You are currently browsing the tag archive for the ‘IBM Watson’ tag.

During recent IBM analyst big data event, I learned about a new product, IBM Predictive Customer Intelligence. It extracts and processes customer-related data from multiple sources to analyze customer-related activities and has capabilities to predict customer behavior and actions. Predictive Customer Intelligence is built on IBM’s big data platform and supports extraction and integration of data from multiple sources, internal and external, and from structured and unstructured data. It can process data created by third-party products, such as text-based files of data created by converting speech to text. The product can capture and analyze customer interactions from multiple communication channels such as voice, email, text messages, chat and Web usage scripts and social media posts.

Predictive Customer Intelligence has four primary modules, for predictive modeling, reporting, real-time scoring and a real-time analytics data repository, which are connected by the IBM Integration Bus. These modules support a predefined process in which users build models from customer data stored in analytics real-time customer database and use them or predefined models to run real-time analysis against the customer data and produce scores, recommendations, reports and dashboards related to customer activities. The outputs can be delivered through a variety of channels such as outbound email, direct mail or text message. This can help contact center agents provide personalized and contextualized responses to customers’ questions. Other outputs can be used to produce targeted marketing campaigns or to respond to customer interactions through other communications channels.

vr_NGCE_Research_08_all_channels_for_customer_engagementMy benchmark research into next-generation customer analytics shows a need for such a product because companies have up to 21 potential sources of customer data. These include transactional business applications such as CRM and ERP, customer data warehouses, spreadsheets, call recordings and text-based files containing content from email, forms, letters, text messages, chat scripts, Web scripts and social media posts. All of these not only contain valuable customer information but also interaction data from which companies need to derive insights into customers’ feelings about products and services and other aspects of the business. The research shows companies have difficulty in extracting value from this data, partially because on average they use only six sources of customer data in their customer analytics. Interaction data is especially problematic because most of it is unstructured and requires tools that can automatically access and extract insights from them; few companies have such tools. This situation also is becoming more complex, as my benchmark research into next-generation customer engagement shows: Companies are supporting more channels of interaction and expect volumes of interactions to grow in every channel as our research shows up to 17 channels in play.

IBM Predictive Customer Intelligence has capabilities that can help companies meet these challenges. However, a close look reveals that it is not one but 10 individual products (not including three connectors) packaged together. Organizations therefore need to understand the cost and operational impact of managing and use these products.

At the big data analytics event, Frank Theisen (IBM VP of front-office transformation for Europe) summed up the information challenges companies face; they need to know:

  • What happened?
  • Why did it happen?
  • What can be learned?
  • What action should be taken?
  • What could happen in the future?

Ventana Research believes that big data analytics can answer these questions. vr_Customer_Analytics_03_key_benefits_of_customer_analyticsFor example, my benchmark into next-generation customer analytics shows that one-quarter (26%) of companies have deployed a dedicated customer analytics product and have found it has helped them improve the customer experience and their analysis of business performance. More generally my colleague Tony Cosentino wrote about three Ws that are key: What data you have, what information you want to derive from data, and what action should be taken as a result of insights gained from it. Once you can answer these questions you can decide which analytics product best fits your requirements.

IBM focuses intensively on its technology sometimes to the extent of obscuring the business applications of those systems. One prime example is that more and more IBM big data products are moving to the direction of IBM Watson and methods of cognitive computing. Basically Watson is a platform that can search very large volumes of information to deliver insights from the data by use of natural language, and it is smart in that it learns as it searches, so that future answers are more refined and targeted to the questions asked. Such capabilities are particularly useful for analyzing the very large volumes of customer interaction data companies accumulate; they help identify trends, hot issues and focused information to help personalize responses and put them in the context of an overall customer relationship.

Our next-generation customer analytics benchmark research shows usability is the top priority for selecting analytics software: 64 percent of companies said it is very important. To provide it vendors should support point-and-click access to information on mobile devices and visual ways of showing the results of analytics. One case study IBM used during the day illustrated this; the user collects a vast array of data, integrates it and delivers analysis in visual formats on Apple iPads. This is well-suited for assisting customer-related activities that happen in real time (such as phone calls) where users need instant access to up-to-date information in forms they can understand immediately.

