10/05/2024
Finding ways to engage customers and keep them engaged has been a challenge faced by businesses for decades. With disintermediation and the continued digitisation of the customer experience eroding traditional business models, the rewards for successfully doing so have never been higher. They’re now more loyal, have more touchpoints with chosen brands, and deliver greater value over their lifetimes than ever before.
At the same time, customers are becoming harder to please. 72% of consumers say that they’ll stay loyal to companies that provide the fastest customer service (Salesforce, 2023), and 88% want brands to provide them with online channels where they can self-serve and resolve their own problems (Heretto, 2023).
Businesses are therefore turning to artificial intelligence (AI) to deliver faster, proactive, and more personalised customer experiences through customer support channels where and when they want it. We’ve seen a similar story before in e-commerce: AI has long since been implemented here to make product recommendations that match customer expectations, provide personalised limited-time deals, and offer virtual shopping assistants.
The overarching goal of implementing AI into customer-facing channels is simple—to improve the customer experience and make them aware of needs that they didn’t know they had.
AI in customer support journey
Recent studies into the customer experience have revealed a harsh truth: Consumers are ready to jump ship if their needs and expectations aren’t met.
According to Salesforce’s Complete Guide to Customer Experience, 71% of consumers switched brands at least once in the past year. This figure demonstrates that consumers have an upper hand when dealing with companies, and that doing the bare minimum is no longer enough.
Knowing where to start making optimisations to your customer support function begins with understanding how AI can help to improve the customer journey.
Personalisation: By offering a bespoke journey that caters to the unique preferences and interests of each customer, AI can easily personalise your customers’ journeys. This is achieved by leveraging existing customer data to predict support needs with great accuracy, enabling customer service teams to craft personalised solutions that resonate with customer needs.
Predictive analytics: This enables customer service teams to proactively address potential issues and tailor their support to meet the customer’s requirements before they escalate. This is achieved by using historical data to forecast future customer needs, thereby making it easier to identify when customers are likely to need assistance or recommend products and services that match their preferences.
Omnichannel engagement: AI-powered chatbots and virtual assistants can provide consistent, immediate support across a variety of channels, including websites, social media, emails, and messaging apps. This ensures customers receive the same level of service and information regardless of the platform they choose to use, offering a unified brand experience.
Customer segmentation: AI tools can segment customers into distinct groups based on demographics, behaviour, and preferences. This makes it easier for customer service teams to tailor their communication and support strategies to meet the unique needs of each segment, thereby delivering a more personalised level of support. The result? Customer service becomes more efficient, and customers feel understood and valued.
Voice assistants: Voice assistants, such as Siri and Alexa, have transformed the way customers interact with brands and offer a more convenient experience. Powered by AI, voice assistants allow customers to use natural language to communicate their needs and queries, from simple informational requests to more complex transactional commands.
Dynamic pricing: This enhances the customer experience by adjusting prices in real-time based on various factors such as demand, competitor pricing, and individual customer behaviour. Dynamic pricing allows businesses to offer personalised discounts or promotions that are specifically tailored to the preferences and purchasing history of their customer, making them feel valued and increasing the chance of a conversion.
Sentiment analysis: Sentiment analysis analyses customer feedback across multiple channels to gauge overall sentiment, identify emerging trends, and pinpoint potential issues before they escalate. By understanding the context of opinions expressed in customer feedback, businesses can proactively address concerns, adapt their services, and communicate more effectively with their customer base.
Journey mapping: This enables businesses to comprehensively understand and improve the customer journey from initial awareness through to purchase and post-sale support. By leveraging AI to analyse customer interactions and touchpoints across the entire journey, businesses can identify critical pain points and opportunities for enhancement. This holistic view of the customer experience allows for targeted improvements in areas that significantly impact customer satisfaction and loyalty.
By leveraging AI effectively across these areas, customer service teams can significantly enhance the customer journey. The deployment of dynamic pricing, sentiment analysis, and journey mapping allows for the delivery of highly personalised content and interactions, proactive support measures, and seamless experiences that resonate deeply with customers.
