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The Next Step in CX: Ambient Experiences

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In the highly competitive world of today’s experience design, practitioners know that good customer experience is expected, if not demanded. Despite the billions of dollars and millions of hours of effort in crafting the ultimate customer journey, the never-ending treadmill of customer expectations has led to an insatiable appetite for unique and personalized experiences. The result: Customers expect enterprises and organizations to know them well enough (without violating their privacy) to deliver real-time experiences that are automatic, natural, and unique, and right-time interactions that are contextually relevant.

Consequently, this shift in emphasis from experiential systems to mass personalization at scale requires a transformation in journey design from agile and flexible to intention-driven. This means ambient experiences that naturally happen in the background based on context, choices, and anticipatory analytics. Intention-driven design deliberately and explicitly empowers customers to move from contextual scale to individual scale and craft their own highly personalized and sentient user experiences. In fact, these experiences must be natural, intelligent, adaptive, and automatic.

Powering these ambient experiences is a combination of smart services that rely on artificial intelligence (AI); apply journey orchestration; pull context from the Internet of Things (IoT) and other sources; and deliver experiences in mixed reality and physical reality. AI-driven smart services not only deliver on customer experiences but also create digital feedback loops for employees, suppliers, partners, and machines (see figure).

Because AI-driven smart services require offloading the decision-making responsibility to atomic-driven smart services, the foundation of any AI-driven smart service is trust. Below is a breakdown of how the five key components of AI-driven smart services orchestrate trust.

  1. Digital footprints and data exhaust use AI to build anonymous and explicit profiles. Every individual, device, or network generates information, digital footprints or “exhaust” that can come from such disparate sources as a network IP address, facial analysis, or even one’s walking gait. Using AI and cognitive reckoning, smart services can analyze patterns and correlate identity, recognizing and knowing individuals across contexts.
  2. Immersive experiences enable a natural interaction. AI-driven smart services employ context, content, collaboration, and multiple channels to deliver immersive and unique experiences to each individual. The services will use context attributes such as geospatial location, time of day, weather, heart rate, and even sentiment—combined with what the service knows of our identity and preferences—to improve relevancy and deliver the appropriate content. Sense-and-respond mechanisms will enable collaboration among participants and machines through conversations and text dialogues. Channels include all interaction points—mobile, social, kiosks, and in-person. The goal is natural user experiences based on identity.
  3. Mass personalization at scale delivers digital services. Anticipatory analytics, catalysts, and choices interact to power mass personalization at scale. With anticipatory analytics, customers will “skate where the puck will be.” Catalysts provide offers or triggers for responses. Choices allow customers to make their own decisions. Each individual or machine will have unique experiences in context depending on identity, historical preferences, and needs at the time. From choose-your-own-adventure journeys, context-driven offers, and multi-variable testing on available options, the AI systems offer statistically driven choices to incite action.
  4. Value exchange completes the orchestration of trust. Once an action is taken, a value exchange cements the transaction. Monetary, non-monetary, and consensus are three common forms of value exchange. While monetary value exchange might be the most obvious, non-monetary value exchange (including recognition, access, and influence) often provides a compelling form of value. Meanwhile, a simple consensus or agreement can also deliver value exchange—e.g., the validity of a land title, or agreement on a patient treatment protocol.
  5. Cadence and feedback continues an AI-powered learning cycle. Powered by machine learning and other AI tools, smart services consider the cadence of delivery: one time, ad-hoc, repetitive, subscription-based, and threshold-driven. Using machine learning techniques, the system studies how the smart services are delivered and applies this to future interactions and facilitates the digital feedback loop.

Successful AI-driven smart services will augment human intelligence just as machines augmented physical capabilities. AI-driven smart services play a key role in crafting and addressing the never-ending battle to meet customer expectations by reducing errors, improving decision-making speed, identifying demand signals, and predicting outcomes. 

R “Ray” Wang is founder, chairman, and principal analyst of Constellation Research. He is the author of the business strategy and technology blog A Software Insider’s Point of View. His latest best-selling book is Disrupting Digital Business, published by Harvard Business Review Press.

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