As we look to 2021, the pressure is on CIOs now more than ever to be accelerating digital transformation and innovation. A key area that insurers should be focused on is customer experience and engagement. However, a large part of the CIO’s battle will be attempting to meet evolving expectations for customer service with existing technology systems that are unprepared for the demands of the new digital age. This blog discusses how AI can help build a strong technological foundation for meeting customer expectations in 2021.
21/12/2020 by Alicia Miller
Customer trends identified by Gartner show that there has been a radical increase in consumer digital dexterity [in 2020]. Customers are using a wider selection of digital channels than ever before. With an evolving omni-channel customer experience comes more challenging and higher customer expectations for customer service. In the past CIOs might have previously focused on traditional system projects, however, in a more digital world, it’s imperative that their end goal is positive and proactive customer engagement to reduce insurer switching and boost customer satisfaction.
Intercom’s 2021 Customer Support Trends Report found that “73% of support leaders say customer expectations are increasing but only 42% believe they’re meeting those expectations.” – Intercom
2020 has added fuel to an already burning fire within most service organisations, which is that a large percentage of demand for customer service is due to failure rather than success in meeting customer expectations. This concept of “Failure Demand” and “Value Demand” are customer–centric concepts within service organisations that focus on identifying root causes of issues within a business that result in the business not meeting a customer’s expectation for customer service.
Let’s define them:
Value demand: “product and service delivery that meets customer expectations, wants and needs.” These are products and services the customer is happy to receive: renewal email reminders, follow-up calls with discounts etc.
Failure demand: “demand caused by a failure to do something or do something right for the customer”. – Prof John Sneddon, Vanguard Consulting
For example: “I called last week to change my address. However, it has not been updated on my policy yet.”
This is a classic example of failure demand that is a preventable yet time-consuming task for both the contact centre agent and the customer, as well as incredibly frustrating.
Sneddon himself found that 40% – 60% of all company activity was a result of failure demand, meaning a loss of employee productivity, time, resources and, ultimately, customers. It’s clear that for companies who proactively aim to combat failure demand and better utilise budgets and employee capacity, there are clear benefits and business value to be derived from proactively meeting and exceeding value demand.
How AI can help combat failure demand
“38% of insurance CIOs are increasing their investment in AI and ML for 2021” – Kimberley Harris-Ferrante, Gartner, 2020
1) Back-end optimisation
First of all, CIOs should be looking to understand how AI can help identify the issues causing failure demand internally. By having AI and Machine Learning at the centre of an enterprise to integrate business data with customer experience, business teams can identify system issues, trends and inefficiencies that result in wastage, time delays or business failures.
Secondly, by having Natural Language Processing in a customer facing role with reporting and analytics, AI plays a crucial role in helping identify web pages that are down, products that aren’t working as they should, or wording that is ineffective and confusing in the user journey. This helps teams identify and fix problems more quickly that will help significantly reduce failure demand.
Finally, by integrating AI with Robotic Process Automation (RPA) to automate legacy backend processes, CIOs can use the NLP insights generated from customer engagements to address the business areas that generate failure demand. RPA can be used as an MVP to test and prove processes prior to investing in implementing new tech to replace legacy systems. RPA is a means to an end rather than the end goal in order to prove a process will have a positive impact on customer experience and patch holes that generate failure demand. Although this will create technical debt, CIOs will have a tried and tested understanding of how to build a strong business supply chain that enhances the digital customer journey in the long term and a clear view of how to address this technical debt.
2) Front-end optimisation
One of the most valuable advantages of implementing a customer facing AI assistant is that businesses can automate the most frequent and laborious tasks for live agents. For example, an AI assistant integrated with the policy system can easily and quickly update the address of the customer in the example previously discussed, without involving a live agent. This frees up the agent to handle more challenging customer queries that require a human touch. What’s the impact of customer self-service? Intercom’s 2021 Customer Support Trends Report predicts that support leaders who automate support with chatbots are 60% more likely to report an improvement in resolution times and 30% more likely to report an increase in customer satisfaction.
In addition to this, if an AI assistant is not able to answer a customer query, then it can hand over the query and all the information already discussed to a human agent. Since the agent will already possess the relevant information, this will remove repetitive identity and verification questions and allow the customer to seamlessly resolve their issue. In turn, this will lead to a clear reduction in handling time.
“Support teams who use chatbots to automate conversations are 27% more likely to say they’re prepared to meet accelerating customer expectations compared with teams who don’t” – Intercom’s 2021 Customer Support Trends Report
In conclusion, for many organisations the first step to innovation is leveraging technology to strengthen their technological foundations for the future. AI can help insurers integrate business data with customer experience to reduce failure demand and proactively engage with users in the broad range of channels they use to interact with the company. As we transition to a post-pandemic world, prioritising investment in AI is a valuable step towards becoming an intelligent insurer and supporting the value chain backbone of the company. Let one small AI assistant transform your company from the inside out in 2021 and beyond.
Curious to know how Lobster can help your business combat failure demand? Request a demo and an AI Solutions Consultant will be in touch with you shortly.