What Is Customer Service Technology?

This post originally appeared on DestinationCRM.com

Good customer service is the result of the right attention to strategy, business processes, technology, and people management. This seven-part series focuses on customer service technology and explains the what, why, how, and when of the technology. Let’s start at the beginning: What is customer service technology?

The contact center technology ecosystem for customer service is a nightmare of complexity. At a high level, to serve your customers, you need to:

  1. Capture the inquiry, which can come in over the phone, electronically via email, chat, or SMS, and over social channels, like Twitter, Facebook, or an interaction escalated from a discussion forum or a Web or speech self-service session.
  2. Route the inquiry to the right customer service agent pool.
  3. Create a case for the inquiry that contains its details and associate it with the customer record.
  4. Find the answer to the inquiry. This can involve digging through different information sources like knowledge bases, billing systems, and ordering databases.
  5. Communicate the answer to the inquiry to the customer.
  6. Append case notes to the case summarizing its resolution and close the case.

The technologies to do this are the ones for:

  • Multichannel communication. This category comprises technologies that support the business processes for interacting with customers over voice, electronic, and social communication channels. These technologies include automatic call distributor, computer telephony integration, interactive voice response, speech recognition, predictive dialing, email response management, chat, co-browse, virtual assistants, social media adapters, proactive outbound notification, and mobile customer service applications.
  • Knowledge management. This category comprises technologies that are used to identify, create, review, publish, and maintain multimedia content, including video, that allows customer service agents to answer customers’ questions and allows customers to find answers to their questions via Web self-service. These technologies include knowledge management, video, and customer communities.
  • Agent productivity solutions. This category comprises technologies that agents use to create and manage an incident (case) in response to a customer inquiry. It includes applications that are used to monitor agents’ answers to questions to ensure a consistent service experience in accordance with company policy and applications used to optimize agent staffing and scheduling. These technologies include case management, process guidance, unified agent workspaces, quality monitoring, and workforce management.
  • Customer service analytics. This category comprises analytics used to deliver the optimal service interaction that is targeted to the persona of the customer and the issue at hand. Technologies include next best action and interaction (speech and text) analytics.
  • Voice of the customer. This category comprises technologies that customers use to interact with their peers to share advice, best practices, and how-to information. It includes the technologies customers use to voice their opinions regarding a company’s products and services over social channels. Technologies include enterprise feedback management systems and social listening platforms.


Virtual customer service

Virtual customer service technology is very important for growth and development of any business organization as it provides efficient call services for its customers that helps improving business.

Customer service expands far

Customer service expands far beyond the 1-800 number today. Customers are getting in touch via a wide variety of technologies and businesses need to keep up. That is why customer service tools are essential. The customer is always the top priority.


thank you for sharing such nice info to us. i like, it its very informative one keep sharing this type of information to keep in touch with the people.

Natural Language Processing

Steps 2-5 also require components of natural language processing (NLP). In particular, steps 2-4 require the actual understanding of the customer's inquiry using natural language understanding (NLU) algorithms.

If the inquiry was spoken, a speech recognition engine may not be sufficient for producing a reliable transcription. Instead, speech recognition has to be combined with NLU
in order to produce a context-relevant representation.
This can be done using a semantic model that takes the world knowledge of the application into account.