How businesses rely on artificial intelligence for customer engagement

Over the past few years, banks have faced many new issues, such as rising consumer expectations, competition from digital competitors with superior customer experiences, technological advancements, and tightening regulations . Many banks have begun digitizing to address these challenges and stay relevant, but as traffic has started flowing to digital channels, banks have downsized their branch networks to save costs.

After the outbreak of the new crown epidemic, digital transformation has further accelerated. This is especially true for traditional brick-and-mortar banks, whose cost-to-income ratio is twice that of digital banks.

We are now in a recession, which is forcing everyone to tighten their belts. Spending is declining and the current situation is likely to continue for some time. The economic downturn has also affected most banks, with many having to cut hiring spending and, in many cases, further layoffs. While this layoff may have eased some of the economic pressure (the Financial Conduct Authority estimates that closing a bank branch can save £590,000 a year), banks still need resources to stay connected with customers to ensure they can retain them and attract new customers.

One solution is to use data and artificial intelligence to gain customer knowledge and build user stickiness. Machine learning (ML) can be used to analyze data to provide banks with insights into contextual experiences, or conversational artificial intelligence to enable “human-like” interactions with customers. NatWest's virtual assistant Cora shows how this can be achieved by helping thousands of customers affected by the pandemic reschedule their loans and credit card payments.

Understanding your customers is the key to better engagement. Artificial intelligence and analytics solutions enable this by providing insights to help customers manage their finances and recommending next steps for them. However, to provide the most effective engagement, comprehensive data is needed. Banks should use a platform to collect information on customers’ other business relationships and locations to be able to understand customer preferences and predict future needs. For example, a collection of data based on customers' income and spending patterns, age, financial assets and liabilities would allow banks to recommend investments in specific companies or to reduce spending on frequent dining out.

Another benefit of using data analytics is that it prevents banks from annoying customers with services and offers they don’t need. Instead, it allows banks to provide the right services to the right people at the right time. This reduces the chance of customers going elsewhere to meet their financial needs.

The examples mentioned earlier could allow banks to achieve high levels of engagement, effectively using AI to deliver highly personalized, contextual experiences and recommendations that improve their customers’ financial well-being.

However, while providing customers with this data can help them manage their finances more effectively, allowing banks to retain them, research report Maximizing Digital Banking Engagement shows that only 5% of banks Successfully creates effective customer engagement. Most do so simply by focusing on temporary fixes for customer experience rather than long-term plans.

The technology required for data collection and analysis can also present challenges. The cost can be high and the results depend on the data used. Therefore, banks should make their choice based on their location, taking into account factors such as business objectives, customer base and technology readiness.

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