Oracle Customer Experience in the Cloud

Oracle has a large and diverse set of products and now has most of its business applications operating in the private and public cloud. However, some recent acquisitions have enabled it to focus on cloud-based-products for managing the customer experience. Our next generation customer engagement research has found that customer experience is the top impetus for improving customer engagement as found by almost three quarters (74%) of organizations. Oracle has created a customer experience suite that includes marketing, commerce, service, sales, CPQ and social cloud. In particular the acquisition of RightNow has become the foundation of Oracle Service Cloud.

Service Cloud is a collection of products built on a common platform : Web Customer Service, Cross Channel Contact Center, Knowledge Management and Policy Automation. Each of these also has several components; for example, Web Customer Service is made up of Web self-service, Social self-service, E-mail support, Live chat, Virtual Assistant, and Smart engagement.  Self-service enables companies to build self-service Web pages which can be accessed on a laptop or a mobile device and can have embedded access to live help (a chat session) if customers need it. Virtual Assistant goes one step further and uses a rules-based engine to initiate a chat session based on the customer’s profile and data entered into the website, in order to provide more contextual responses. It can also be set up to automatically send a link to a document or send out a survey. All three cases make engagement more proactive and potentially more relevant to the customer.

Social self-service supports Facebook-like capabilities that enable companies to collaborate with customers, share information, or create and operate a closed social forum to, for example, gain input for product improvement initiatives. Live chat and Oracle RightNow Cobrowse Cloud Service provides extensions to Virtual Assistant and allows the agent and the customer to browse Web pages together. E-mail support provides standard email management capabilities but is linked with Virtual Assistant to provide more personalized responses; it also includes escalations and workflows to ensure that any required actions are carried out. Smart engagement pulls many of these capabilities together so companies can build guides that walk users through resolving issues, modifying the steps in real time as data is entered.

Oracle has built most of these capabilities on the original RightNow products. According to Oracle, customers can choose only those they need, which means customers and prospects have to understand exactly their business needs and carefully evaluate which products meet those needs but there are so many products making it hard to find what you are looking for and understand all the capabilities. And Customer Service is only one-quarter of the customer experience portfolio. Cross Channel Contact Center is not a contact center in the usual sense. Its focus is systems to manage the operations of a contact center, as opposed to managing communications. Cross-Channel Contact Center does include integration capabilities to these technologies; it can, for instance, collect records of interactions that can be used in subsequent analysis and processes. It consists of nine components: Case Management, Guided Resolution, Social Engagement, Customer Engagement, Analytics, Telephony Control, Unified Agent Desktop and Mobile Desktop. Case Management is not for managing service cases but provides intelligent management of interaction queues; for example, it uses rules to route interactions to the agent mostly likely to meet a customer’s expectations. Guided Resolution enables development of scripts and prompts to guide agents through the process of resolving issues. Social Engagement allows companies to monitor social media activities and proactively reach out to help customers find information or resolve issues. Customer Engagement is what many people think of as customer feedback management; it uses rules to solicit customer feedback. Analytics provides canned reports and analysis, and capabilities that allow users to build their own to gain insights from a variety of customer data including across channels of interactions. Telephony Control provides integration with on-premises or cloud-based telephony management systems so that agents can manage calls from their desktop. Unified Desktop has development and integration tools so that companies can build a unified desktop that enables agents to access systems from a single desktop. Finally Mobile Desktop untethers the desktop from a laptop and allows any authorized user to access contact center systems from their smart devices. Mobile was one of the top areas planned for improving customer engagement (41%) as is analytics (38%) that can operate across channels.

Knowledge Management basically supports the end-to-end process of managing creation, distribution and access of content so the same content can be used by all the other systems, and Policy Management basically supports the end-to-end process of managing a company’s policies.

The Oracle CX portfolio consists of many products that support a very wide range of capabilities. It is true that customer experience management is not simple and requires multiple capabilities. My benchmark research into next-generation customer engagement shows that to improve customer engagement companies have invested and today use a variety of systems; chief among them are CRM (48%), performance management (44%), business process management (43%) and Web-based self-service (39%). The same research also shows more companies looking for cloud-based systems (29%) and mobile systems (63%). My concerns about the Oracle portfolio is that it might be too broad and too complex for any but large organizations to understand; smaller companies with fewer resources might get lost trying work out exactly what they need. This is most likely a consequence of Oracle having to bring together various products from acquisitions. I suspect the same is also true in the naming of some of the products. For example, Web Customer Service doesn’t adequately reflect the capabilities it supports and Cross Channel Contact Center isn’t what many companies think of as a contact center. Companies that make the effort to work through these concerns will find many capabilities that are required to support what I call the omni-customer experience in which customers find it easy to engage with the company and receive personalized, contextual and consistent responses no matter what channel they use or who they interact with. Oracle has a robust portfolio of applications and technology for customer experience, just might take you a little longer to assess the portfolio and approaches.

Regards,

Richard J. Snow

VP & Research Director

IBM Brings Sophistication to Customer Analytics and Prediction

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.

My 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. For 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