In today’s fiercely competitive environment, clients have increasingly high expectations around customer experience. How can practitioners deliver what is expected without having to invest heavily in additional marketing resources? A solution could be provided by artificial intelligence (AI).
First-generation chatbots are not yet appropriate for business-to-business organisations
Measuring and improving customer experience is best achieved by investing in hardwired technology that captures feedback on an ongoing basis from multiple channels, integrating survey results and other data into centralised dashboards and reports. Predictive technology can help to unlock powerful insights about how your clients feel about you and help shape your service offering.
AI technology – and the ability to embed it in customer management systems – allows practices to anticipate ‘client experience’, helping them to respond quickly and effectively to expressions of dissatisfaction, if and when they occur.
Choose the right AI
When it comes to customer service, the most common AI use case has been chatbots. A recent study by Juniper Research found that bots will handle up to 70% of online customer conversations by the end of 2023. These bots use an early form of AI that delivers pre-programmed responses to expected questions.
Machine-learning algorithms can identify tens of thousands of relevant terms and themes in seconds
Anyone who has clicked on the chat option of an insurer’s or airline’s webpage will recognise the frustration of having the same answer repeated back to you, no matter how many different ways you attempt to describe your problem. Despite their versatility, chatbots struggle to understand complex requests. As a result, these first-generation chatbots are not yet appropriate for business-to-business organisations, whose clients require a more personalised and sensitive client feedback approach.
Recently, firms have been turning to ChatGPT and other large language models (LLM) to write marketing and customer service content. However, like Chatbots, LLMs are much better at generating content than providing insight. Public LLMs have been trained on billions of data points across the internet so their summaries are generic; they will not necessarily reflect the brand and priorities of an individual accountancy firm.
Where AI works
Practitioners often say that they possess all the information they need to drive an effective commercial strategy; they just don’t know where to find it. They might have valuable client insights and data stored in emails, files, WhatsApp exchanges, folders and notebooks or shared on calls, but retrieving, collating and interpreting it is too onerous.
AI can identify themes and create mood charts, which record how clients feel
This is where AI can help firms, by collating and aggregating information from multiple sources to present practitioners with client insights. Drawing on interviews, feedback forms, operational data and unsolicited verbal and email comments, AI can identify themes and trends, and create heat maps and mood charts that record how clients feel about the firm.
AI’s strength here is in bringing all the information together, organising it, measuring it and trying to interpret it, without the need for input from the marketing or client relationship teams. The machine-learning algorithms can identify tens of thousands of relevant terms and themes in seconds. This can give firms the confidence to collect more text-based feedback without having to hire additional staff to process it.
Listening to tone is more effective than looking at content. No matter how close practitioners’ relationships are with clients, and no matter how often they speak, you won’t necessarily pick up on the significance of every conversation.
AI-powered client listening sifts and interprets data for meaning, tone, trends and expectations, and can draw insights from across all clients within a sector or practice area. This enables fee-earners to anticipate needs that their own clients haven’t mentioned yet.
It also enables firms to identify whether what they are saying about themselves in their marketing, external communications and business pitches matches what is being said about them by their clients. For example, you may measure your reputation by how you are perceived for innovation, friendliness, expertise and value for money. In feedback, if clients commend your expertise, friendliness and value for money but never mention innovation, you can address that gap in your testimonials and comments.
Even if AI can sometimes seem threatening or daunting, there are some relatively simple ways that it can be used to your commercial advantage.