Chatbots for customer experience

Customer experience chatbots are AI-powered conversational tools designed to interact with customers across various touchpoints. These automated tools streamline customer inquiries, answer questions, troubleshoot problems and guide users through processes without human intervention.

Modern chatbots range from simple rule-based systems following predetermined scripts to sophisticated AI agents capable of understanding context, learning from interactions and autonomously handling complex queries. Understanding the difference between these chatbots helps organizations determine the right solution for their needs. These options include:

Rule-based chatbots: These chatbots operate on predetermined scripts, following a programmed path to provide relevant responses. Typically, they act as something as a first-line help desk. These chatbots tend to be predictable and reliable for handling frequently asked questions, but struggle with variations in phrasing or requests outside of scripted scenarios.

AI-powered chatbots: AI chatbots use natural language processing (NLP) and machine learning to understand user intent, even when questions are phrased in unexpected ways. Using artificial intelligence, these chatbots can interpret context and provide more nuanced responses, improving over time as they learn from patterns in customer communication. 

As chatbots have increasingly incorporated new technologies—generative AI and other large language models (LLMs), NLP, intelligent document understanding, speech recognition and speech synthesis, among others—they’ve become nearly ubiquitous across the customer journey.

And they’re a central part of a wide-ranging change in how customer support teams operate: According to recent research from the IBM Institute for Business Value, 71% of executives aim to fully automate customer support inquires by 2027. Another 47% forecast touchless automation in customer product and service training by the same date.

By delivering consistent and intelligent customer care, chatbots can increase productivity and empower employees to focus on high-value activities across an organization. They can also meet surging customer expectations for personalized, omnichannel, real-time support.

Customers with a positive chatbot experience are more likely to have a positive relationship with a brand. But impractical or limited chatbots that insufficiently resolve queries frustrate users. Typically, successful chatbot initiatives prioritize careful planning and appropriate data practices, producing intuitive and secure AI tools. 

The evolution from simple chatbot to an intelligent AI agent represents a shift from the automation of responses to the automation of outcomes. Early customer service bots were interactive FAQs—rigid decisions trees that could respond only to specific keywords and menu selections.

But with the introduction of natural language processing and generative AI, chatbots began to interpret intent and provide more effective answers by using a conversational AI interface. This capacity can transform customer experience from purely transactional to truly helpful, increasing personalization and fostering friction-free customer experiences.

While these terms are often used interchangeably, they represent distinct levels of capability and autonomy, which have critical implications for both AI customer service practices and a customer’s journey. 

Chatbots are conversational interfaces designed primarily for instant responses and information exchange. They excel at answering questions, providing status updates and guiding users through defined processes.

A traditional chatbot might help a customer check an order status or find store hours. Most chatbots are fundamentally reactive—they respond to user queries but don’t take independent action. A chatbot, for example, might provide return instructions and a link to a return portal in response to a user query.

AI assistants represent a more sophisticated tier, providing intelligent recommendations through an understanding of natural language commands. These systems integrate more deeply with business data and orchestrate information from multiple sources to provide comprehensive support. However, like chatbots, AI assistants primarily rely on user inputs, predefined tasks and preprogrammed responses to inform and guide users. Assistants do not autonomously execute on behalf of a customer. For example, in response to a customer question, an AI assistant can pull up order details and explain a return policy specific to a product. It would not also act to the process said return.

AI agents are autonomous, completing tasks and deciding within defined parameters. Depending on which external tools and APIs agents have access to, they can solve more complex issues (for example, modifying reservations, processing exchanges and coordinating actions across multiple systems). AI agents break down complex requests into sub-tasks and determine an appropriate sequence of actions. For example, based on a single customer prompt, an AI agent can handle various tasks. The AI agent could make a purchase, initiate a replacement, generate and send a shipping label, and follow up to confirm a new item arrived in good condition. 

Customer service agents receive high volumes of calls daily, quickly becoming outnumbered by customer queries that typically have common answers. Therefore, customer needs are left unmet over time, depleting customer satisfaction, business reputation, customer retention and a business’ bottom line.

Chatbots are computer programs that understand customer questions and automate responses to them, simulating human conversation. Through the functions of AI-powered chatbots, business owners vastly improve customer satisfaction, while also relieving the constant pressures on human agents and allowing them to be more intuitive in their work. Recently, researchers at Harvard Business School analyzed more than 250,000 chat conversations, finding AI chatbots reduced response time by 22% and improved customer sentiment by as much as 1.63 points.

Customer service chatbots also provide businesses with invaluable insights into how, and how effectively, their automated workflows are operating. With the use of customer data provided by chatbots, businesses:

• Expand their knowledge base on issues that plague customers during their service experience

• Reduce wait times throughout the customer journey, allowing service teams to concentrate on more significant customer interactions that need legitimate human intervention.

• Provide themselves with relevant metrics for customer satisfaction

• Allow customers to explore a more personalized experience

• Maintain healthy costumer relationships

Over the last decade, chatbot adoption for customer experience has accelerated dramatically, largely because they address several critical business challenges simultaneously. Some of the central benefits of using chatbots to engage with consumers across the buying journey include:

Unlike human support teams, chatbots are available at any time, on any platform, and anywhere across the globe. They provide instance responses at any hour, meeting the expectations of customers who increasingly demand immediate assistance regardless of time zones or business hours.

