With the number of businesses adopting AI growing by 270% in four years most senior professionals, in the credit and collections industry, will have some awareness of AI and automation, with different companies being at different levels in their journey.
When considering what challenges AI and automation are now being used to solve, we can look at calculations that can be used in decision-making processes, such as when to reach out to a customer, how much to ask for, what channel to communicate over, and what may be improved by specific AI or automation interventions.
AI-driven customer engagement is all about finding out which customers should be contacted, thus driving down outbound contact attempts, and increasing overall contact rate performance.
So where can an organisation more efficiently deploy its resources in support of customer conversations? There are multiple answers here, but just a few will include:
So, an organisation can now ask in what parts of the customer journey and decision-making process can AI and automation make an impact? Maybe getting customers to respond to messages through better copy, giving customers better decisions through better conversational design, or using AI to identify the likelihood that a customer will complete a form.
After all, automation is not being utilised enough by the collections industry, as a whole we would be considered technology ‘laggards’ when compared to other industries.
AI and automation are now modernising debt collections through AI-driven customer self-service, which enables more customers to self-heal their accounts.
Anything that can drive that number up is a good thing. Also, AI-driven contact is finding out which customers should be contacted, thus driving down outbound contact attempts, and increasing overall contact-rate performance.
AI can help customers stay on plan and AI-powered decisioning, supported by workflows, gives end users much more flexibility. Rather than just “conforming to plan,” it is possible to personalise a plan on the fly, again, within rules.
In collections, the one-to-one personalisation of each customer journey is the goal. With AI, the personalisation of customer journeys is more easily developed and delivered. Identifying entities and intents early in conversations helps you understand what a conversation is trying to achieve.
Being able to offer conversational engagement that gives personalised options to customers based on their account status, or customer personae enables you to deliver services that feel custom developed to their needs. And being able to understand how a conversation is progressing and how likely it is to achieve its goal is a valuable AI outcome.
If a person does not fill in their income- and-expenditure form, can we redesign the flow of these questions, can we prepare the customer better, can we follow up in stages to collect all the desired information?
So, using AI and automation to connect a customer and an agent is a perfect example of vulnerability being identified early in a digital conversation and the business proactively creating a different action plan for every circumstance.
Highly personalising customer journeys are based on customer-specific circumstances is possible with AI. With our Propensity Studio, we are changing the way collections teams interact with their customers. Conversations can be personalised at a point in the conversation based upon what the customer response is. So no more, on fits all approach.
Automated personalised customer journeys are here to stay which is a win-win for everybody.