Predictive Customer Insights through AI
I recently heard a CMO claim that with artificial intelligence (AI), he expects to predict what a customer wants before the customer even knows they need it. Is this hubris or rational foresight?
It sure is a compelling notion, isn’t it? One where we are in constant pursuit to streamline product and development, production, and increase profits. The same goes for service industries. As companies gain greater resolution and accuracy in understanding customer needs, they simultaneously gain greater advantages over the competition, leading to longer customer retention and higher lifetime value.
The Quest for Anticipating Customer Needs
So, when an executive wants to know a customer’s needs and desires even before that customer does, it’s a fair question to ask “how? Broadly speaking there are two main schools of thought in crafting exceptional customer experiences. One is based on implicit customer data, the other is on explicit customer data.
Customer Experience Management (CEM)
Customer Experience Management (CEM) is one approach that employs surveys and sentiment analysis to generate customer insights. Several tech titans have based their business on collecting and analyzing customer surveys. This makes use of fundamentally implicit data in terms of responses that are often self-reported. With this methodology, we evolve and modify products and services based on customer’s assessments after experiencing those products, processes, and services. While valuable, the flaw is thinking the customer knows what they want and a satisfaction survey will capture this information accurately.
Customer Experience (CX) and Data Utilization
Customer Experience (CX) by contrast, is the process by which we collect explicit data to reimagine the customer experience through AI, automation, systems, and process integration and innovation. This involves an accurate mapping of the customer journey followed by a well-implemented CX system and process that gather real-time behaviors, reactions, and choices, and then feed those data points into AI and deep learning-based systems that modify the customer journey in real-time based on the collective inputs from all customers.
Practical Example of Data-Driven Decision Making
A simple way to illustrate the difference is to consider something like a color choice for a car. A customer may be asked “What is your favorite car color?” to which she may respond, “red.” It’s an assumption. But that same customer may browse a car manufacturer’s website and consistently choose to preview a car model in the color silver. With enough consistent action, her explicit data may indicate silver to be a preferred color. For instance, that data combined with the selection of all customer browsing actions is used to predict the most appealing color presentation as the default.
Integrating Automation, Integration, and Innovation
To reimagine the customer experience and create long-lasting differentiation, both approaches are necessary to create the ultimate customer and employee experiences. Both can be aligned through the application of three key experience pillars:
- Automation - Experience Automation maps the customer journey and related behaviors and then identifies opportunities for process and product improvement within systems and organizational structures. Those systems can then be streamlined by unifying data and processes with workflow automation, intelligent applications integration, and artificial intelligence.
- Integration - Breaking down silos of operation and taking a more holistic approach to all customer interactions and touchpoints reveals cross-functional strengths, weaknesses, and opportunities. The future of outstanding delivering customer experience is to leverage every part of your organization, not just the customer-facing functions.
- Innovation - Today, organizations must respond with ever-increasing speed to new opportunities in emerging markets, evolving behaviors and preferences of customers, new technologies, and competitive threats. Embracing this pressure to innovate by reimagining processes and customer experiences, delivered through the latest technologies creates new value and revenue streams. This means integrating business systems with client-adopted technologies (smartphone, home automation, etc) into a separate customer experience layer. One that is constantly evolving to stay on your customers’ journey.
Conclusion: Building a Responsive and Predictive Customer Experience Platform
To compete effectively in a customer-centric world,companies must be so responsive to customer needs as to appear to know what they want before they do.The new platform for developing that insight and responsiveness is the result of combining the three experience pillars and the strategies of CEM and CX. With this platform approach, companies across industries can revolutionize their customer experience, build lasting competitive advantage, and develop invaluable foresight.
