Only with the “brain” provided by predictive and prescriptive analytics can generative AI (GenAI) offer a truly personalised service that will optimise the experience and improve loyalty.
In 2022, CCW asked consumers whether they felt their typical experiences with brands were meaningfully improving. Only 10 per cent answered positively. In 2023, the number declined to an alarming 4 per cent.
Weak personalisation is one of the biggest drivers of customer complaints. It damages the overall experience and causes suboptimal performance from efficiency, sales and retention perspectives. It also lowers customer lifetime value and is one of the most important reasons behind digital transformation initiatives not achieving their full potential. But could personalisation be improved with AI?
Today, in CX, the most common uses of AI are for bots and agent assistants. Here GenAI has made a dramatic difference, thanks to its ability to communicate in a human-like fashion, mimic empathy, understand complex intents and solve complex problems. But GenAI alone cannot provide services that are personalised to a particular customer.
To achieve this, GenAI-based solutions like bots and agent assistants need “brains” that, in the back end, analyse large quantities of data, identify patterns and provide intelligence about the next best action or next best experience for a particular customer at a certain journey moment. Only with that “brain” can GenAI offer a truly personalised service that will optimise the experience and improve loyalty.
The essence of personalisation and, consequently, superb CX is doing the right things for customers at every step of the journey. But that’s easy to say. The challenge is in knowing what the right thing for a particular customer at a specific journey point is.
Figuring this out and then propagating it to bots, agent assistants and other components influencing the customer journey is perhaps the most important question that companies need to ask themselves when digitally transforming their customer services.
Unlocking insights
To answer that fundamental question, we need data. Brands today have unbelievable real-time access to customer data and digital footprints. However, it is very difficult to interpret the vast amounts of data that the average enterprise typically collects. Humans, by themselves, simply cannot see the important insights and correlations. So, data is only part of this equation.
Here’s where AI-powered predictive and prescriptive analytics come in. Predictive analytics utilises historical data to predict future outcomes. And prescriptive analytics works with those predictions to suggest the next best step.
The result is understanding the specific wants and needs of an individual person at a particular journey moment and proposing the next best action. In this sense, the concept is not new in CX, but the new breed of AI-based analytics tools enable personalisation on steroids – or, as we like to call it, hyper-personalisation.
Getting results
The majority of enterprises already use (or are in the process of deploying) some form of predictive and prescriptive analytics solution, typically as part of, or as a supplement to, their CRM solution. But strategies are often half-baked. To get the best out of these tools, companies should use related insights to orchestrate their customers’ engagements and, therefore, offer hyperpersonalised services tailored to each individual.
For example, a GenAI-driven bot may be able to show empathy to the customer, but what does this mean without proper mitigation? If, in addition to showing empathy, the bot can also offer a creative solution, personalised for that person exactly, such as making a decision on compensation or similar, the CX will be excellent, and the interaction will be fully resolved without involving a live agent.
By the same token, if we let agent assistants simply search internal knowledge banks and present articles for agents to read to the customer, the experience will be largely the same as one provided by an automated bot. But if, by drawing knowledge from other tools, the virtual assistant supports the agent in doing the right thing for the customer, we are talking about something entirely different.
Next gen CX
Predictive analytics can also help companies to move from large outbound campaigns – which cost a lot, damage CX and have quite low success rates – to engaging micro-targeted audiences with a high probability of interest in a certain offer.
Predicting challenges before they arise and resolving issues before they happen keeps customers satisfied while simultaneously decreasing contact centre loads. Predictive analytics-driven, proactive notifications are playing a key role here, as we can see in different industries from telcos to utilities. However, this is not only about problems and issues.
Some banks use predictive analytics-powered notifications to inform customers about relevant investment opportunities and personalised wealth management suggestions. Being able to reach out to customers before they even know they need us is the next generation of customer engagement.
With that in mind, it’s hardly surprising that-forward thinking CX leaders underline predictions and proactivity when talking about the contact centre’s future; predictive and prescriptive analytics move experiences from reactions to predictions.
According to McKinsey, “Companies should make hyper-personalisation in customer care a top goal for 2024. AI-based analytics is the technology that can realise that goal.
This article appeared in our September 2024 print issue. You can read the magazine in full here.