How to Start Harnessing the Power of AI in the Contact Centre

We all know that AI, particularly Generative AI like Chat GPT, will transform what contact centres do by fundamentally changing how businesses interact with their customers.

But it’s still early days, and many contact centres are struggling to get past all the hype and get started. How they are asking, can we apply the latest AI technologies in practical, useful ways?

If you’ll forgive the pun, they want Generative AI to generate more than just words; they want it to generate ROI.

The First Steps

While Chat GPT and the like are the shiny new arrivals in town, AI is about much more than chatbots. The promise of AI comes from the ability of these new models to understand language, human intent, and emotions.

However, the full power of AI is only unlocked by combining that with their capacity to analyse vast amounts of data swiftly and make connections that a mere human analyst would miss.

Before launching multiple AI initiatives, it’s essential to build a strong foundation on which you can innovate, and which gives you the means to make data-driven decisions and keep your projects on track. Your strong foundation could look like this:

• Step One: Gather data from across your business so you can analyse it to find out where you have service gaps, unmet customer needs, or inefficient processes.

• Step Two: Create a platform that allows you to bring all your customer data and market intelligence together and continuously mine them for insights.

• Step Three: Listen to what your customers say and track customer sentiment and issues in real-time to ensure appropriate actions are taken quickly.

• Step Four: Equip yourself with the capability to measure the success of your activities comprehensively and objectively in terms of how it impacts your customers.

Only once you have these solid foundations in place can you build out your use cases, such as chatbots, hybrid human/bot processes, agent-assisted technologies, and other forms of automation.

One: Unlocking Your Data

Contact centres deal with a deluge of data from sources like voice calls, emails, chats, and social media. Most of this data is unstructured, meaning it wasn’t created to fit any database’s nicely defined criteria and fields. The vastness and complexity of unstructured data make it tough for contact centres to glean valuable insights from it.

AI-driven Classification and Summarisation

AI-powered machine learning algorithms can analyse the content and intent of customer interactions, automatically sorting them into predefined categories. This enhances routing efficiency and reduces the manual effort required by agents.

LLM (Large Language Model) AIs can create concise summaries of customer interactions, highlighting key points quickly and turning unstructured comments into structured data that can be analysed. Presenting agents with summaries significantly reduces handling and wrap time and boosts productivity

Practical Applications

With these capabilities alone, you can start to deploy some handy AI tools that will help your colleagues and customers accomplish their goals more effectively:

• Build AI-generated FAQs from your unstructured knowledge data and make these available to customers for self-service or to agents to assist them when handling interactions.

• Identify intent in customer communications to improve routing and context for agents. This could include an IVR front-end on the voice channel.

• Analyse customer sentiment during live interactions to guide agents, or post-interactions to optimise the follow-up process.

Two: Hyper-Personalisation through Customer Data Platforms

One of the promises of the digital and AI revolution has been the ability to personalise all customer interactions. The local corner shop historically gave the best customer experience because the proprietor knew their customers personally and remembered their preferences.
AI can allow even the biggest business to replicate that type of relationship. All it requires is a comprehensive understanding of each customer, and for the appropriate information to be made available to the agent or process interacting with a customer at precisely the right moment.
While customer data exists in silos across different systems and departments, it is difficult to create a unified view of the customer and their journey. Customer Data Platforms (CDPs) address this challenge by integrating data from multiple sources, including CRM systems, website analytics, social media, and other customer touchpoints.

AI-Powered Personalisation at Scale

With a centralised repository of customer information, CDPs enable contact centres to build rich, 360-degree customer profiles encompassing demographics, preferences, behaviours, and interaction history.

AI algorithms can then analyse customer profiles and behaviour patterns to generate highly targeted recommendations, offers, and content. You can then tailor interactions across all channels, from website visits to email communications to contact centre interactions.

Practical Applications

Building a CDP is no small task, but it’s crucial to your ability to maximise the opportunities presented by new AI technologies. With all your customer data from multiple sources integrated, you will be able to:

• Update customer profiles in real-time for an always up-to-date single customer view.

• Enable customer-facing staff and systems to make data-driven decisions.

• Visualise and optimise customer journeys, down to each individual.

• Enable hyper-personalised communications and targeted marketing campaigns, improving engagement and results.

Three: Customer Listening

Many organisations use off-the-shelf social media listening tools designed to pick up customer chatter about their brand and products. AI models’ ability to ingest and summarise large amounts of audio and text is now giving contact centres more powerful ways to listen to their customers.

Classify, Categorise, Summarise and Route

Customer listening tools monitor your customer interactions across all channels for keywords and trigger phrases so that you can ensure appropriate assistance is provided quickly. They can automatically classify, categorise, and summarise interactions in real-time, enabling contact centres to identify and prioritise customer issues quickly.

By leveraging natural language processing and machine learning, these tools can understand a customer’s intent and route them to the appropriate agent or department for prompt resolution.

Practical Applications

By classifying, categorising and summarising interactions and intent across all channels in real-time, your contact centre will be able to:

• Collect feedback and take action to prevent customer frustration and dissatisfaction.

• Analyse and route tickets to the right agent based on customisable triage processes and service levels.

• Provide real-time data for a better understanding of the customer experience.

• Reduce handle times significantly with AI-generated prompts and responses and better routing.

Four: Better Customer Satisfaction Measurement

For years, contact centres have conducted Customer Satisfaction (CSAT) surveys following interactions. In addition to giving a simple score, these should offer decision-makers and customer-facing staff insights into customer perceptions and experiences. More importantly, the results of these surveys should be possible to interpret clearly and objectively so that you have actionable ways to change the customer experience.

Leveraging AI to Enhance CSAT Insights

AI can be used to uncover better and deeper CSAT insights by improving the reliability of data collection and conducting a deeper analysis of customer feedback. The key is to create a custom view of each customer’s journey. Combining that with the rich data you have on each customer can help explain how to improve that person’s journey.

AI algorithms can uncover patterns and correlations in CSAT data, revealing hidden trends and opportunities for improvement. More accurate and actionable insights can also be gathered by using AI to tailor CSAT surveys to individual customers on the fly and by engaging them through their preferred channels.

Practical Applications

An AI-enabled CSAT programme across multiple channels will allow you to:

• Gather feedback from customers through voice, web, and messaging channels.

• Integrate your CSAT surveys with virtual assistants and your CRM.

• Conduct surveys in multiple languages.

• Access various feedback types (NPS, text, numeric, etc.).

• Get more detailed and granular insights into individual customer journeys.

• Understand how your AI initiatives are performing and adapt accordingly.

Rolling Out AI in your Contact Centre

Once you have built a strong foundation, such as the one we’ve outlined, you can start piloting and rolling out AI initiatives, including chatbots to automate interactions and agent-assist technology to reduce AHT and improve efficiency.
As customer expectations continue to soar, those contact centres that build a strong AI foundation and invest in the right technologies will be well-positioned to innovate and grow.

You don’t have to do everything yourself, of course. Working with a technology partner who is an expert in the field of CX can help you skip two years of R&D, as the partner has already done it.

Take a look at the AI technology solutions that have been powering Ventrica’s AI journey for the last few years—and which we’re now making available for you to use in your contact centre.