<img src="https://secure.leadforensics.com/133892.png" alt="" style="display:none;">

Achieving customer satisfaction is not just about speed; it has to be in combination with effective problem solving and personalization. This is where generative AI chatbots come into play, cutting-edge tools that transform customer service organizations and processes. By integrating them into service strategies, companies can not only boost customer satisfaction, but also increase operational efficiency and reduce costs.

Research published this year from Gartner identified that 42% of top performing organizations have already implemented generative AI in production, with one of the top use cases being customer service automation.

This article is aimed at business leaders looking to understand the potential of this emerging technology and implement it successfully within their organisation.

The shift towards personalized customer service: Understanding generative AI

Historically chatbots have acted as customer service agents being able to answer generic questions from users or perform simple workflows. We've all experienced the frustration of robotic chatbots that don't understand your question, force you to box your query into a category that's not really appropriate, fail to answer your question before inevitably being forced to connect you to a human.

By equipping these chatbots with generative AI, businesses are essentially creating a digital workforce capable of understanding and addressing customer needs with an accuracy and depth previously thought to be exclusive to human capabilities. This is achieved via the integration of Large Language Models (LLMs) to collect information, execute processes, and respond to users’ queries.

The value proposition of penerative AI in customer service

As we all know satisfied customers are more likely to exhibit brand loyalty, increased customer life-time value and can even refer new customers.

Chatbots represent a major contact point for customers to get answers to their questions or seek action. Generative AI can craft responses unique to each customer's query while following the controls you set and the brand guidelines you provide. This fosters a sense of individual acknowledgment seldom seen in the sea of standardized customer service model and call scripts human agents follow.

Granting chatbots access to the data collected on customers becomes imperative. By integrating these AI systems with CRM (Customer Relationship Management) databases, chatbots can draw on a wealth of historical data — from past purchases to customer preferences and interaction history. It enables them to deliver not just accurate answers, but also recommendations and solutions tailored to the unique needs and past experiences of each customer.

However, with great power comes great responsibility; this necessitates the stringent authentication of users to safeguard privacy and ensure that the personalized assistance provided is both relevant and secure. Establishing robust authentication and security protocols is thus critical in creating a secure environment where personalization thrives without compromising customer trust.

Handling volume quickly and consistently

A person can only handle a single conversation at once, and you can only handle as many conversations as you have people. A chatbot can handle endless customer queries simultaneously, doesn't take the weekends off, doesn't sleep or get sick.

Chatbots don't get emotional, hungry or anything else that causes a loss of politeness and emotional intelligence in a person. Their service level is consistent and always adheres to the brand voice guidelines you set.

Overall, this translates to a rapid resolution of issues, no cap on volume and on top of it a consistent service level that can appear superhuman.

Signficant cost savings possible

By reducing the dependence on human customer service personnel, call center technology, facilities and more, businesses stand to cut significant operational costs. Generative AI chatbots, when implemented correctly, can lead to on average a 23% reduction in customer service headcount (Gartner, 2024).

Navigating the chatbot development process

It’s easy to build a mediocre chatbot that hurts customer satisfaction more than improves it. It’s important to understand that the chatbot interface is only one component of the chatbot as a whole and is probably the least important! These are some of the elements commonly done poorly.

The knowledge base is the key to success

The backbone of any successful Generative AI Chatbot is the depth and usability of its knowledge base. The knowledge base contains all the information about the specific things the chatbot can answer questions on, for example a simple customer service chatbot might help users answer typical FAQ questions without having to read extensive FAQ webpages. However, real value comes when the chatbot can provide updates on order statuses or other information stored about the customer.

This element of generative AI solutions is known as RAG or Retrieval Augmented Generation. It is nuanced and comprised of many different approaches and technologies. It’s not enough to dump some files in a file store and hope the chatbot can navigate them or add the data directly into the chatbot query behind the scenes.

For some this could be a vector database, storing information in a form which provides the chatbot with the ability to rapidly search and find relevant information. This is great for fast answers to simple queries.

For organisations looking for significant accuracy boosts it could include graph networks which are used to visualise the relationships between pieces of information, therefore allowing the chatbot to answer more complex questions that combine a variety of data.

