Using Generative AI to transform your Customer Experience
13 Jul 2023
9.5 min read
Customer Experience (CX) is a really huge field to navigate for brands, and finding ways to maximise it allows brands to better acquire new customers and retain existing ones. Business leaders play a crucial role in driving the adoption of generative AI for customer experience, recognizing its potential to transform digital interactions and deliver strategic value. The challenge that digital teams face is creating an online experience that has the right balance of efficiency but also creates room for a personalised experience. This is a challenge that AI can help solve. Incorporating AI into CX strategies enables organizations to address this challenge by enhancing personalization and operational efficiency through real-time, AI-driven support.
An individual customer experience sits somewhere on a spectrum. At one end, customers just want to be left alone and find the answer themselves, while at the other are customers who need their hand held and want personal interactions. The challenge is that a customer can switch places along the spectrum depending on what they are looking to do. But also they can move along the spectrum in one website visit: starting off wanting to be left alone, get frustrated that they can’t do what they need to do, and suddenly need help. AI can be the bridging gap between the extreme ends of this spectrum. Modern generative artificial intelligence, built on large language models and tailored to the modern retail experience, can transform the customer experience. Organizations can integrate generative AI into their CX strategies by selecting AI-agnostic solutions, ensuring compatibility with LLMs, and focusing on customization and scalability for greater impact. Brands can also leverage AI, including generative AI and chatbots, to deliver more personalized and efficient customer experiences, gaining a competitive advantage in their service operations. By helping customers at the “Leave me alone” end when they get stuck, but also solving issues faster for the “Hold my hand” customers. More specifically, it can do this by:
Smoothing purchase journeys, allowing customers to easily find answers they need
Making intelligent product recommendations
Speeding up customer service interactions
Answering, but fully resolving customer service queries
Personalized experiences are most effective when brands understand the customer's perspective and use customer’s data, history, and behavior to tailor interactions and recommendations.
Let’s explore some of these areas below.
Understanding Customer Data
Understanding customer data is at the heart of delivering outstanding customer experiences. Every interaction a customer has with a brand—whether it’s a purchase, a website visit, or a support request—generates valuable data. This includes purchase history, browsing patterns, and demographic information, all of which can be harnessed to create more personalized interactions and boost customer satisfaction. By analyzing customer data, businesses can spot trends and anticipate customer needs, allowing them to offer proactive support and tailored recommendations. This not only streamlines customer service operations but also helps reduce operational costs and foster greater customer loyalty. Furthermore, customer data is essential for training generative AI models, enabling them to provide accurate, context-aware responses to customer queries. When businesses leverage customer data effectively, they can transform routine interactions into memorable experiences that keep customers coming back.
The Role of Customer Feedback
Customer feedback is a powerful driver for enhancing customer experiences. It offers direct insight into what customers value, their preferences, and where they encounter friction along their journey. By gathering feedback through surveys, social media, and contact centers, businesses can pinpoint areas for improvement and make informed decisions to refine their customer service operations. Analyzing customer feedback helps organizations optimize the customer journey, address pain points, and ultimately increase customer satisfaction. Importantly, this feedback can also be used to train AI models, making them more adept at understanding and responding to customer inquiries in a personalized way. Companies that actively listen to and act on customer feedback are better positioned to delight customers, build lasting customer loyalty, and drive sustainable business growth.
How Generative AI Works
Generative AI represents a leap forward in how businesses interact with customers. At its core, generative AI uses advanced machine learning techniques to analyze vast amounts of customer data—including historical data, purchase history, and customer behavior—to identify patterns and generate human-like responses to customer queries. These AI systems are trained on large language models, enabling them to understand natural language and provide personalized recommendations or solutions. By automating routine tasks, such as answering common customer queries, generative AI frees up human customer service agents to focus on more complex issues that require a human touch. Additionally, generative AI can predict customer behavior, offer tailored support, and optimize customer service operations, all of which contribute to higher customer satisfaction, improved operational efficiency, and reduced costs. By integrating generative AI, businesses can deliver faster, more relevant responses and elevate the overall customer experience.
Smoother customer journey purchase journeys
For customers who want to be left alone, one issue is helping them find the information that they need. However much a customer wants to be able to do their own research, it can be tedious sifting through information on a PDP (Product Details Page) to find a simple answer. Self service options powered by AI tools can empower customers to quickly find the information they need without leaving the page, improving their overall experience. Then, if a customer has an issue about shipping, or returns, then most likely they will have to navigate off the PDP to find an FAQ page that answers that particular question. Having to move off the PDP could derail the customer journey. If instead, the customer can stay on the page and ask a question directly to an AI-powered chatbot, this journey can be kept on track. This chatbot can use generative AI to find, and then summarise the information kept on the relevant FAQ page. Virtual assistants can further enhance customer interaction by providing immediate support and managing simple queries efficiently. For example, AI tools can improve response time for customer queries, ensuring that customers receive quick and accurate answers.
Making intelligent product recommendations
Another more advanced use case that is possible is using AI to make product recommendations. Being able to process and “understand” the information in a product description, or some other information sheet, the AI can relay product features back to the customer. Deep learning algorithms can analyze customer preferences to deliver personalized service, ensuring that recommendations are tailored to individual needs and interests.
Say a customer is looking at a range of products, but can’t work out which is the right one for their use. For Home and Garden retailers this could be a customer looking for the correct tool for a project, for electronics it could be understanding which computer screen meets their requirements, or for sportswear it could be which shoes suit flat feet most.
