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AI’s role in customer experience OPINION
Artificial intelligence is transforming the way that businesses engage with their customers, but what is the value of this technology within the banking sector?
Co-founder and CMO, SugarCRM Clint Oram
A
rtifical intelligence is only worthwhile if it’s improving the customer experience and increasing efficiency of banking professionals. For example, if a customer wants to carry out
a simple task like setting up a direct debit, human interaction typically isn’t essential. This is where AI tech should be utilised – automating jobs where humans are not needed.
Banks are routinely rolling out chatbot technology for this very reason. For example, RBS’s chatbot ‘Luvo’ has the ability to solve basic customer issues, and can therefore reduce the need for as many customer service employees. There are other finance organisations jumping on board too, as UK startup Habito is providing customers with the world’s first ever mortgage advice chatbot – disrupting what is conventionally thought of as a lengthy procedure.
But chatbots have limitations as they do not have the capabilities to understand complex issues or emotional signals such as tone of voice. For example, a chatbot wouldn’t be best served delivering news regarding rejected loans, as the lack of empathy would likely cause offence. This is where human employees are still needed, to maintain relationships and avoid losing customers.
It’s fair to say that to date, the most noise around AI has been the role it can play in customer-facing businesses. But there is exciting potential in all aspects of the banking industry – particularly when it comes to analysing customer data, and optimising sales and marketing strategies based on the data. In fact, UBS predicts that AI could boost banks’ revenues by 3.4% and cut costs by 3.9% over the next three years.
Data drives meaning from AI
AI, machine learning and predictive technologies all hinge on the quality of the data set they are interpreting and learning from. The whole purpose of this technology is to study patterns of behaviour from data, and construct algorithms that can learn from and make predictions, boosting efficiency and cutting down on manual processes. The aim is to reduce the investment and resource needed to programme machines – it’s called machine learning for a reason. Customer relationship management (CRM) systems can be at the heart of this, driving insight and valuable learnings from rich, robust data.
As the technology becomes more adept at consuming large amounts of data, and leverage machine learning algorithms to generate insights more quickly, it will allow users to better know every customer, and to anticipate and predict customers’ needs more effectively.
By collecting unstructured data, such as social media posts and call centre recordings, and combining this with transactional data, CRM systems will deepen the customer relationship.
For example, relationship intelligence platforms help marketers within the banking industry gather a wealth of information about businesses and individuals from just a name and email address. It eliminates the need for lots of manual research and data entry and gathers customer intelligence from a broad range of social data sources so users can quickly and efficiently learn more about their prospects to establish a productive relationship.
While chatbots were the first noticeable manifestation of AI in action, innovations such as relationship intelligence are the next step. They will revolutionise the way we interact with our customers – telling us things we don’t already know about them or what would take hours to discover manually. Down the line, through AI, banks will be able to obtain intelligent recommendations for best actions, priorities and likely outcomes and to use this insight to engage in ways that truly resonate. By combining AI and CRM, all of our interactions will become more meaningful and effective.
www.ibsintelligence.com | © IBS Intelligence 2018
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