Generative AI in customer support
AI is a broad church, and the examples above can be categorised into various areas including general AI, language processing, and generative AI. Indeed, it is generative AI that is making the biggest waves in the AI space at the moment and will likely prove transformative for customer teams.
Since the launch of ChatGPT in late 2022, the majority of companies are now exploring how it can augment the capabilities of their customer teams. The language models upon which ChatGPT and other text-based generative AI solutions are built give these apps the power to respond to prompts with human-like text, answering relatively complex queries quickly and with ease.
This makes it a natural fit for customer support and success teams, many of which were implementing chatbot systems long before ChatGPT was released. According to a recent report by McKinsey, generative AI could further reduce the volume of human-serviced contacts by up to 50%, while applying generative AI to customer care functions could increase productivity by up to 45% of current costs.
Over time, generative AI will become more embedded in the customer service function as it matures and becomes more capable of handling queries without supervision. Source: BCG analysis.
While questions remain surrounding the accuracy of generative AI-powered chatbots and the potential for biases, these are likely to disappear over time as the technology matures. In the meantime, customer teams need to think about which use cases will deliver the most value and develop processes for analysing and refining their chatbot deployment to overcome current fears and challenges.
7 ways AI can be implemented to optimise customer support
Now that we’ve explored how AI can help to improve customer journeys, let’s look at how it can be implemented in practice to optimise the way customer support is delivered.
1. Natural Language Processing
Natural Language Processing (NLP) is transforming customer service by empowering chatbots to respond to queries in a way that closely mimics human conversation. This enhances the quality of interactions between customers and businesses, making digital communication feel more personal and engaging.
NLP allows chatbots to understand the nuances of language, including slang and colloquialisms, enabling them to provide responses that are not only accurate but also contextually relevant.
This level of understanding and interaction significantly improves customer satisfaction, as it reduces the frustration commonly associated with automated systems and makes customers feel heard and understood, thereby streamlining the support process, and fostering a positive experience.
2. Automated ticketing
AI-powered automated ticketing streamlines customer support by intelligently categorising and prioritising incoming support tickets. This system ensures that tickets are addressed based on their urgency and complexity, directing critical issues to the top of the queue.
By automating this process, businesses can significantly reduce response times and improve the efficiency of their customer support teams. This prioritisation ensures that customers with urgent problems receive timely assistance, enhancing their overall experience.
Automated ticketing not only optimises the workflow for support staff but also contributes to a more organised and effective support system, leading to higher levels of customer satisfaction.
3. Self-service portals
Self-service portals empower customers to resolve common issues on their own. By democratising access to information, troubleshooting guides, and frequently asked questions, these portals enable customers to find solutions quickly without waiting for human intervention.
This autonomy not only enhances the customer experience by offering immediate assistance but also reduces the workload on customer support teams, allowing them to focus on more complex queries.
Providing self-service options reflects a company’s commitment to customer support, improving satisfaction and loyalty by catering to the modern customer’s preference for quick and easy problem-solving.
4. Sentiment analysis
Sentiment analysis tools can be implemented to help support teams assess customer sentiment, identify trends, and pinpoint areas requiring attention. This insight enables customer support teams to proactively address concerns, tailor their approach to meet customer needs better, and refine products or services accordingly.
Sentiment analysis helps in recognising both positive feedback, which can be celebrated and built upon, and negative feedback, which provides a valuable opportunity for improvement. By leveraging this technology, businesses can enhance their responsiveness to customer feedback, leading to increased satisfaction and loyalty.
5. Predictive analytics
Predictive analytics leverages historical data to compile predictions about customer behaviour, potential issues, and needs. This capability allows businesses to proactively address concerns before they escalate, personalise the support experience, and recommend solutions that are tailored to individual customer profiles.
By anticipating customer needs and potential problems, predictive analytics helps in creating a more responsive and anticipative customer support framework, significantly enhancing the customer experience by making it feel more tailored and attentive.
6. Knowledge management
AI-powered knowledge management systems transforms the way that customer support representatives access and share information. These systems compile and analyse data from past interactions to provide quick access to relevant solutions, ensuring consistency and accuracy in customer support across various channels.
