Advanced Analytics

How Can Analytics Deepen Banks’ Retail Personalization?

Artificial intelligence (AI) and machine learning (ML) have become critical disruptors in almost every industry, including banking and finance. Today, the banking sector is moving from a product-centric to a customer-centric model. The introduction of analytics has pushed financial institutions and banks to reevaluate their traditional systems and make necessary changes to stay relevant in the market. So, how does retail personalization help banks win their customers? This article at McKinsey & Company explains why financial institutions and banks must set up ecosystems to use analytics well.

Retail Personalization: What Do Studies Reveal?

  • Only 8% of banks successfully apply predictive insights from their ML models, despite significant technological investment.
  • Close to 16% of banks have standard protocols for algorithm development.
  • Organizations that have codified, unified, and centralized key analytics have witnessed 5 to 15% higher revenue from their campaigns.

What Are the Defects Hidden in Retail Personalization?

Often banks claim that they already have analytical tools to drive retail personalization. However, without effective mechanisms in place, banks may end up focusing on only retaining customers. Experts believe that mainly focusing on retaining customers may not be a helpful goal because indiscriminate retention hurts a bank’s economy. Furthermore, it entails spending money on loss-making customers or focusing on customers not susceptible to intervention.

How to Deploy Advanced Analytics

Retail personalization needs more than advanced analytics. “It needs an integrated infrastructure with precise mission alignment around high-priority opportunities, use cases that cover the customer value lifecycle, an asset library equipped with ready-to-deploy case code, and a uniform set of practices to guide teaming and execution,” says the author.

Take Stock of Customers’ True Value

Business leaders must use analytics to measure large customer groups by age and deposit volume. They must also assess variables that tend to be counterproductive and disengage customers. Additionally, leaders must utilize advanced analytics to develop a more granular view of target customer groups anchored in their value.

Automate Forecasts

The author says ready-to-deploy algorithms and automated decisions enable banks to effortlessly test new ideas and offer refined outputs.

Understand What Causes Customer Churn

Explainable AI reveals the significance of different variables on an individual’s likelihood to change banks. Based on these factors, frontline teams can be equipped with the right tools to offer the best actions, such as customized offers, sales materials, and other services. Furthermore, banks can consistently improve their services based on new data and experience.

To read the original article, click on

Related Articles

Back to top button