TCS Top Uses of Artificial Intelligence in the Contact Center

Real Artificial Intelligence. Working. Now.

Understanding, not just hearing.

Replacing IVR Processes

An IVR has a set of simplistic predefined rules that it follows in a deterministic manner. An example might be a “sales” rule that transfers the caller to the Sales Department.

In contrast, AI, which includes areas like Natural Language Processing and Machine Learning techniques, can understand statements instead of simply giving the user a set of choices.

Also, with IVR, a predefined input gives a predefined output. With AI, a predefined input may give a completely different output depending on what the system has learned through probability calculations.

AI should also eventually improve the caller experience by ending the often frustrating “Press 1 for sales” or “Press 2 for customer service”, followed by a queue that negatively impacts modern contact centre interactions.

Predicting Customer Behavior

Artificial Intelligence (AI) will enable new trends in customer behavior to be identified at very early stages in their development.

AI will enable new trends in customer behavior to be identified at very early stages in their development. Interaction analytics tools already have the capacity to do this, but the addition of AI will accelerate identification and mean that there is less need for human intervention.

Providing this early insight to call center managers will enable them to brief advisors so that they handle the emerging customer needs and expectations more effectively. This could lead to retaining customers who might have been about to defect or up-selling to customers who are looking for information about a new topic.

The ability to spot trends in customer data will also enable call center managers to model best practices and predict the outcomes or the consequences of a particular course of action.

By using AI in this way, an organization could see benefits in resource planning, sales and marketing campaign planning, as well as attaining a more accurate Voice of the Customer (VoC).

Removing Humans From Skills-Based Routing Tasks

Familiar processes like forecasting and skills-based routing will become increasingly automated, and will require fewer, if any, humans in the loop. Companies that achieve self-learning algorithms via AI will gain a competitive advantage because they’ll be getting the most from their sea of data. Thanks to that, systems that are currently rules-based will move toward “cognitive” systems that allow for more intelligent prediction and reaction.

This will help to improve the customer experience by improving the way contact centers both predict and respond to demand. It will also mean that advisors can play to their strengths on a more continuous and consistent basis.

Monitoring Advisor Performance

One type of AI, which is great from a quality standpoint, is real-time speech analytics (RTSA). The solution analyses advisor and customer speech to provide live feedback to advisors, team leaders and quality assurance teams about what is being said, as well as how it is being said. It also monitors stress levels, speech clarity and script adherence, all while the call is in progress.

In addition, this technology has the capability of listening into the content of the call and effectively search for and provide the advisor with missing information to give to the customer.

Identifying Call Types and Passing Contacts to Relevant Channels

When receiving calls, AI can be used to help identify the type of incoming call request, so that it can be passed on to the relevant channel, be that human interaction or chatbot. AI can also provide contact centre advisors with useful background information on the customer or the nature of enquiry via a single desktop view, so that they can close the call quickly and effectively.

In short, AI will require companies to rethink the way that they interact with their most valuable asset – their customers.

Future success for companies will be somewhat dependent on how they organise their customer interactions and their willingness to invest in their contact center team, who are increasingly having to deal with more complex customer interactions.

Predicting Customer Needs

RPA tools can leverage machine learning engines and big data to predict customer needs, so that the chatbot can communicate with them proactively.

By analyzing, interpreting and understanding high volumes of customer inquiries, the solution could support up-selling or cross-selling of various products or services, while the RPA robots could auto-fill the application form to save the customer time.

As the technologies mature, it will become easier and more cost effective to create conversational interfaces for customers to interact with chatbots in a more natural way

Automating Responses to Customer Complaints

Combing process automation technology with optical character recognition (OCR) enables the automation of more complex business processes.

A common challenge facing many enterprises today is the ability to make sense of unstructured data in the form of customer complaints and inquiries accurately and efficiently.

So, let’s take a look at how this combination of AI can resolve this problem:

    Customer letters, emails and web forms are ingested into the system as scanned images (through OCR functionality).
    The system has the capabilities to understand the intent of the enquiry and extract all the relevant details from the content. It then produces and sends a recommended customer response over to the human employee. The employee has the option to edit the content before sending it over to the customer.
    The structured input is received by an RPA robot for data verification and enrichment (adding additional relevant info to the case).
    The updated data is then automatically uploaded to the case management