Companies already have huge amounts of customer-related data, and if you factor in the increasing volume of electronic communications, social media and the coming Internet of Things, this need will grow more acute. IBM has a variety of analytic products and is developing more. The challenge is to figure out which IBM products can best process what data and produce the required information and insights to drive decisions and action. Predictive Customer Intelligence and IBM’s other big data analytics are worth considering in organizations’ efforts to improve understanding of customers and their experiences.

Regards,

Richard J. Snow

VP & Research Director

The contact center market continues to shift focus from handling customer calls as efficiently as possible to providing superior customer engagement across multiple touch points. The latest advancement is an joint announcement from IBM and Genesys who have signed a partnership agreement to provide “smarter customer engagement”. The agreement includes a technology partnership and a joint marketing plan, and brings together IBM’s Watson Engagement Advisor and Genesys’ Customer Experience Platform.

IBM Watson first became known by beating expert human contestants on the TV show Jeopardy! Since then it has impacted the medical industry by advising doctors on medical care, and more recently IBM created a smart mobile app that is able to advise and resolve customer issues. IBM Watson is an cognitive computing platform that uses machine learning to provide responses to questions through its natural language question based search interface that can process large volumes of information including a knowledge base of history and trained responses to find the most relevant and probabilistic response. Its machine learning can refine its responses from previous interactions, thus making the outcomes increasingly relevant. In the mobile app it can capture text entered into the app, search data from in-house and publicly available information sources and return information most closely related to the request. For example Watson can capture a customer’s request for an insurance quote, search all available insurance information based on the person’s extended customer profile and return information on the most appropriate policy for that customer and circumstances and refine it based on the individual unique requirements. The IBM Watson technology received the Ventana Research Technology Innovation Award for its ability to change how companies operate in taking actions and making decisions.

vr_NGCE_Research_08_all_channels_for_customer_engagementGenesys was an early developer of customer/telephony integration (CTI) and call routing. Over time management has combined acquisitions and internal development efforts to create a contact center in the cloud product set that is available in the cloud. Its original product set has thus been extended to include management of multiple channels of communication, single-queue routing regardless of type of interaction, workforce optimization, an agent desktop and analytics. Nearly half of companies in my benchmark research into the contact center in the cloud are considering such systems to improve customer engagement.

Several research studies including next generation customer engagement show that companies are deploying more channels of interaction to meet customer expectations; on average companies now support more than seven channels with seventeen possible. These are a combination of what are commonly called assisted channels, in which a person responds to the customer, and self-service where the customer interacts solely with technology, such as an interactive voice response (IVR) system. The research shows that both approaches create issues. Assisted service depends on the skills of the people who handle interactions and their abilities to access the right systems and information. The agent’s desktop is a major determinant of whether this is hard or easy. Self-service depends on how well companies have set up the technology and how closely it fits customer expectations. In this respect my research shows that companies have not done a very good job; for example, nearly two-thirds of IVR interactions and Web-based self-service attempts end up with the customer taking the option to speak with an agent.

In simple terms these systems are not smart enough. The IBM/Genesys partnership is intended to address this. Genesys has built interfaces from its agent desktop and self-service channels to pass data to IBM Watson, which applies its cognitive computing capabilities and returns with specific information to the questions asked. The results can be passed to the agent through the desktop to suggest the best action or delivered to the customer through the self-service channel. In either cases the customer gets relevant information, which contributes to a better experience, and the company may get a better business outcome. If you want to understand more about cognitive computing, our educational overview will help and my colleagues recent analysis of IBM billion dollar investment into IBM Watson.

vr_NGCE_Research_01_impetus_for_improving_engagementMy research has led me to insist that the customer experience is now the primary business differentiator and is the top driver for improving customer engagement in three quarters (74%) of organizations according to our customer engagement research. It has almost become impossible to compete solely on product, service or price; how customers feel during and after interactions is what counts and what determines the actions they take. This new partnership has the potential to deliver better responses to customer inquiries and requests and should thus improve the experience. It also provides a competitive advantage for Genesys to differentiate its offering to others in providing truly a smarter agent desktop and customer self-service. The alliance of these two major vendors will be worth watching for companies striving to improve internal business processes and better meet customer expectation for relevant and timely responses to their questions.

Regards,

Richard J. Snow

VP & Research Director – Customer Engagement

RSS Richard Snow’s Analyst Perspectives at Ventana Research

  • An error has occurred; the feed is probably down. Try again later.

Twitter Updates

Error: Twitter did not respond. Please wait a few minutes and refresh this page.

Stats

  • 68,554 hits
%d bloggers like this: