AI Propels the Dawn of a New Customer Experience Era

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Improving customer experiences demands an approach that takes into account all of the tools, processes, and data across the customer journey. The complex process usually involves dynamically maintaining a single source of truth about each customer to drive personalized experiences based on individual preferences and behaviors. However, businesses today have primarily invested in systems of record, such as legacy customer relationship management (CRM) and enterprise resource planning (ERP). While these systems are critical for managing internal operational processes, they are typically not effective for today's pace of business change.

Companies must complement systems of record with systems of engagement that are more agile and intelligent to provide frictionless customer experiences. Systems of engagement (tools and applications specifically designed for automating customer-facing processes) can be tied to systems of record so that existing intelligence isn't lost, but can also be leveraged in ways that enable, rather than inhibit, good customer experiences. These systems of engagement need to be able to take advantage of all this additional customer interaction and the data generated from it in systems of intelligence. That helps businesses keep pace with the smartphone-toting, social-media-posting, empowered customer.

Advancements in artificial intelligence driven by machine learning algorithms are at the heart of next-generation customer experience initiatives (figure 1). AI is the glue delivering contextually relevant experiences, powered by newfound data. Systems of record coupled with systems of intelligence can make processes more efficiencient. When combined with engagement-driven applications, they can create actionable insight. Engagement-driven applications paired with systems of intelligence will ensure more dynamic experiences. But looking ahead it is clear that the next stage of customer intelligence platforms will leverage the power of all three and be driven by AI.

Figure 1 Artificial Intelligence is the Glue for Process Efficiencies, Dynamic Experiences and Actionable Insight

Since the universe of what is "knowable" about customers is expanding, new machine learning technologies help us to see further and deeper to improve business decisio- making. Data plays a powerful role in improving the context of interactions. Users aren't limited to what they themselves discover. Combining human expertise with machine intelligence can be a powerful combination, but human interpretation alone can miss contextual clues, since the data sets are so huge. According to 451 Research's VoCUL data, 80 percent of businesses say that machine learning for automated contextual recommendations is important to creating personalized customer experiences.

Insight-driven experiences require customer intelligence platforms that combine both first- and third-party data and adds a layer of predictive machine learning intelligence to achieve real-time one-to-one capability (ideally in less than 20 milliseconds). The deeper data and improved algorithms now available let users factor in individual affinity, segment, and survey-response data along with overall intent. The result is greater relevance and effectiveness.

Information, Identity and Insight Drive New Customer Intelligence Platforms

Turning data into meaningful intelligence is crucial. Data-driven individual experiences require information that is updated constantly (e.g., transactions, events, contexts, interactions, and behaviors) and tied to a unique identity for each customer to build complete customer profiles. Then that information and identity must be turned into prescriptive insight using machine learning-based algorithms to identify customer opportunities and determine how to best engage with customers across multiple channels and devices.

Machine-learning algorithms can self-learn to adjust or adapt based on any factor or combination of factors in each individual visitor's personal interests/preferences. While algorithms can be based on a visitor's behavior or geolocation, it can also be based on key company variables, such as inventory levels and manufacturer incentives. Figure 2 shows a variety of ways businesses are considering machine learning and analytics for intelligent automation.

Figure 2 New Requirements for Intelligence and Prescriptive Insight

Source: 451 Research, VoCUL 2H 2016

Among our survey respondents, 46 percent are prioritizing the ability to improve application awareness of context and presence for improved business processes. Application awareness is primarily used to maintain information about connected applications to optimize the process. An important element is embedding intelligence to understand contextual relevance to the application. The goal is to use patterns so the application understands and adapts based on a variety of conditions.

This in turn enables businesses to create more intelligent processes. Intelligent business processes also demand automated insight on unstructured data with embedded analytics for prescriptive guidance. That's because customers don't communicate using the kind of data that neatly sits in relational database tables; they email, tweet, call, or send pictures, and that needs to be analyzed too. So 43 percent of respondents prioritize using machine learning with unstructured data, and 36 percent are also interested in embedding analytics into business applications.

New Approaches to Create Contextual Experiences

Business applications are becoming more intelligent to take advantage of the abundance of data growth within organizations or third-party services. Context-aware, process-driven applications are the future. There are many vendors embedding machine learning for contextually relevant experiences – Adobe with Sensei, with Einstein, Oracle with Oracle Adaptive Intelligent, SAP with Leonardo, IBM with Watson, Microsoft with Cortana, SugarCRM with Candace, OpenText with Magellan, and IPSoft with Amelia, just to name a few.

There are also many customer experience start-ups using machine learning algorithms, natural language processing, and speech technologies. Companies such as Allsight, Amplero, NGdata, Quaero, and Reltio offer new customer data platforms and approaches that typically combine structured and unstructured first-, second-, and third-party data to continuously synthesize, learn, adapt, improve, and automate in real time, relating and linking data and creating a dynamic customer graph with a high accuracy.

Specific analytical use cases are embedded in customer-facing processes to build deeper connections, recommend next-best actions, and create more contextually driven interactions. Additionally, personalization vendors such as Evergage, RichRelevance, Reflektion, and Optimizely also use machine learning to create one-to-one experiences. Web experience management providers SiteCore, EpiServer, and Progress use machine learning to improve content-driven experiences, such as improved tagging and image recognition.

Customer service and support initiatives can be improved with Coveo; cognitive search technology applies machine learning algorithms to continuously adjust and auto-tune relevancy based on the behavior of like users to surface the best content, such as knowledge articles or videos. The explosion of new conversational technologies, such as chatbots, mobile messaging apps, augmented reality, and two-way video chat, has dramatically changed the face of customer engagement. Vendors such as HelpShift, CafeX, [24]7, and Inbenta use machine learning to trigger contextual responses throughout the customer lifecycle for both assisted and self-service interactions.

AI and machine learning technology is quickly becoming the catalyst for one of the most profound changes ever to occur in the relationship between individuals and the world around them. Business are redefined from a transactional relationship between people into more nuanced, tangled relationships between humans and the automated systems and devices they use to engage the world.

Sheryl Kingstone is research director for business applications at 451 Research.