Knowledge management experts often discuss customer service being a prime use case for utilizing artificial intelligence (AI) and machine learning (ML). I have discussed this in previous articles concerning generative AI, natural language question answering (NLQA), and sentiment analysis, as well as insight engines.
We all know that patience is minimal today. Lower patience levels are often the case with consumers who expect perfection from companies and products they spend their hard-earned money on. No matter how complex the question is, consumers want answers quickly and faultlessly. Expectations for complex question answering are the same for standard and more straightforward questions. For businesses to achieve this high level of support, AI-based applications, such as insight engines, must be part of the workforce’s toolbox – providing valuable and consistent insights.
Making Customer Service More Intelligent
Large enterprises have over a million documents with valuable information not being leveraged. Businesses leveraging insights from these documents are the ones that are ahead of the curve in multiple business areas, such as customer service. Using insurance companies as an example, the amount of data compiled is truly never-ending. With data growing faster by the year, access to different applications and departments where the data is stored is becoming all the more necessary. Insurance companies may have departments that handle car, health, home, and liability insurance.
All these insurance departments have different access to data and claim handlers may need bits and pieces from multiple. Also, essential to realize, the claims are attached with many varied details that the company needs to account for to handle the claim correctly.
Mindbreeze InSpire saves claim handlers from the exhaustion of hunting for information. Customer service teams industry-wide are faced with the same difficulties and barriers. Insight engines use enterprise search technologies and artificial intelligence methods like machine and deep learning. These methods can search and find correlations between structured and unstructured data within corporations, regardless of size or business area.
Creating 360-degree views of multiple entities gives users the facts they need to act quickly and satisfy customer needs.
Many techniques go into this process, and insight engines make it simple for companies to cash in on all of them. Speech recognition techniques like natural language processing (NLP) and natural language understanding (NLU) set the foundation for human and machine correspondence – also referred to as “conversational search.” NLP encapsulates and processes words, either spoken or written. On the other hand, NLU uses algorithms to understand and make meaning out of the captured terms. The goal of NLU is to shed light on the user’s intent and derive meaning from the input. Doing so helps identify and predict behavior.
In the background of an insight engine, machine and deep learning are constantly in motion to certify intelligence is advancing from the use. By evaluating user behavior and searches, insight engines can adapt to provide users with the most relevant and personalized results to their queries.
Advancing Customer Service Intelligently
Both customers and support staff reap the benefits of insight engines and AI-based applications.
Self-Service: Many questions or inquiries directed at a company have already been asked by and answered for other customers. Support staff searching and ensuring the best answer is given to the customer requires resources and time.
What if time and resources could be saved?
With intelligent self-service, customers can answer their own questions with automated assistance. Self-service systems point customers where to go, whether it is a landing page on their website, FAQ pages, or blogs that detail a specific topic. The solution is capable of searching existing content from documents and other resources. Searching the content permits the solution to identify correlations and present relevant knowledge and a solid solution directly to the customer.
Personalized Support: A high level of automation significantly contributes to customer service offerings. Although an insight engine can achieve self-service optimization, the human touch cannot be entirely forgotten. Subject matter experts have experience that can’t be ignored and has to be utilized. In complex cases, expert knowledge will always be a driving factor to top-notch support. Employees must update CRM systems, databases, archives, and other internal sources. Without updating these sources, the much-discussed 360-degree view cannot be generated fully for complex question answering. Insight engines rely on this information to make their offerings more intelligent.
Are you ready to take advantage of intelligent customer service?
