Advancing the language capabilities in the communication yields more precise, granular interactions focused on the specifics the customer needs to know.
- "I'm not interested in a 90-minute hike, is there a 60-minute one?”
- "Has my CEO checked in yet, and is she planning on an activity tomorrow?"
- "How many people have arrived for the conference, and how many of them are analysts?"
- What kind of analysts?
- "Oh, sorry, I meant industry analysts."
Data requirements for these more intelligent and personalized interactions are even more vast and complex: weather, Nielsen, travel sites, tourist guides, travel bloggers and influencers, permissions about arrivals, and more.
As you can see, many complications and implications follow the scenarios and escalate as the personalization and complexity of transactions increases. This is in line with all research on CX that states the complexity increases the deeper the connection with the customer grows. Creating a satisfying customer experience requires data from many sources, provisioned in near-real-time, but there is more to consider. The data sources are not typically tailored for this application. It takes intelligence to provision the data and integrate it for the application. To create accurate recommendations for mappings of data sources, and related objects, one must leverage methodologies including machine learning, natural language queries, intelligent search, and graph-based relationship patterns.
if the application has to query data from multiple sources, many of which are not under the control of the application, a useful technique is to create a knowledge graph of the data sources to guide queries quickly to sources without moving data around the network. A knowledge graph powerfully distills complexity to something understandable. Connections between nodes form a directed graph, which is the bedrock for background processing. A suite of AI tools employed for constructing and using the knowledge graph is:
- Recurrent Convolutional Neural Networks
- Semi-structured data parsing: Hidden Markov Model and gene sequencing algorithms
If these terms seem esoteric, they are, but it takes complexity to make things simple. There is so much more that can be done with the customer experience using communications, data, and AI… but integrating the pieces is not simple. The controlling point of customer satisfaction is reaching people when and where it matters, how they want to be reached, and delivering to their expectations. Not wasting their time with trivial and irrelevant interruptions, and enhancing their experience with useful and timely resources, is vital for success in CX and engagement.