Why age-old pricing strategies don’t have a place in modern banking
By Amit Dua, President and Global Head of Client Facing Groups at SunTec
The COVID-19 pandemic has prompted notable shifts in the banking industry as financial institutions grapple with significant challenges, from dipping interest rates to an increasingly competitive market, loan defaults and now even impending inflation. While some of these changes may be temporary, some will influence traditional banking patterns for years to come. This will in turn push banks to build new capabilities to adjust their operations to navigate the new normal and emerge from pandemic operations stronger than ever. Once the banks have exhausted the cost lever, what will really matter for banks (and importantly their incomes) during this phase is how they price their products and services as they transform into digitally powered organizations.
It’s critical that banks be accurate with their pricing strategies if they want to remain profitable – but age-old pricing strategies will not help banks get there. Instead, they must put emerging technologies such as artificial intelligence and machine learning to use to improve their pricing strategies. But how?
Mapping the past to drive the future
A bank’s success was traditionally measured on a limited range of capabilities, namely credit allocation, capital management and operations. Unlike today, there was historically very little difference between one bank’s product and what its competitors offered, and there was often no real priority given to customer needs. Today, banks are setting themselves up for failure if they don’t understand what their customers need. Banks of the future must better understand their customers and develop the skills to foster an emotional connection with them. But what has any of this got to do with pricing? Like customer preferences, prices can no longer remain static. Pricing must change to suit the evolving micro and macro environments.
Zeroing in on a pricing approach can be quite a challenge for banks. It’s usually factored around profitability, market share, the competitive landscape, consumer perception of price, revenues and profit projections – all of which are constantly changing. Banks are now required to adjust pricing quickly, or even course-correct on-the-go. But traditional pricing methods are not equipped to match this unprecedented pace and are also prone to human error. The good news is that there is a solution to this conundrum: technology.
Building-blocks: data-powered capabilities
Artificial intelligence (AI) continues to gain popularity in banking, and financial institutions have begun to use the technology to solve more complex challenges. In the early days, AI was restricted to customer-facing functions like chatbots and virtual assistants – in other words, conversational banking. But over the last several years, banks have been using AI to fine-tune middle office tasks such as fraud prevention, customer segmentation, KYC verification, credit underwriting and risk management.
The biggest value AI adds to any organization is its ability to deliver immediate actionable insights. We all understand the value technology delivers with its ability to analyze vast amounts of data in very little time particularly to the banking sector which has a great deal of customer data. AI can not only analyze the bank’s data but map this data against external factors – all in real-time – to help banks arrive at personalized and competitive pricing. In a way, this method fosters transparency into the variables behind each decision.
An AI-powered pricing platform will enable banks to closely monitor trends affecting customer behavior as well as the market, and then correlate these factors with competitive product prices. They can use the analysis to compare limitless pricing scenarios to devise more optimized pricing recommendations. The right pricing strategy will help banks manage their customers’ evolving expectations by prospecting, cross-selling, upselling and retaining customers.
As banks embrace digital, their top priority should be to make their customers’ lives easier and their experience seamless and enjoyable. Customer needs should be at the heart of every transformational plan. Alongside this, banks also have a responsibility to manage pricing, increase profitability, promote sustainability and counter dynamic challenges – one of which is the advent of hyper-personalization.
Even before the pandemic, customers were already getting used to brands treating them as individuals, as unique customers. Fintechs and big techs opened a new era of hyper-personalization – and banking customers want that same experience. They expect their banks to understand their needs and offer solutions accordingly.
As custodians of vast amounts of data, banks have a distinct advantage over fintechs, and AI can help unlock the true value of their data. AI platforms can analyze data across silos to understand customer behavior, usage of services and even willingness to pay for products and services. Banks can then calculate prices based on micro-segments and allow customers to compare prices in real-time. But before embarking on this journey, banks will first need to understand their business needs, map their pricing priorities and – most importantly – ensure maximum data security.
By scanning and analyzing data, banks can shift away from selling identical products (at the same price) to different customers and instead focus truly creating value for their customers. A focus on this type of hyper-personalization will help banks differentiate their brand, boost their revenues and improve financial inclusion.
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