Today’s customers have become accustomed to having their way. They are highly informed, motivated, and won’t hesitate to head elsewhere if their needs aren’t met. That’s part of the reason that retailers and businesses in just about every sector have made personalization the cornerstone of their customer engagement plans. The question, however, is how to deliver personalization at scale without breaking the bank.
Fortunately, modern businesses have some high tech help in their personalization efforts. As the importance of personalization has grown, so too has the availability of artificial intelligence (AI) tools that make delivering customized and personal experiences to customers. Here are some of the ways that AI can be used right now to drive personalized customer experiences, and where it may turn up next.
Targeting Using Up-to-the-Minute Personas
The interesting thing about AI-powered personalization is that it seems to be the one area where the average person doesn’t mind sharing their data to help companies they do business with to do a better job delivering the experience they expect. That means businesses should have no trouble soliciting as much customer data as is necessary to build detail-rich personas that allow for hyper-targeted marketing. Today’s AI-powered marketing tools have the power to analyze customer data relating to context, values, and behaviors in real time to create one-off marketing messages delivered for maximum effect.
Consider, for example, the ability to trigger a customized email offer for a product a customer has been researching online based on the customer’s proximity to a retail location, as determined by their phone’s location. With the rich and varied data sources now available, the possibilities are endless.
Delivering Knowledge
AI tools can also be valuable for front-line customer experiences, in addition to operating behind the scenes. That’s what has been behind the rise of AI chatbots over the past year because they make it possible for even a small customer service operation to engage with customers on-demand. That ability alone has turned previously hours-limited businesses into 24/7 operations, all without any additional labor costs. The uses for AI in customer engagement don’t end there, however. AI systems can also be used to assist customers with the research they need to make informed purchases, which is a valuable part of helping them feel in control of their journey. An excellent example of this is the IBM Watson-powered Rosi. It’s an AI-driven digital gemologist that can help customers compare and contrast the qualities of diamonds that they own versus ones currently on the market. It gives customers access to a wealth of knowledge in an industry driven by insider information, thereby transforming it into a customer-driven sales experience.
Recommendation-Driven Sales
Of all of the ways that current-generation AI tools may be used to enhance customer personalization, it is product recommendations that may be the most valuable to businesses. It’s the secret weapon that has made companies like Netflix, Amazon, and Spotify market leaders and trend-setters. Amazon, for one, uses a sophisticated AI-powered recommendation engine that outperforms the competition by up to 60%. While the average business won’t have the financial wherewithal to surpass Amazon’s results, tech giants like Google now make their AI technology available as a service so any business can build a recommendation engine that suits their product lines perfectly.
Looking Ahead
In a fast-developing technology field like AI, it’s difficult to pin down what the next major leap forward will be. As it pertains to sales and marketing, however, it’s not impossible. As AI systems continue to develop, they will rely more on predictive analytics and adaptive data collection to shape outcomes. That means that businesses will be able to automate almost every part of their customer experience initiatives and let their AI tools do the rest. Next-generation AI will be able to refine operations, not only by observing current results and waiting for new instructions but by formulating new questions to pose straight to customers.
For example, a marketing AI might decide on its own to ask a consumer when their sibling’s birthday is to match behavioral patterns it had previously observed. In that way, businesses will have access to a learning system that will end up knowing customers better than was ever possible before. That will pay dividends for all involved, with businesses seeing rising sales, and consumers having unprecedented personalization that gets better with every interaction.