Chatbots deliver uniform responses across platforms based on their programming and knowledge base. This process reduces the variability that might come with human agents with different levels of experience or training. This consistency helps maintain brand standards and ensures accurate information delivery—even across cultures, as many provide instantaneous multilingual support.

While chatbots do require some initial investment, the long-term costs per interaction can be substantially lower than human-staffed support. This method allows businesses to reallocate human agents to more complex and high-value interactions—such as customer relationship development involving creativity or specialized expertise. This approach serves to both increase productivity for agents and to provide cost savings to the business at large.

Every chatbot interaction generates valuable data about customer needs, challenges and patterns of behavior. This information helps businesses identify common issues and make data-driven improvements to their customer experience strategy. Some organizations integrate chatbots with other day-to-day tools, such as customer relationship management (CRM) platform like Salesforce to provide a 360-degree view of consumer data. Others perform sentiment analysis on consumer interactions, gaining a deeper understanding of consumers’ experiences.

Today’s consumers expect to receive streamlined, accurate and cohesive experiences across platforms and channels. Today’s chatbots communicate in various mediums including mobile apps like WhatsApp social media platforms, text messaging (SMS) and in-browser chats.

A single chatbot handles thousands of conversations simultaneously, eliminating wait times during peak periods. This scalability is valuable during product launches or crises, when support volumes spike unexpectedly.

In customer support, chatbots handle simple tasks by reporting on order tracking and return processing along with troubleshooting and answering common questions. These AI support chatbots pull information from databases in real-time, providing tailored responses for various requests. In one instance, a Chinese utility company’s contact center struggled with a surge in pandemic-era calls—particularly when it came to maintaining quality across languages. By rolling out a chatbot, the organization saw a 100% reduction in customer wait times and a 50% increase in self-service.

Also, Camping World, the world’s largest retailer of recreational vehicles (RVs) globally, was able to transform its customer service experience with chatbots. Following COVID-19, a customer surge revealed gaps within agent management and response times. The lack of an always available call center became an issue for Camping World, as many customer queries would go unnoticed, impacting retention. Camping World’s virtual agent, “Arvee”, helped increase customer engagement by 40% across all platforms and decrease wait times down to about 33 seconds.

For sales and lead generation, chatbots qualify prospects by asking relevant questions, recommending products based on customer preferences or pricing requirements and even process simple transactions directly within a conversation. Recently, e-commerce giants such as Amazon have implemented AI-driven chatbots as part of their sales process, helping consumers navigate deals and leading them through the purchasing journey. According to Adobe Analytics, US visitors to a retail site from an AI service were 38% more likely to end in conversion, compared with non-AI traffic sources. 

In onboarding, chatbots guide new users through setup processes and explain features. They also answer initial questions, reducing learning curves and improving early customer satisfaction. These types of chatbots have proven useful in municipal operations as well, borrowing from consumer-facing technologies to create seamless experiences for residents. In one case study from 2019, the city of Helsinki deployed chatbots and virtual assistants to answer citizen questions about six areas related to healthcare and social services. A year later, the city of Austin started a chatbot program to provide instant answers to pressing pandemic-related questions. 

Chatbots frequently assist employees with HR inquiries, IT support requests or other critical company information. These chatbots improve workplace efficiency and reduce the burden on internal support teams. One small HR team handling 600 employees in the Netherlands automated a series of FAQs for their co-workers. This process significantly reduced response time and freed HR to focus on more generative, and valuable, community-oriented work. 

Implementing an effective chatbot program requires more than deploying technology—it demands a customer-centric approach considering both business objectives and user needs. Some best practices for this process include:

Successful organizations define what victory looks like before starting a chatbot. Is the goal to reduce call center volume, improve response times or enhance customer satisfaction? Establishing measurable key performance indicators (KPIs) like resolution rate, customer satisfaction score or average handling time early helps the project stay on track.

Early in the process, it’s also imperative to audit existing infrastructure and talent to select a technological solution: Will the organization partner with a provider to build a chatbot? Will they opt for a purpose-built no-code solution? Will they develop the chatbot in-house? 

A lack of transparency damages user trust. It’s important to set appropriate expectations about what a chatbot can and cannot do, and make the path to human support easy to access during a conversation. Also, successful chatbot initiatives rely on clean, explainable data sources to foster transparency and help ensure that the information provided to consumers is accurate and bias-free. 

Not every interaction should be handled by a chatbot—providing clear triggers for human escalation remains critical. When escalating, transfer the full conversational context to human agents. This way, customers don’t need to repeat themselves, and consider proactive escalation where the chatbot offers human assistance before a customer becomes frustrated. 

If possible, and with permission, use customer data to provide a tailored experience. For instance, allow the chatbot to use a customer’s name or order history to give relevant, contextual responses. (However, it’s important to balance personalization with privacy, and to be open about what data is being used.) And, if customers are reaching a chatbot across multiple channels, it can help ensure the overall experience is consistent. Responses should align with information provided through other customer service channels to avoid contradictions. 

Create clear guidelines around—and stakeholders responsible for—what a chatbot should and shouldn’t do. This aspect is important when handling sensitive information or financial transactions. Implement safeguards and establish protocols for monitoring chatbot behavior and responding to issues. 

Chatbots require ongoing optimization. Review conversation logs regularly and update knowledge bases when policies or processes change. Proactively monitor customer feedback, and train AI models with new data to improve accuracy over time.