Today’s customers rely on digital channels, including for banking and insurance. More and more customers are using online services to communicate with their financial services providers. This in turn has changed customer expectations: customers want offerings that are tailored to their needs, irrespective of when they contact their financial services provider and which channel they use. To predict the requirements of individual customers as accurately as possible, GFT has developed an innovative scoring approach.
Consequently, in today’s increasingly customer-driven communications environment, real-time marketing is taking on greater significance. When customers make contact, financial services providers must able to provide them with appropriate offers and services in real time over all communications channels. The key challenge here is predicting customer needs and behaviour as accurately as possible using scoring models.
Currently, customer scoring is usually based on expert knowledge fed into manually created predictive analytics models. While this approach has been widely used for decades, it is labour-intensive, time-consuming and expensive. The required experts are hard to find, and the software is highly complex and operationally intensive.
The benefits of cutting-edge customer scoring – improved efficiency, lower costs
In light of this, GFT has worked in partnership with software specialists to develop an approach that can provide automated support to address the issue of real-time digital communication. Intelligent software transparently integrated into the workflow handles the most important core tasks. The approach has been tested against various benchmarks in real customer situations. By reducing labour-intensive analytical processes, the system demonstrated considerable benefits in terms of cost and efficiency. In specific terms, GFT’s approach has the following advantages over traditional modelling in customer scoring:
Higher quality: Models and their output are optimised and specialised for the varying tasks of different departments, which in turn enables optimisation of the overall marketing portfolio.
Lower cost: Instead of implementing cost-intensive projects to enhance and optimise existing models, smaller work packages suffice to partly automate and recalibrate them. Additional data sources and influencing factors can also be integrated into the model more quickly and easily.
Competitive advantage and time to market: Partly automated, software-supported development, adaptation, expansion and optimisation shortens the time required for all the main BI and data-mining processes. This considerably reduces the burden on specialist departments and IT, allowing them to focus on their core tasks. Shorter time-to-market cycles allow faster processing of far more tasks as well as enabling the business to react more swiftly to current events such as new product launches or rising customer churn.
Looking to the future
Financial services providers who wish to go on serving customers and prospects efficiently over digital channels need to engage with real-time marketing in depth. In this respect, long-standing derivatives of forecasting models based on discussion and development cycles of several weeks or even months entirely fail to meet the requirements of Generation Y, let alone its successor Generation Z.
For more information on intelligent, self-teaching customer scoring from GFT, request our white paper on the subject here.