How AI is changing the face of customer experience 

Providing customer support was once considered a cost to doing business, and the technology to support it, such as Interactive Voice Response systems, was designed with the purpose in mind of increasing efficiency and lower expenses. Now, with the rise of AI, the conversation has been elevated from providing a duty of care, to enabling a better customer experience that has tangible benefits to the bottom line.

This shift comes at a time when customers’ expectations are also on the rise, especially in competitive industries where service can be a huge differentiator. In a report from technology company Genesys¹, based on a global survey of over 1,900 consumers and 1,300 businesses, it notes: “Managing all aspects of customer experience, from initial engagement with a prospect through to customer support and renewals – across the entire customer journey – is critical to remain competitive.” 

‍

How AI and machine learning is enabling customer experience 

AI and machine learning is coming of age, and the past 12 months has seen an explosion of activity in the area of Generative AI (GenAI), fuelled by Large Language Models (LLMs), and Natural Language Processing or Understanding (NLU), to improve the customer experience. Primary applications include enabling businesses to proactively address issues to improve customer perceptions and satisfaction, and understanding what a customer requires when they make contact and routing their enquiry to the most appropriate agent.

New Zealand has a growing number of companies leveraging AI and machine learning to enhance customer experience. Cloud-based accounting platform Xero is integrating these capabilities to provide smarter financial insights for businesses, and helping their customers manage invoicing, expense tracking and payroll more efficiently.

Meanwhile ASB Bank has an AI-powered chatbot 'Josie’ that assists customers with banking tasks, such as checking balances, transferring funds, and managing accounts, to help enable a seamless and more convenient banking experience.

‍

Rise of omnichannel marketing

Many businesses already have rich information from their web and app journeys that help them run targeted marketing campaigns and offers based on segmented groups of customers. However, AI and ML has taken this to the next level in one area in particular – omnichannel marketing.

Omnichannel marketing aims to deliver customers an integrated experience across all of the business’s channels and touchpoints. With AI this experience moves from sharing context across channels to managing the customer’s entire end-to-end journey with a brand, including interactions with the website, web chat, and mobile app, as well as social media, email, phone calls, and visits to a physical store.

According to the Genesys report¹,

“true omnichannel journeys let customers start, pause and resume their journeys because the business is prepared to provide that personalised, contextual experience. When you can retain a customer, their journey with you never ends.”

- Genesys report¹

‍

Predictive engagement

In addition to providing seamless customer care, omnichannel marketing provides businesses with an opportunity to offer specific products and services that are targeted, relevant, and consistent with the customer’s personal experience with the brand. 

At Spark, our AI-powered omnichannel marketing engine, 'BRAIN', can analyse customer data from multiple sources, and channels, to create a holistic view of every customer’s journey, behaviour and preferences. This enables the generation of highly personalised content and product recommendations that help boost sales and improve engagement.

‍

AI to enhance human interactions

Spark's approach to building conversational chat bots, and other AI tools, involves using trained specialists in conversational design, and taking human-to-human dialogue as the starting point for building an automated journey. We also ensure there is a way for customers to escalate to a human agent to assist, if they find dealing with a chatbot frustrating.

It’s worth remembering that effective AI solutions don’t yet replace agents, instead they enhance their ability to provide a better customer experience. AI solutions can assist by providing just-in-time information to help agents better handle the customer interaction, or surface knowledge base articles based on what the customer is asking for. Following the call, they can provide a GenAI summarisation of an interaction, reducing the time taken by an agent to update the CRM. Emerging capabilities include mining of unstructured data, aimed at identifying automation opportunities, looking for agent training gaps and creation of AI-driven guided flows, to further assist agents in their customer conversations.

‍

Taking care with customer data

As AI and machine learning rely on large amounts of customer information to be effective, businesses must ensure that they handle this data responsibly, comply with local regulations, and maintain transparency regarding data collection and usage to ensure they maintain customer trust. It’s also important to understand where the source data used in your LLM’s is coming from. The emerging “Constitutional AI” concept, by Anthropic, aims to merge legal principles with AI systems, ensuring they adhere to national ethical guidelines, to make AI helpful, harmless and honest.

While some customers will be delighted by the personalised service they are being offered as a result of AI initiatives, others may not be so welcoming. The Genesys report¹ notes that in its survey, 64% of millennials value anticipation and customisation of the experience using their transaction data over privacy concerns, compared to 45% of Baby Boomers (age 55 and over) who value privacy over personalisation.

‍

Getting started with using AI to enhance the customer experience

Successful implementation of AI to enhance the customer experience requires collaboration across different functions within an organisation. Brand, Marketing, IT, customer service, and data analytics all need to work together to ensure that AI initiatives are integrated seamlessly into all customer touchpoints. It can be detrimental to have siloed teams building capabilities for one channel, and not providing a unified customer experience across all touchpoints.  

In its report Genesys¹ noted that 45% of businesses it surveyed manage the customer experience through their customer care team, and 30% through marketing, while only 19% have a dedicated customer experience team.  

Successfully implementing AI solutions can require specialised skills in data science, machine learning, software engineering, and domain knowledge. Many businesses may struggle to find the necessary expertise to develop and deploy their own AI-driven customer experience solutions. This is particularly true for small and medium-sized businesses with limited resources. However, AI capabilities are becoming more mainstream, with many SaaS providers incorporating them into their solutions, making it much easier to leverage the benefits that AI brings.

Research firm Gartner® recommends, in its recent report² ‘Overcome 3 Pitfalls When Implementing GenAI in Customer Service’ that businesses: “focus more on developing robust processes and the skill sets that your people will need while outsourcing the technical considerations of GenAI capabilities to your technology vendors.” 

“They have a backlog of problems that they have been trying to solve for years, with a focus on the most common and costly ones. They have developed not only the GenAI component but also the application framework in their products to manage the risks and contextualise the prompts to support customer service activities,” the Gartner, Inc. report states. 

Partnering well enables companies to avoid creating a solution that doesn’t solve a problem, that teams aren’t ready for, and which has the potential for bias. 

It will also give you the confidence to get started on your AI journey to a transforming your customer service operation. Once you take the first small step with a pilot project, you can iterate from there, and scale to a solution that drives down costs while greatly improving the experience for your customers - and the agents who interact with them everyday.

ABOUT THE AUTHOR

James is a Principal Consultant in Spark with over 25-years’ experience in Contact Centre and CX. James works with enterprise and government customers to deliver innovative solutions that elevate customer and agent outcomes and remove friction.

¹State of Customer Experience report by Genesys. Research is based on a global survey of 1900 consumers and over 1300 business across North America, Europe, Latin America and Asia Pacific. ^‍

²Gartner, Overcome 3 Pitfalls When Implementing GenAI in Customer Service, David Norrie, 2 November 2023.  

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.