Taking this last example, using product knowledge – essentially the same content that an agent would be trained on – the AI could respond to this query with an appropriate answer and recommend the right shoe, if one existed.
Going a step further, an AI can even be trained to give more opinion-based recommendations. Using a customer’s order history, what other customers have ordered, or even following what human agents have recommended can allow AI to make recommendations on the right outfit to wear for an occasion, or to suggest complementary items. Predictive analytics and data from previous interactions can further refine these recommendations, helping the AI anticipate customer needs and improve the overall customer experience.
Speeding up customer service interactions
Speed is one of the key components that underlies a good customer experience. If doing something takes too long then a customer will get frustrated and possibly leave a website and go elsewhere.
So, if a computer can do the same task as a human, then it can do it faster. Just as a calculator can perform arithmetic operations faster than a person, if a customer service AI can locate an order, it can do it quicker than a human. In a contact center, conversational AI tools can significantly reduce response times by automating routine inquiries and allowing human agents to focus on more complex issues. If you're interested in how metrics such as First Contact Resolution are impacted by AI in the contact center, learn more.
For an agent to be able to locate an order with the order number, they need to look it up in an Order Management System (OMS), find which carrier the order is with, locate the tracking number or link, process the information and then relay that back to the customer.
An AI can be integrated with the OMS and carriers, and using the technology underlying AI (intent detection, summarising information) then this process can be massively accelerated. Real time data enables AI to provide faster and more accurate responses by instantly accessing and analyzing the latest information.
For a customer this means the difference between waiting for an agent to come online, pick up this ticket, and then do the steps outlined above. Whereas with AI it can be a matter of seconds to get the same information. AI can also automate tasks such as data entry and order lookups, further improving efficiency and streamlining customer service operations.
Fully resolving customer service queries
This is the key point. Being able to answer questions accurately is a very good task for an AI to perform, but what will ultimately make the biggest difference to both the customer experience and the brand performance is resolving tickets. The examples given so far are all examples of where an AI can resolve queries. Either by giving the right, relevant information, providing credible recommendations, or successfully locating orders. With deep integrations into backend systems, AI can do a lot of what customer service agents can do. At DigitalGenius we have customers who are using our AI to:
Expedite late orders from the warehouse
Order replacement items for missing or damaged products
Generate returns labels Issue refunds early, when a return is in transit
Raise complaints with carriers
Proactively inform customers about late or missing shipments, and give them replacement or refund options
Refund shipping costs for late orders
AI agents and generative AI tools play a crucial role in streamlining customer service operations by automating repetitive tasks. This automation allows the customer service team to focus on more complex issues that require human expertise and empathy.
All without a human getting involved in the action. These actions are performed faster than human agents and round the clock, with human-like responses that can be personalised based on who the customer is, meaning that some customers don’t even know they’re talking to a bot. Maintaining customer trust is essential when deploying AI-powered solutions, ensuring transparency and positive customer relationships.
Measuring the Success of AI Powered Solutions
To ensure that AI-powered solutions are delivering real value, it’s essential to measure their success using clear metrics. Key performance indicators such as customer satisfaction, net promoter score, and response times provide a snapshot of how well AI is enhancing customer experiences. Businesses should also monitor cost savings, operational efficiency, and customer loyalty to assess the broader impact on customer service operations. AI powered tools can further analyze customer sentiment, preferences, and behaviors, offering deep insights into evolving customer needs and expectations. By regularly tracking these metrics, organizations can identify opportunities for improvement, fine-tune their AI-powered solutions, and ensure they consistently deliver exceptional customer experiences that drive loyalty and growth.
Best Practices for Implementing AI Solutions
Successfully implementing AI solutions starts with a deep understanding of customer needs and expectations. Businesses should identify the areas where AI can add the most value and build a robust data infrastructure to support their AI powered tools. Transparency and accountability are key—customers should know how their data is being used, and AI-driven decisions must be fair and explainable. Investing in ongoing training for service teams ensures they can work effectively alongside AI solutions, combining the efficiency of automation with the empathy of human interaction. By following these best practices, businesses can maximize the benefits of AI powered solutions, deliver exceptional customer experiences, and build lasting customer loyalty.
A transformed customer experience
At its heart if we look to improve the customer experience, what we want to do is to be able to make everything easier for the customer, and allow them to have a more joyful and less frustrating experience. AI can amplify the human connection by enabling more meaningful customer interactions, making each engagement more personal and proactive.
If customers complain about customer service saying they just want to speak to a human, what they mean is that the technology is not helping them. We’ve seen this before with deflection tactics where the answer is buried in an FAQ somewhere, or there is a poorly built chatbot, or automated phone service that takes them around in circles and eats up time.
But what if you could speak to a chatbot that could understand your problem and actually solve it, and could do it faster than a human? And crucially if it couldn’t help, it would pass you straight over to a human agent who could. AI technology and new technology are transforming the work of sales reps and agents by automating routine tasks, providing data-driven insights, and improving agent performance through real-time feedback and monitoring.
For those human agents, this solution means fewer tickets overall and fewer repetitive and boring tickets to handle. In exchange it means more interesting and challenging tickets where they can actually use their skills to provide a better experience. CX leaders are driving the adoption of AI in customer experience strategies, recognizing its value in transforming operations and meeting evolving customer expectations.
That’s what this new generation of AI can achieve. Faster resolutions, more personalised responses, and happier customers and agents. AI in customer experience enables more natural customer conversations, enhances customer experiences, and ensures consistent brand interactions. For example, one company successfully implemented a large language model to improve customer interactions, resulting in reduced call volume and better service quality.
Book a demo with our team to find out more.
