How Contextual Answers Transforms Customer Support

How Contextual Answers Transforms Customer Support | AI21

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Delivering exceptional customer support is more challenging than ever before. Customers expect fast, comprehensive solutions to increasingly complex issues, putting immense pressure on support teams. Yet sifting through vast knowledge bases to find relevant answers is inefficient and wastes precious time.

AI21 Labs has identified gaps where Generative AI can create fast, tangible impact for businesses, starting with their customer support teams. ‘Contextual Answers’ is an AI system based on question-answering technology that allows support agents to search their company’s extensive knowledge bases to rapidly provide customers with accurate, personalized solutions. It can help businesses boost agent productivity, reduce handling times, and increase their first-call resolution rates – all the while enhancing customer satisfaction.

This article will explore how Contextual Answers integrates into existing workflows to boost productivity. You’ll learn how it empowers support teams to deliver the responsive, satisfying experiences customers demand in today’s highly competitive landscape.

The Growing Pains of Customer Support

Studies show that increasing customer retention by just 5% can boost profits by 25%-95%. And the number of repurchases and renewals also increases by 82% when the customer receives excellent and prompt service. Despite the fact that high-quality customer service boosts profits and loyalty, achieving it is a formidable challenge.

‍Costs

One major pain point is the substantial cost of staffing large, round-the-clock support teams to meet today’s expectations of instant, reliable responses. Even then, delivering personalized, researched answers quickly strains resources.

Effectiveness

Even large teams struggle resolving queries quickly and correctly. Inquiries often require extensive research and tailored responses, lengthening wait times and frustrating customers.

Self-Service

Despite readily available website information, users often escalate straightforward questions to agents. This overburdens staff and disappoints customers. Improving accessibility is key for satisfaction and efficiency.

Using Contextual Answers to Overcome Support Challenges

What is ‘Contextual Answers’?

Contextual Answers is a task-specific Generative AI system, based on large language models (LLMs), to provide accurate responses to questions based on a company’s data. It lets users ask natural language questions and receives answers grounded in uploaded documents and information fed into the LLM. This prevents fabricated responses common with AI, known as AI hallucinations.

At AI21 Labs, we have built a comprehensive end-to-end API solution which allows companies to effortlessly upload their required documents and allow their employees or customers to ask inquiries using natural language. The responses provided rely solely on the inputted information. In addition, the Contextual Answers tool provides a reference to the source which the answer was taken from, ensuring an unparalleled level of credibility.

How Contextual Answers Can Improve Customer Support

There are two main ways to use Contextual Answers for customer support:

Improve Efficiency

The Contextual Answers system gives customer support agents quick access to relevant information. This makes it faster for them to find answers when customers ask questions, as it significantly cuts down research time. This results in shorter wait times for customers – a significant metric for customer satisfaction.

Automate Responses

The Contextual Answers system can be built into the company’s website as a dynamic chatbot or refined search bar. This lets it directly answer common customer questions instantly and accurately. The company then doesn’t need to handle questions already answered on its site. This focuses support efforts on complex issues and improves team efficiency.

Companies can use one or both approaches. But the Contextual Answers system reduces the need for large support teams either way, while improving customer satisfaction. The ultimate outcome includes a two-fold benefit: a significant reduction in costs of support representatives, along with an elevation in company profits, as a result of the ongoing retention of satisfied customers.

How to Implement Contextual Answers in Your Company

1. Make a Plan

The first step is to make a plan for what you want to achieve. Decide if Contextual Answers will be used internally, externally, or both. At this point your company will also need to choose an LLM provider, exploring factors such as price, the latency of the LLMs, the throughput (how many questions per user), API quality and ease of use.

You’ll also have to decide if you need multiple languages. If so, you’ll need to add a translation step to the process, and pick which languages to include.

For example, an online bank approached us with the need to improve their customer support.

Being primarily online, one of their main features is having available support 24/7, and Contextual Answers was a perfect fit for this use case.

During the initial planning phase, this digital bank determined that, for their specific use case, an externally-facing customer solution best suited their needs. A user-friendly chatbot was developed to respond to facilitate customer inquiries and provide relevant answers. Fast, reliable responses grounded in the company’s data were the priority.

It was equally important for them to maintain consistency in tone, format, and response length during customer interactions. They also included a translation component into the process, recognizing the multilingual nature of their clientele.

During the planning phase, these decisions were used as guides to streamline our processes and align them with the bank’s specifications.

2. Curate and Label the Data

Once your company has its plan in place, relevant data must be compiled and curated.

If the system is for external use, and customers will be interacting with it, the required data should also be publicly available to customers. For internal use by the support team, the company can also incorporate non-public policies and guidelines.

After the data has been collected, it needs to be labeled accordingly.

For instance, a SaaS company might have premium,...