In this post, I will talk about how using technology, and in particular conversational AI technology, can help you identify, support, and engage your most vulnerable customers, allowing you to manage the conversation more proactively and effectively.
During the initial months of the Covid-19 pandemic, the number of UK customers with vulnerability characteristics such as low financial resilience, poor health, or a recent negative life event, rose by 15%, and now stands at 27.7 million, according to a recent survey by the Financial Conduct Authority (FCA). Having one of these characteristics means that these consumers are at greater risk of harm. This figure is up 15% since the FCA completed its FLS in February, when 24.0 million displayed characteristics of vulnerability.
According to the October survey, there are now 27.7 million adults in the UK with characteristics of vulnerability such as poor health, low financial resilience or recent negative life events. Having one of these characteristics means that these consumers are at greater risk of harm. This figure is up 15% since the FCA completed its FLS in February when 24.0 million displayed characteristics of vulnerability.
So, with the incidence and complexity of vulnerabilities increasing, how does this impact how you engage with vulnerable customers? Or should I say, how do you know which customers are exhibiting signs of vulnerability? The best-trained agents in the world are able to pick up on some of the nuances of customer responses and others are only hearing what they want to hear.
So how can conversational AI technology help in identifying and helping vulnerable customers?
The nuance of individual customer circumstances can be challenging for agent advisors, there is no one fits all approach. But providing them with some technology support goes a long way in making the engagement experience for the customer and also the agents more positive and productive. Imagine having the ability to predict the financial and personal well-being of every customer at each stage of their conversation and design personalised journeys for each customer type, delivering the right message and level of agent resource at the right time.
This is what is possible with conversational technology. When I am talking about conversational technology, I am talking about messaging channels, automation and AI. I know when people hear the term AI, especially in the area of collections and with vulnerability there is a reluctance to consider applying it to the business. But believe me, when I tell you AI in collections is real and it is working.
But don’t let preconceived ideas or the experience with other industries shadow your views, conversational technology offers great opportunities to engage in not only more efficient ways but more empathetic and offer personalisation of customer journeys that are tailored to the specific needs of each customer, especially vulnerable ones.
Let vulnerable customers choose their channels
A digital channel for someone may be less likely to trigger anxiety than needing to speak to an agent on the phone. For others it may be the opposite, the key is an individual choice. Building in the ability to move from a digital channel if the customer wants, but also enabling them to remain within the digital channel where possible, gives the customer the control to manage the conversation on their terms.
In many cases, digital channels are the preferred choice as it is easier to talk about sensitive issues when you don’t talk directly to the person. I think that most people vulnerable or not will agree. Enabling different channels for customers gives them the opportunity to communicate through a channel of their choice.
Improving a vulnerable experience with Automation and AI
Conversation automation is designed to remove the cost of human one-to-one interaction, but it also brings added benefits when designed well. I can hear you now, “Automation improving experience for vulnerable customers, does it really?”. The answer is a resounding yes. Automation when coupled with AI is a powerful combination. The automation takes care of the who, where, what and when and AI (or what I call ‘Empathetic AI’), brings in the understanding.
By using natural language understanding (NLU), which is trained continuously provides a far superior experience for customers than the use of simple keywords. Natural Language understanding should always be configured to look for vulnerability and conversation flows designed to handle this in a way that meets compliance and ethical requirements.
Conversations that are earmarked as vulnerable may need to be immediately moved to a skilled agent team trained to deal with vulnerability, or the customer is given an option on how they wish to proceed. Whatever the business process, this should be designed rather than defaulted. Webio’s Propensity Studio is designed to assist agents with identifying vulnerability. Running against digital, text-based conversations, the studio flags up conversations that display characteristics of vulnerability.
With the functionality to be trained to focus on those specific vulnerability characteristics that are relevant to a business and used to augment the decisions made by agents. As the conversation progresses you may wish to allow the automated conversation to continue further and design different conversation journeys dependent on the level of vulnerability identified. The choice is up to you.
A key component is the ability to report on those vulnerable conversations, status tagging and reporting give you the foundations for producing the MI you need to ensure vulnerable customers achieve comparable outcomes to those customers that haven’t been identified as vulnerable and also that that engagement activities are having a positive rather than negative impact on customers.A word of caution though, it is always advisable to be transparent that the conversation is being held with an automated process i.e., chatbot. If this isn’t clear to the individual customer at the time of the conversation then any missed indicators or intents can appear rude if a response is provided that ignores or doesn’t adequately acknowledge what has been said.
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