Combining Big Data and Machine Learning For Precision-Targeted Marketing
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Combining Big Data and Machine Learning For Precision-Targeted Marketing

By CIOReview | Wednesday, May 2, 2018

Every financial organization has increased its focus on big data analytics to maintain the edge in this era of information-based competition. On the other hand, face-to-face interactions with customers via traditional branch networks are declining in a time when understanding the customers has become an imperative.

Hyper-Personalised Experience

Organizations can leverage big data analytics and enable a “Hyper Personalised Experience” on a large scale. New technologies can now seamlessly capture, analyze, and scale customer's banking information, and predict cross-selling opportunities owing to the massive investments in the CRM over the last ten years within the banking sector. In order to take this into effect, customer information footprint needs to expand from internal transaction systems to data from the online world such as social media and mobile devices to gain a 720-degree view of the customer.  

Analyzing Ever-Increasing Data Volumes

Legacy systems can no longer be used with increased volumes of data as they require analyzing and processing a greater variety of data including unstructured text and call recordings. Today, companies can utilize natural language processing (NLP) to gain insights into their customer sentiment on products and promotions.

Building an analytics solution for self-service customer journey

Solution providers are building a self-service customer journey analytics solution that can integrate with various customer accounts and transactions data across multiple systems with third-party data. These solutions can apply machine learning models to operationalize customer potential value (CPV) and other customer scores for a variety of use cases including cross-sell and upsell activities, customer retention targeting, and system and process optimizations.

Precise Deployment of Initiatives

Latest technological developments have allowed solutions to provide a higher level of precision-targeted pricing, which allows organizations to reach different groups of customers. The target audience can be divided into groups of people for whom price will drive incremental volume, and for those the persistence of the deposit and utilization of the credit will eventually yield substantial profits over the estimated lifetime of the balance (including the incremental cost of the incentive).

Such solutions have uncovered new opportunities for the banking sector for customer acquisition through digitally refined targeting to find customers with similar customer profiles. This has effectively reduced a bank's reliance on broad campaigns that often consume substantial time and monetary investments.