A Deep Dive into the CX Q&A Life Cycle. Transforming Customer Experience with AI.

Introduction 

Customer Experience (CX) teams face increasing pressure to deliver timely, accurate, and personalized responses to a growing number of customer inquiries. This challenge is compounded by the wide range of customer needs and multiple touchpoints.  CX agents must also master an overwhelming amount of information about their organization's policies, procedures, and processes. 

As one call center supervisor remarked to me: 

People don't realize how much of a 'jack of all trades' each of us must be, and how much knowledge and information we are responsible for.

Do we really need to think hard about how "questions and answers" work?  A customer asks a question, my CX agent answers that question, simple right?  When we look closely, we see that it isn’t simple at all. 

Each time our agent answers a customer’s question, and delights that customer in the process, it means our agent has navigated a very narrow path.  Providing support that our customers love might seem easy, but when we take a closer look there is more to it. It only looks easy when we get it right. 

Let's dive into what I call the CX Question & Answer Life Cycle.  We’ll outline the four key steps involved in answering a customer query, understand the challenges and complexities of each step, and finally, see if AI might be the perfect partner to help teams streamline, enhance, and personalize this process. 

Section 1: The CX Question & Answer Life Cycle: An Overview 

The CX Question & Answer Life Cycle can be broken down into four stages: 

- Understanding the Question 

- Finding the Knowledge 

- Understanding the Content 

- Answering the Question 

These steps form the foundation of any customer service interaction, whether in a call center, via email, or in a chat session. These steps may seem straightforward, but each presents different challenges that can bog down our teams and result in longer response times, low quality answers, and frustrated customers. 

Let’s walk through each Q&A step: 

Step 1: Understand the Question 

This seems easy and obvious, but is it?   If a customer already knows the answer, they will ask a perfectly clear question.  But they are asking for help precisely because they do not know the answer, so their questions are often confused and off the mark. 

It is our agent’s job to interpret the intent behind the question.  They must also understand the context in which it’s being asked.  This can be difficult even if the agent is experienced.  If the agent is inexperienced this becomes even harder. 

What if the question is asked in a foreign language?  How much extra effort will it be to translate the question into the language the agent can handle? 

It’s not just about the words; it’s about understanding the underlying need and divining the intent. 

Step 2: Find the Knowledge 

Every organization provides a knowledgebase (KB) for their agents to store up-to-date information about the org’s processes, procedures and policies that your agents will need to answer customers’ questions.  These KBs range from files on network drives, SharePoint servers, intranets, and dedicated knowledge management tools. 

Finding the data you need in these KBs is universally an awful experience.   Over time the organization’s information becomes fragmented and strewn across multiple repositories, and the information in the KB can be incomplete, conflicting or obsolete.  The built-in search mechanisms are typically poor. 

The news isn’t all bad; our research finds that Pareto’s 80/20 rule holds true in CX too.  Experienced agents can typically answer 80% of questions off the top of their head. (Basically an ‘instant’ answer for the customer.) That leaves 20% of questions that require a trip to the KB. 

We’ve found that a “trip to the KB” can take on average 3-7 minutes to find the required information.   At best, this means a long hold time and a frustrated customer.  At worst, it means the dreaded, “Can I take your number and call you back?”  This then can be followed by the agent needing to interrupt their supervisor to get help. 

Step 3: Understand the Content 

The agent isn’t at the finish line when they have found the right documentation. 

Now our agent must read and understand the content of the document(s) they found.  This “read and understand” step can be more time consuming than the search.  There may be multiple documents to read and review, the information might be scattered throughout the documents, there might be conflicting information, or the documentation can simply be difficult to decipher. 

We can’t answer the question until we have read and understood the information. That takes time. 

Step 4: Answer the Question 

Finally!  Our agent has found, read and understood our process, procedure or policy.  Now we can now craft the response to the customer.       The agent must now synthesize everything found into a single coherent answer.  An answer that fits the intent of what that customer asked originally.  An answer that aligns with our preferred corporate tone and organizational voice. 

We did it!  Now on to the next call... 

Recap:

Looking through the lens of the CX Q&A Life Cycle, we see we are asking a lot of our CX agents.  We want our agent’s answer to leave our customer happy, satisfied, and confident.  To do that our agent’s answer must be fast, accurate, and match the customer’s original intent. 

The life cycle operates in a continuous loop. Each new call, email, or chat starts the cycle over again.  Each question leads to interpretation, new searches, and new knowledge to understand and synthesize.   If we improve any one of these steps, we improve our whole process multiplied times each agent, and each customer interaction. 

Section 2: Where Can AI Fit in the CX Q&A Life Cycle? 

I often warn leaders that AI is not the solution to every problem, or even most problems.  For many problems you are trying to solve in your business, Gen AI solutions don’t provide significant improvements over the existing tools you are already using.  But: 

AI is perfect for the Q&A Life Cycle. 

AI can clearly and measurably improve each step in the Q&A Life Cycle we've described.  It is almost an ideal optimizer, and can help CX teams respond to customers faster, more accurately, and more empathetically. Here’s how: 

Step 1: AI for Understanding Questions 

Gen AI is excellent at quickly interpreting customer intent, regardless of the language used or the phrasing of the question. With AI, context is understood in real-time, and intent is extracted with accuracy. 

Step 2: AI for Finding Knowledge 

AI-driven search tools can sift through massive volumes of documentation in a KB at lightning speed.  The magic of vector databases gives us dramatic improvements in searching for relevant information buried deeply in our documents. The benefit isn’t just the increased speed, it is the search quality. 

A vector database paired with a Large Language Model (LLM) can find the right information where traditional keyword searches simply fail due to the reliance on exact matches. Our AI and vector database pair gives us the ability to search by understanding context and meaning. 

Step 3: AI for Reading and Understanding Content 

AI can read your content at computer speed.  Even if our search finds hundreds of relevant documents within your KB, LLM based AI can read and understand those passages in milliseconds.  Just think of the difference in time between your agent reading 3 pages of text vs the AI. And LLM's have no difficulty understanding your content, remember that the second 'L' in 'LLM' stands for Language.

This processing speed difference might be the single biggest advantage of using AI in CX. 

Step 4: AI for Answering Questions 

This is the easy and obvious part.  LLM’s are also great at writing text.  AI can easily synthesize the data from the relevant documents, and then draft a great answer in your company’s tone of voice, and across various languages. 

This allows our CX agent to review and personalize the AI’s response, allowing for high-quality, consistent, and fast responses to customer questions. 

A Caveat:

You will note that I do NOT automatically advocate that AI should replace your CX agents.  In fact, in many use cases the best use of AI is to support and enhance the CX team’s efforts. Instead of replacing your team, AI can give your CX team superpowers. 

Conclusion: AI is the Future of CX 

We have described the CX Question & Answer Life Cycle to highlight the hidden complexity of our customer service interactions.  We don’t realize how much load we put on our CX teams’ shoulders. 

AI is a surprisingly good fit to improve and enhance each step in the Q&A process. AI helps to reduce customer wait times and improve answer quality and accuracy. The resulting improvements in customer satisfaction and engagement are obvious. 

AI is not here to replace customer service agents; instead, it’s here to empower them. CX teams are a critical part of the customer relationship we have worked so hard to build. AI can simply provide faster, more accurate access to the information our teams need to do their jobs. This enables our CX teams to focus on delivering exceptional, empathetic service to our customers instead of slogging through yet another painful search. 

Embracing AI is a key to future-proofing customer experience operations and meeting the ever-evolving demands of customers. 

Tell me what you think. 

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