The Art of Conversation and Customer Experience in Banking

The human voice is the most powerful tool, yet we still feel its unheard when spoken. In the words of Julian Treasure, a notable sound consultant, “We spend roughly 60% of our communication time listening, but end up retaining only 25% of it.” When we apply this to customer experience in banking, the retention of communication data, and reciprocative responses to customer communication by banking support systems, is questionable.

The tone of voice and intent recognition are innate qualities on which humans rely to make conversations meaningful and to conduct mutually beneficial business. Greeting customers by their names to offering personalized financial advice are two examples of getting customers’ attention. Our brains are wired to listen and filter beneficial information while discounting the irrelevant as noise.

Here are some stats on typical customer experience in banking expectations, including customers’ preferred channels of communication.

1. Fifty-two percent of millennials would switch banks for better mobile or digital capabilities. However, digitized consumers state they miss the personalized service that live agents provide.
2. Ninety percent of consumers rate an immediate response as “important” or “very important” when they have a customer service question.
3. Of those consumers, 33% state the most frustrating aspect of getting customer service help is being kept on hold and repeating themselves.
4. By 2023, customers will prefer to use speech interfaces to initiate 70% of self-service customer interactions, rising from 40% today, according to Gartner Customer Service Technology Vendor Guide, 2019, 27 June 2019.

Banks recognize that adopting customer service technologies and communication channels is imperative to meet the goals of customer satisfaction and company cost-effectiveness. Higher customer satisfaction can be achieved faster when most commonly asked questions and queries are executed by digital agents on voice and chat, using natural language processing and machine learning. Live agents can address complex inquiries.

In the past, digital banking solutions assumed that the customer had the knowledge and patience to make the right choices along their digital banking journey. Now, with most customers seeking instant gratification because of genuine time constraints or cultural preferences, there’s neither a beginning nor an end to the customer journey. This is glaringly due to an intermittent or lack of continuous customer engagement.

With millennials, Generation Z and the iGeneration fervently mutating communicative languages using slangs, emojis and other abbreviations — customer experience in banking must be differentiated so it continues to increase their customer lifetime value and bottom line.

A well-designed conversational artificial intelligence (AI) platform frees up customer support personnel by up to 20%. And that enables them to respond to more complex problems that are best resolved through human interactions. This adds to value creation by 80%.

The best bots today offer hyper-personalization with a thorough familiar voice and text-based user interfaces to enable a two-way digital experience and conversation. The keys to success are:

1. Building customer intent: It’s essential to develop and build customer intent; a vast and diverse consumer vocabulary data set is a must for this. Because the majority of data sets required to build conversational-based digital agents rests with large tech giants who control the worldwide web on search and eCommerce, developing your own data set requires effort. Once you identify customer intent, a predefined journey with predominantly multiple-choice questions can be used for the rest of the customer interactions to get a final answer.
2. Superior automatic speech recognition: Trained for higher vocabulary ranking, this provides context intelligence and enables natural language processing.
3. Speed to market: Use pre-built bots to begin the conversational banking journey. This includes a framework to build and customize bots or build new bots to meet revised business strategies.
4. Built for banking: These are enterprise-grade bots that are purpose-built to enhance the customer experience in banking.
5. Omnichannel: Build once and deploy across many channels with centralized management.This results in seamless customer experience as the data that’s shared is consistent across channels.
6. Flexible deployment options: The framework used to build bots should allow banks to deploy in the cloud, on-premises or using a hybrid approach.
7. Empathy engine: Though empathetic bots cannot replace humans, they perform better at certain tasks than ordinary bots do, including customer/transaction servicing, walking through services that the bank offers, answering basic questions (FAQs, execute a single function like bank balance and reset password).

Getting Your First Quick Win and Showing ROI

Banks have struggled to balance old-fashioned personalized services with the cost of using use human agents exclusively. Therefore, conversational banking can provide a path to reduce the cost to serve.

Banks would have to leverage existing customer data for previous interactions until they build their analytical and cognitive abilities to take complex inquiries. This includes segregating existing customer data sets based on complexity and frequency of the questions asked.

FAQs: Starting with the questions of the highest frequency will have immediate returns for the banks. It’s easy to automate answers. This automation will free up precious contact center resources to answer questions related to password resetting, address of the nearest branch location, etc.

Account and payment services: After converting FAQs into chatbots, banks can provide simple services for accounts and payments, such as “What is my balance?” and “Pay my mobile bill.”

Simple financial advice: Customers appreciate if their banks behave like their financial advisors — giving them information on spending patterns. This requires that banks have access to historical insights that would help answer simple questions like, “How much did I spend on online purchases last month?”

Predictive analytics: The most advanced use cases require predictive analytics and financial planning algorithms. In these instances, customers want a human to provide the advice. So a human might take over the bot conversation. An example of this would be, “What is the best investment plan for me?”

Complex queries: These are the questions for which chatbots haven’t been trained. These queries are answered with cognitive search abilities through unstructured data. Humans are still the best resource for these questions.

Prepare to start your journey toward offering AI-based self-service.