By leveraging AI for knowledge management, businesses can ensure that their support teams are equipped with the information needed to resolve queries efficiently, fostering a more informed and cohesive approach to customer service.
7. Call routing optimisation
Routing optimisation significantly improves the customer support process by analysing customer profiles and preferences to direct calls to the most suitable agent or department. This targeted approach ensures that customers are matched with agents who are best equipped to address their specific needs, reducing wait times and improving the chances of a first-call resolution.
This is particularly valuable for keeping repeat customers satisfied. By ensuring that key customers or accounts always reach the same team or person, it becomes a lot easier to efficiently handle queries as the person on the other end of the telephone will already have experience in dealing with that customer.
What about customer success?
Customer support is just one side of the coin when it comes to customer experience; customer success is the other.
Rather than focusing on solving problems, customer success teams focus on ensuring that customers achieve their desired outcomes while using a product or service. This function focuses on understanding customer goals, guiding them through the customer journey, and helping them maximise the value they get from the product or service.
Although many of the same high-level AI concepts apply to customer success—personalisation, automation, sentiment analysis, etcetera—the tools are used in different ways, and it’s important to know the difference in order to deliver a consistently strong customer experience.
Predictive analytics: Predictive analytics can be used to examine extensive customer data, just like in customer support, to forecast behavioural trends. Rather than focusing on proactively addressing customer challenges, predictive analytics in customer success instead works to do things like pinpoint customers at risk of churning and suggest customised measures to enhance customer loyalty.
Personalisation: AI in the context of customer success can analyse individual customer preferences, purchase history, and interactions to enable businesses to tailor recommendations and communications specifically to each customer. This creates a more engaging and personalised experience to not only enhances customer satisfaction but also fosters a deeper connection between the customer and the brand.
Automation: AI-powered automation transforms customer success by handling routine tasks such as scheduling appointments, sending follow-up emails, and updating customer records with minimal human intervention. This allows customer success teams to allocate more time and resources to strategic initiatives that require a personal touch, thereby enhancing the overall efficiency and effectiveness of the team.
Sentiment analysis: Customer success teams can leverage sentiment analysis to gauge customer feedback across various channels like surveys, social media, and reviews. This analysis helps in understanding customer sentiment, pinpointing both strengths and areas for improvement in products or services. Armed with these insights, customer success teams can implement targeted strategies to address concerns and amplify positive experiences.
Customer segmentation: AI algorithms facilitate advanced customer segmentation by categorising customers based on their behaviours, preferences, or demographics. This nuanced segmentation enables customer success teams to craft and implement highly customised strategies for different segments, leading to more effective engagement and a higher level of customer satisfaction.
Data insights: AI can quickly analyse vast amounts of customer data to unlock valuable insights about trends, patterns, and customer behaviours. These insights are highly valuable for customer success teams in making informed decisions and developing strategies that are both effective and aligned with customer needs.
By implementing AI technologies across these critical areas, customer success teams can significantly enhance their operational efficiency, elevate customer satisfaction, and contribute to the overarching success of the business. AI not only streamlines processes but also enables a more personalised, proactive, and data-driven approach to customer success, setting new standards for how businesses engage with and retain their customers.
AI is changing the way businesses interact with customers
Customer service and success agents can realistically only interact with one customer at a time. Whether that’s a phone call, social media message, or email, human staff are always going to be limited and therefore their time should be reserved for only the most pressing matters that AI cannot itself handle.
The solution to this, as we’ve explored, is AI. Chatbots, for example, can handle multiple queries at once, while self-service portals can enable customers to resolve their own problems much more quickly than they would be resolved if waiting on an agent to respond to them. If a query too complex for a chatbot arises, a live representative can easily step in and address the issue.
Meanwhile, advances in natural language processing, sentiment analysis, and predictive analytics are all playing their part in helping customer service and success teams to identify customer emotions and satisfaction levels from their interactions. This insight allows for more empathetic and tailored support responses.
If you want to find out more about how the latest advances in AI can be implemented to supercharge your customer support and success functions, get in touch with one of the AI experts at Getronics.
