Your internal policies are almost as complex as the government regulations. How do you make sense of it all to help your agents serve your customers best?
Insurance companies are governed by complex internal policies and external state and federal regulations. Both can and do change, sometimes often. Support documentation that doesn't keep up with these changes may create future issues through distributing outdated or irrelevant information to call center staff and customers. This couldn’t be more true in times of crisis, with all the pandemic disruptions.
In fact, IBM reports that in a 6 minute customer service call, agents spend 75% of their time doing manual search for the relevant information.
It is often the case that the data received by call center staff is poorly structured or unstructured, and much of the workforce can be occupied with processing it. Besides, the contact center is an environment with high turnover rates, where it is often the case that essential knowledge leaves with the agents possessing it.
To avoid this, you might want to consider using a call center AI solution to manage and structure incoming data (including information provided by your customers).
This article is a part of a detailed report on how insurance companies are improving their call centers with voice AI. Download the full report here!
According to Talkdesk, an AI-powered knowledge base should possess a number of features, but, above all things, it needs to be
connected – when all the information gathered across the enterprise is structured in a comprehensive way and can be easily accessed and navigated in real time
contextual – when a database employs natural language understanding algorithms and not simple keyword inquiries to provide the customer with information tailored to their request
dynamic – when a database delivers not just static search results, but up-to-date articles that evolve as the call center evolves
Accenture’s MALTA (Machine Learning Text Analyzer) AI and data governance tool automates the analysis and classification of incoming text across a variety of communication channels (e.g. when a customer emails their policy documents). Apart from classifying text, MALTA can also cleanse data and extract features.
This tool links policy documents to business processes, triggering the required actions. Based on the business and architecture set-up, MALTA or the API output sets off a process chain, an agent or a robot to execute the processing steps. Accenture Insurance claims that it takes a few seconds for MALTA to classify data, and that the automated classification is 30% more accurate than manual classification.
Such a single, shared, up-to-date point of reference can keep agents and customers informed. Agents don’t have to identify great amounts of incoming information themselves, which reduces both stress and response time.