In 2024, expectations around personalization are sky-high.
According to a recent Gartner study, 71% of B2C and 86% of B2B customers expect companies to be informed about their personal information at the point of conversation. To make matters more complicated, this preference isn’t a request – it’s a demand. In a Qualtrics survey, nearly half of respondents admitted they would consider walking away from a company after just one poor experience.
This means companies don’t really have much say when it comes to personalization. They can get it right on the first try, or it’s a toss-up if they will ever see that customer again.
It’s hard to blame the modern customer for this ultimatum, either. As automation and chatbot tools have become more accessible to companies across industries, they have exposed customers to plenty of examples of poor personalization.
Take this increasingly common use case, for example. Imagine you are a customer who needs support with a recent product purchase. You check the website for contact information, and you are prompted to begin your chat with the chatbot. After trying your best to describe the issue, you soon learn the chatbot is not equipped to address product questions. As a result, the chatbot forwards you to a live agent queue and before you know it, you are asked to start over and describe your issue and the product order to the agent again on the other end of the line. Frustrating – not to mention time-wasting.
We see this all the time. Poor CX automation strategies come in and strip away personalization, frustrating customers in the process. This can make it feel like automation and personalization are at odds – that one is not possible with the other.
This is simply not true. With the right guardrails in place, automated experiences can support a stronger, more personalized customer experience. Powerful artificial intelligence technologies, like Google Cloud’s Vertex AI, are helping take automation strategies even further – allowing these tools to communicate more effectively with customers, tap into existing CRM profiles, and of course handle more complex topics from start to finish.
As a result, the challenge for companies is less about choosing between automation or personalization and more about finding the right automation and customer journeys to make mass personalization possible.
How Vertex AI powers mass personalization
The most important aspect of mass personalization is the data. How well can AI technologies access it, where can they access it from, and how are they empowered to use it?
By answering these questions, contact centers can begin to separate the AI tools that understand the importance of personalization, from those that are simply focused on automating as many customer experiences as possible.
To give you an idea of what types of traits you should be looking for to create personalized experiences through automation, here is how Vertex AI approaches customer data:
- Full spectrum of customer data. Vertex AI combines conversation data with first- and third-party customer data to ensure every automated experience is built on a complete 360-degree view of the customer and customers like them.
- Full suite of use cases. Unlike tools that might require a single outcome, like a chatbot, Vertex AI can be deployed to create chatbots, search tools, and more thanks to Gemini – Google’s versatile multimodal AI model.
- AI focused on enterprise data. Hallucinations are always a concern with AI. But tools like Vertex AI ground responses in a company’s unique enterprise data, ensuring users can verify results across sources.
So, how does this all come together to create a personalized experience that makes sense for customers? Let’s take one last look at a real Vertex AI example not too unlike the fictional example we discussed earlier.
A large, international bank sought a way to increase chatbot conversation accuracy and personalization with customers, while also cutting costs by 30% by 2025. To do so, it needed to find a way to personalize customer advice, without allowing hallucinations.
Using Vertex AI Search and Conversation, the bank was able to find new ways to make conversations with the chatbot easier – solving more customer problems in chat, offering personalized tips, and helping users navigate the banking app more easily. As a result, the bank is seeing greater deflection rates and higher Net Promoter Scores (NPS).
In 2024, these types of experiences can become the norm. With the right tools and CX strategies, companies have an opportunity to tackle their most pressing budgetary goals – without sacrificing the personalization their customers demand.