Analyze Your Customers’ Behavior To Predict What They Want

Digital transformation is a top trend in banking, and most banks realize that “going digital” means more than meets the eye. It is not a marketing strategy but a need for change in the way employees perceive and interact with customers. The power of digital goes beyond  investing in digital channels or tools to streamline processes. In a world of AI, Big Data, and machine learning, financial institutions that don’t adapt will most likely get left behind. In banking, predicting what customers want can only be done right after a careful analysis of customer behavior. 

It’s no use to offer several of the most helpful services or products on the market if you can’t hold, convert and retain customers to track the proper metrics.  One of the best ways to increase ROI is to understand user behavior, so that you can spot and fix loopholes within the conversion funnel. According to the Boston Consulting Group, “the starting point for each financial institution will depend on its business strategy, market position and capabilities,” [...] But all must consider how they can reshape their distribution models, improve their value propositions and develop end-to-end consumer-centric journeys to increase growth and customer satisfaction.”

In this article our main focus will be on steps to take to predict customer behavior in the banking sector. We will tackle the best strategies banks should implement to meet customer expectations and increase ROI. 

It all begins with a customer behavior analysis 

Omnipresent mobile technology, secure electronic banking, and a large volume of financial information readily available at a click of a button has led to the materialization of a diverse audience of banking consumers, each with different needs and wants. What’s stopping financial institutions from making sensible decisions? The answer is:  data-driven information. According to an Accenture study performed on 33,000 customers across 18 markets: “customers expect digital innovation, meaning that banks should  transform their value proposition to meet distinct, emerging customer needs.” 

Very few industries enjoy access to rich customer data sources like banking. The information a bank has on a customer reveals - and even predicts - useful information about behavior. Furthermore, the information gathered can be presented in a data-driven form to different departments (e.g. product development, marketing, HR, customer service), thus helping them improve their decision-making process. Just as compelling is the promise of using customer data to increase revenue. 

A customer behavior analysis can have an even wider scope. It maximizes customer profitability while improving retention via actionable intelligence to target the right customer, through the right channel, at the best time, with the best offer. 

As an example, banks can leverage the power of social media analytics to present targeted offers/services to targeted customers based on recent life events (e.g. new job, marriage, divorce). Keyhole is a great tool as it excels at helping marketing departments get inside customers’ minds via optimized posting, competitor strategies, audience growth, automated account KPI reporting, and more. 

IBM’s Watson Customer Insights for Banking has proven incredibly useful for a large global bank that implemented a 360-degree strategy for viewing all customers across all channels, and studying behavior in real time. “IBM Watson Customer Insight for Banking observes how each customer reacts to each campaign, and uses machine learning to refine its algorithms. We have identified approximately CHF 2.5 billion [USD 2.56 billion] of untapped sales potential—for example, customers who currently do not hold a credit card with us, but would benefit from doing so. So, if we can convey these messages to customers at the right time and in the right way, the potential for revenue growth is huge.”

Bottom line is, a detailed customer behavior analysis provides valuable insights into the various variables that have an influence of your targeted audience. It gives an overall perspective of the main priorities, motives and decision-making methods that are being taken into account throughout the customer journey, helping you understand how customers feel your bank. In the long term, such an analysis can be used to improve the customer connections, update CRO, and ultimately, increase sales. 

AI (artificial intelligence) to the rescue! 

Financial institutions of all shapes and sizes have started looking into the inherent potential of artificial intelligence (AI), particularly when seeking to deliver improved, fully-customized, customer-oriented services. However, the beauty of AI goes beyond traditional computing. Extracting valuable information from the data bulk is just as important. A great example of a bank that recently implemented an AI-based system to better analyze and predict customer behavior is The Bank of America. 

Launched in 2018, Bank of America’s virtual assistant Erica chatbot seeks to shape the future of retail banking with its AI-based, customer-oriented features. Available on mobile, users can communicate via text, voice, or tap; a “tri-modal” approach capable of assisting customers with functions such as: finding past transactions, locking/unlocking debit cards, schedule payments, make money transfers, and a lot more. According to bank officials, “At the time of launch, Erica could understand 200,000 variations of questions from customers. The capacity of the bot increased to 500,000 variations in less than 1 year. Out of the bank’s 27 million customers, 7 million are already using Erica”. 

Most bank interactions are based on context, and many have difficulties building up an A to Z profile of their ideal customer. A lot of them feel misunderstood, or worse, they feel ignored; and that happens because data is not managed effectively when implementing advanced technologies. Bain & Company mentioned in a recent article how a large European bank adopted Taiger’s advanced tech stack for client onboarding - which combines NLP processing with machine learning to “automatically identify, extract, and validate information from different types of documents”. The end result was an 85% drop in costs and a turnaround decrease in time from weeks to minutes without losing quality. 

Redefining operating models 

Consumers constantly seek to get the best of both worlds: benefit from human interaction for complex concerns and reap the benefits of a digital experience that provides them convenience and speed. According to the Boston Consulting Group, banks that choose to refine operating models, turning them into hybrid approaches, can expect a revenue and customer satisfaction increase of 15%, and a decrease in overall costs of 35%. 

“Infusing a digital mindset into a traditional banking culture can be challenging and the need to manage two cultures during the transition can exacerbate the situation. Success depends on engaged senior leadership that is committed to radically changing the bank (Boston Consulting Group)”. 

Digital transformation is not tied to advanced technologies only. It is about putting technology to good use by supporting customers in their financial journey,  making their lives easier. For example, mobile technology has had quite an impact in the recent years, and it is because customers want to conduct finances on their own; across multiple channels, benefitting from both digital and physical perks. The omnichannel approach keeps branches alive in a highly-digital world because the human factor remains key to retaining and delighting customers.  

Although redefining operating models centers around what customers need, it also relates to the way banks operate internally. The digitization journey of every bank out there will be unique; with challenges and tailored solutions for every institution. In an age of instant approvals and one-click online ordering, the average consumer has become intolerant of non-transparent processes that take weeks to finalize. It all begins with understanding your customers and predicting what they want via innovative technology like AI, machine learning and predictive analytics that can help replace the old legacy-based infrastructure with a modern, data-centric approach. End-to-end process optimization is just as important as it helps keep costs under control while reshaping the customer journey; as well as streamline processes via automation which adds agility and speed, eliminating waste. 

Conclusion 

Customer expectations are ruled by giants like Google, Apple, and Amazon; meaning that corporate banks should rethink and reframe processes from the point of view of the client to deliver outstanding digital experiences across the customer journey. In the simplest terms, customers should be at the core of all internal decisions. Banks must leverage the data they already have to enable seamless experiences; breaking down the barriers between ATMs, branches, mobile apps, and online banking. The amount of work involved in going digital might seem overwhelming. However, it is a competitive field where change must happen now; not tomorrow, not at some point in the future. 

With the advent of technologies like IoT, AI, machine learning, NLP, and Big Data, today’s customer demands can different from tomorrow’s demands. Why plan ahead into the future and risk missing the digital revolution train, when you can start today?