Others will look to finetune the LLMs themselves, adding their specific knowledge and answers directly into the LLM, reducing the need for complex retrieval systems.

Our recommendation is always to leverage the simplest, lowest cost, easiest to maintain option that provides the customer service level which meets your users’ expectations.

Controlling the chatbots tongue

The strength of a language model is in its ability to answer diverse questions with relevant responses. However, this potential requires careful management and oversight. Trained through data gathered from all over the internet and other sources, these models can occasionally produce responses that may be inaccurate (hallucinations) or inappropriate if not closely controlled. In a 2023 McKinsey survey 88% of businesses were concerned with the inaccuracy of generative AI's responses.

Therefore, setting rules and validation measures, along with maintaining continuous governance is essential for preventing reputational damage. Fortunately, methods for preventing these responses are becoming more mature and plentiful. Some are going as far as to use a preliminary LLM to rewrite the users question to ensure nothing inappropriate makes it to the primary LLM answering the question.

Make customer support accessible

Making your users navigate through 15 different useless webpages to access your customer support is a common technique to reduce the amount of people requiring human interaction. If you're confident in your chatbot put it at the forefront of your customer support and let it do its job.

It also needs to function like you'd expect a chatbot should. Waiting 60 seconds for an AI response isn't acceptable anymore, it doesn't have to type using fingers so it should be fast. Your holistic application architecture needs to support this as one of the primary goals.

Be Proud of your AI

Users want to know when they're talking to an AI bot as opposed to a human. Historically chatbots have performed poorly and serve as a frustrating intermediate to get to a person that can solve your problem. A Deloitte study from 2023 identified that 50% of respondents were concerned about the transparency of businesses use of AI. Label your chatbot as an AI bot, tell the users it’s an AI bot, make it clear and prove their expectations wrong.

Use generative AI in customer service to be in the forefront

The imperative for business leaders to invest in Generative AI for customer service cannot be overstated. With shifting customer expectations, driven by early adopting competitors, staying on top of the market means moving quickly into this new technology.

Chatbots that perform better than humans are not just a futuristic concept but a present-day possibility to enhance customer satisfaction, streamline operations, and secure a significant edge in the market.

Columbus's support can help you identify the right use cases customised for your organisation, implement them quickly via accelerators like our Chatbot Starter Pack, and ensure successful adoption by your users. Reach out to charles.wright@columbusglobal.com to find out more.

Topics

Discuss this post

Recommended posts

Collecting ESG data is complex, therefore establishing a set methodology is essential. Here are our six essential steps to ease the collection and increase the quality of your ESG data.
Every business sector knows the impact of customer churn—how the steady stream of departing customers can erode profits and destabilize even the most robust revenue streams. But what if there was a way to gain insights into the future, to anticipate and alleviate this risk to your business's financial success?
For many modern organizations, becoming data-driven means having the right insights to make effective decisions quickly and confidently. It’s no surprise that digital business leaders are investing heavily in their data platforms.
Imagine a modern data platform as your all-in-one system for collecting, storing, and consuming massive amounts of data in real time. It is a game-changer, allowing companies to weave data into almost every aspect of their operations. Think of it as the backbone of a business’ data strategy.
Making use of your organisation's data is essential for staying competitive in any industry. Having a modern data platform is a must, and it is important that management makes the right decisions for their investments from the start. We generate ever increasing amounts of data with systems and tracking that covers almost all of our business processes. Therefore, being able to leverage that data via a modern data platform is a priority for most organisations. This article covers: • What business leaders need to know about modern data platforms and their benefits • How you can get started with a modern data platform Modern data platform is a term that's thrown around with little clarity or consistency. Depending on the author it can mean multiple things and cover various technologies. On top you'll also hear terms like lakehouse, data mesh and fabric which make it even more difficult to understand what one actually is. Therefore, we’ll explore a few clarifiers to define and explain a modern data platform in an understandable way.
right-arrow share search phone phone-filled menu filter envelope envelope-filled close checkmark caret-down arrow-up arrow-right arrow-left arrow-down