a unified view of every customer. The platform enabled them to successfully glean customer data and design customer-specific products and services. The introduction of the Big Data platform helped in seamless customer profiling and facilitated the tracking of customer behaviors, demands, and interactions, all of which are key factors in the delivery of personalized customer experiences.
Our solution consolidated the data from their complex landscape of legacy systems and databases onto a new scalable platform and integrated it with business analytics capabilities. The integrated data is then fed into the bank’s CRM solution, supplying the call center with more relevant leads. The solution also uses the analyzed data to make recommendations to the bank’s web team on ways to improve customer engagement on their website. We also infused deduplication capabilities that minimized data silos and helped gain real-time insights, enabling them to reorganize their business around customer needs. This resulted in an enhanced, personalized customer experience and a lead conversion rate increase of over 100%.
The platform used the datasets of over 8 million customers with over 200 variables to create a propensity model that predict the probability of the customer base to invest in various products and services, leading to increased cross-selling. The analytics team used current and historical data, tested over 50 hypotheses through a logistic regression propensity model to discover prospective customers, improve conversions, and predict customer needs. The bank witnessed a 10x increase in sales and a 200% growth in conversion rate over a two-month period.
- Increased cross-selling opportunities
- Reduced customer attrition
- Consolidated data environment to deliver real-time actionable customer insights
- Helped in contextual marketing
- Improved risk models