Recently my colleague Mark Smith wrote about the IBM Watson platform. Mark is our expert on technically complex subjects like IBM Watson and cognitive computing
I conclude from my research into customer experience management and discussions around the customer experience that consumers want three related things from companies: to recognize them as individuals, to handle any interaction within the context of their overall relationship and previous interactions, and to provide answers that are personalized to their individual needs. Furthermore, as I described in my recent blog about the 2.0 customer, they are increasingly likely to interact with companies through smart mobile devices. Added up these requirements present a major challenge for companies, and the solution lies in data. Organizations have ever increasing volumes of customer data, found in records in CRM, ERP and other business applications, letters, forms, email, call recordings, scripts collected during Web and chat sessions, text messages, video recordings and now social media posts. These all add up to what we call big data, and it comes in structured, semistructured and unstructured forms. To provide a personalized, in-context response to any interaction, companies have to make sense of all this data and build applications that follow typical customer interactions (for example, a request for information, a billing inquiry, a sales inquiry or a complaint) and use it as the interaction is taking place.
This is the context in which IBM Watson Engagement Advisor debuts. It uses the Watson platform to analyze all available data, make sense of it and provide information back to the application the consumer is using to interact with the organization. Its built-in natural-language processing engine allows it to extract relevant words and phrases and combinations of both from text-based input and to search for relevant data in customer records, documents and other relevant sources. To use IBM’s favorite word, Watson is smart, so once it has followed a process, it learns from that and can improve how it carries out the process in the future. The consumer can thus interact using natural language and receive answers that are personalized and in context, and which should improve in both senses over time and experience.
I recognize that companies may have difficulty understanding this new approach to customer experience management. To engage them IBM has announced an early customer adoption program in which it will provide support and guidance on what interactions are appropriate to automate in this way and how to configure the technology and gain access to the data sources; the target of the program is to have an initial system running in six weeks. My recommendation here is the same as I made last year when several vendors announced tools that help companies build mobile customer service apps: Think of the customer first and build apps to match what they want, not what you think will improve the efficiency of your interaction-handling.
It is often said that customer service is the only true differentiator in competitive markets. The challenge I see is that the boundaries between marketing, sales and customer service are blurring, and what consumers want is answers, and they want them immediately during every interaction. As data volumes and types grow, finding answers is like looking for the proverbial needle in a haystack. Watson may be a platform capable of matching up to this challenge. I recommend that companies looking to improve the customer experience take a close look at the IBM Watson Engagement Advisor.
Regards,
Richard J. Snow
VP & Research Director