Only with the “brain” provided by predictive and prescriptive analytics can GenAI offer a truly personalized service that will optimize the experience and improve loyalty, writes Avaya’s Tvrtko Stosic.
In 2022, CCW asked consumers whether they felt their typical experiences with brands were meaningfully improving. Only 10% answered positively. In 2023, the number declined to an alarming 4%.
Weak personalization 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 digital transformation initiatives do not achieve their full potential. But could personalization be improved with AI?
Today, in CX, the most common uses of AI are for bots and agent assistants. Here, generative AI (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. However, GenAI alone cannot provide personalized services to a particular customer.
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To achieve this, GenAI-based solutions like bots and agent assistants need “brains” that, in the back end, analyze large quantities of data, identify patterns, and provide intelligence about the next best action or best experience for a particular customer at a certain journey moment. Only with that “brain” can GenAI offer a truly personalized service that will optimize the experience and improve loyalty.
The essence of personalization and superb CX is doing the right things for customers at every step of the journey. But that’s easy to say. The challenge is knowing what is right for a particular customer at a specific journey. Figuring this out and then propagating it to bots, agent assistants, and other components influencing the customer journey is perhaps the most important question companies need to ask themselves when digitally transforming their customer services.
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 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 utilizes historical data to predict future outcomes. Prescriptive analytics works with those predictions to suggest the next best step. The result is understanding an individual’s specific wants and needs at a particular journey moment and proposing the next best action. In this sense, the concept is not new in CX. Still, the new breed of AI-based analytics tools enable personalization on steroids—or, as we like to call it, hyper-personalization.
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Most 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 offer hyper-personalized 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, personalized for that person exactly, such as deciding 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.
Predictive analytics can also help companies 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.
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Predicting challenges and resolving issues before they happen keeps customers satisfied while simultaneously decreasing contact center loads. Predictive analytics-driven, proactive notifications are playing a key role here, as we can 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 personalized wealth management suggestions. Reaching out to customers before they 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 discussing the contact center’s future; predictive and prescriptive analytics move experiences from reactions to predictions. According to McKinsey, “Companies should make hyperpersonalization in customer care a top goal for 2024. AI-based analytics is the technology that can realize that goal.
