In the age of AI, your competitive advantage is derived from the ability to obtain, analyze, and apply personalized data at scale to understand, customize, and optimize customer journeys. In this competition, the big players and the big tech companies have been the winners as they are the first to embed these capabilities in their marketing and business models. The frontrunners of the industry implement intelligent experience engines to optimize customer experiences with AI.
The Customer Experience Engines provide end-to-end solutions to assist the customer and enhance their experiences. They provide services like scheduling an appointment, finding the current location, sending reminders, providing directions, and guiding consumers through any process that would lead them to achieve their goals. This may sound fabulous but building an Intelligent Experience Engine may be time-consuming and expensive with technical complexities. The results help companies implement personalization at scale reaping immense benefits including a major upward shift in the revenue curve. In short, digital advantage supremacy has gone beyond the limits of traditional marketing to become a C-suit issue. In this article, we attempt to explore those.
What is an Intelligent Experience Engine?
The Intelligent Experience Engines are intelligent in many ways. They employ bots that are ever-improving through the dint of machine-learning algorithms that help it to figure out ways to enable the customer’s progress. The engine constantly tests, learns, and finds out newer ways to make the customer’s experience better. The engine only gets better at understanding the context of the customer and figures out who and where someone is. The result is a distinctive, positive, and seamless experience that only improves over time. Brands can build a platform that orchestrates communications to smoothen the customer’s journey through every touchpoint. An example would be a messaging platform that leverages personalization to deliver the right message at the right time to the right customer for maximum results. Now we explore the main functions of the AI engine and what strategies it should follow and execute in order to enhance the customer experience.
Understanding the Customers’ Contexts
Retail giants like Tesco and Kroger have huge data analytics team that builds algorithms engaging customers in ways that are most attractive and appealing to them. The retail giant in grocery, Eagle has gamified the shopping experience of the customers and rewards its customers through loyalty points. Eagle understands the context of the customer through the data analyzed by AI and offers customers according to their profile and preferences.
Deliver a Multichannel Experience
A multi-channel experience enhances the customer experience and fulfils the expectations of the customers as well. The behaviors and preferences of the customers and prospects across channels are analyzed for a more wholesome understanding of their tastes and preferences. This gives a 360-degree picture of the customers to deliver them an experience that appeals to them the most.
Rigorous Tests Should be Conducted
Relentless testing of customer data enables the brands to understand and gauge the preferences of the customers and prospects. The algorithm extrapolates the customers’ data about browsing, selection, and rejection of items to come to an understanding of their tastes and preferences. This information helps to assess what the customers want and what they don’t. Each interaction of the customer at every touchpoint is analyzed and the results are tallied with the inferences and tested relentlessly to understand customer’s preferences. This data is then aggregated to make decisions at different levels based on its accuracy.
The Bottom Line
The Intelligent Experience Engine that you build varies according to the experience you want to deliver to your customers. This exercise itself has stages: exploration, investigation, drawing inferences, and decision-making. At the exploitation stage, the data is structured and segregated to come to inferences through investigation. Once these three stages are complete, comes the decision-making process that inculcates the kind of experience that is expected to be delivered to the customers. The algorithms are then formulated to constantly provide the customers with the expected level of service that must be again honed and sharpened.
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