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July 30, 2014 in Customer Performance Management (CPM), Information Applications (IA), Business Analytics, Cloud Computing | Tags: Contact Center, Call Center, Social Media, Customer Experience Management, Cloud Computing, IBM, Text Analytics, Customer Service, Collaboration, Voice of the Customer, Contact Center Analytics, Customer Analytics, 360-degree view of the Customer, IBM Watson, IBM Predictive Customer Intelligence | by Richard Snow | Leave a comment
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.
Richard J. Snow
VP & Research Director
June 26, 2014 in Business Analytics, Cloud Computing, Customer Performance Management (CPM), Social Media | Tags: 360-degree view of the Customer, Call Center, Cloud Computing, Contact Center, Contact Center Analytics, Customer Analytics, Customer Experience Management, Customer Service, Mobile apps, Social Media, Text Analytics | by Richard Snow | Leave a comment
By its own admission, SAS has a very large software portfolio (of more than 250 individual products), and it continues to develop and release more products and updates to existing ones. Some of the products are sold alone, and others are bundled into “enterprise solutions”. Some are for technical users, and others are business applications. This complexity can make it hard to identify which product or bundle serves a particular need. Three are most relevant to my research practice: Customer Intelligence (CI), which I wrote about after attending the 2013 SAS European analysts event; SAS Visual Analytics; and a new one, the Customer Decision Hub that SAS has developed to support multichannel customer engagement.
When I last wrote about Customer Intelligence I noted that it was designed mainly to process structured customer data (such as found in CRM and ERP applications and customer data warehouses) and the analysis it generated was largely for use in marketing. At this year’s analyst event SAS highlighted several developments, but most are to support marketing better, although some directly impact customer engagement. One of the challenges in understanding CI is that it is a bundle of 11 products, and that doesn’t include products that are part of the underlying SAS technology platform. Of the 11 business applications, six relate directly to marketing, and one, SAS Profitability Management, allows companies to understand and manage profitability at a detailed level. The remaining four products relate more to customer engagement: SAS Customer Link Analytics (designed to identify the communities in which customers interact), Real-Time Decision Manager (to deliver personalized offers derived from rules-based analytics), Adaptive Customer Experience (to create profiles of customers based on interactions and other customer data) and Social Media Analytics (to view and analyze customer activity on social media). Collectively the CI bundle of products supports the end-to-end marketing process, but a lot of the capabilities also relate to sales and customer service. The issue for potential customers thus becomes which of the products directly serve their business objectives and what impact picking among them has on pricing, implementation and ongoing operations.
SAS Visual Analytics is a product that makes it possible for business users to create and run their own analytics. This is especially relevant in the contact center and customer service business units. My benchmark research into next-generation customer analytics shows that unlike most other business units, these tend not to have data scientists or analysts to help them produce analysis of customer-facing activities. Instead they rely on managers to produce their own reports and analysis, and as the research shows, they rely heavily on spreadsheets to do this. After IT sets up access to the right data stores, Visual Analytics helps business users create their own analysis requirements and run these against the data sources to produce the analysis and metrics they need. It thus enables managers to keep up with the ever increasing demands of customers and to base decisions on the most up-to-date information, without having to rely so much on IT assistance.
My benchmark research into next-generation customer engagement shows that customer engagement is a multichannel task that is carried out by multiple business units, which CI and Visual Analytics both support. Companies thus need to recognize that customer engagement is a cross-business responsibility that should be based on a single view of the customer, be rules-based to ensure consistency and the best possible outcomes, and should use multiple forms of analytics, on all sources of customer data, to provide the analysis and metrics to monitor and assess past performance and influence future actions. My benchmark research into next-generation customer analytics shows that many businesses have not made this transition yet and still rely on tools not suited for these tasks. The most common tool (used by 52% of companies) to monitor and assess customer-related activities is spreadsheets; only 26 percent have deployed a dedicated customer analytics tool. While spreadsheets have their place, they cannot process unstructured data and cannot work in real time to provide advice such as next best actions a contact center agent should take while talking to a customer.
To meet these requirements SAS has developed its Customer Decision Hub. This bundle of products can help businesses achieve an omni-channel experience – that is, consistent, personalized and in-context experiences at all touch points. It includes APIs that allow businesses to capture all customer interactions in real time or batches, regardless of channel, including unstructured interactions such as calls, email, text messages and social media posts. The hub can also capture data about marketing, sales, service and other ad hoc actions. It uses this data to produce analyses, insights and metrics about those actions, put them in context and show history, risks and potential opportunities. The hub also has a rules engine that can recommend actions and the channel through which to communicate with the customer; among the focus of rules are priorities, strategy, constraints, customer preferences, channel restrictions, budgets and contact permissions. The optimization engine is set up using SAS CI Studio, which uses drag-and-drop techniques to create intelligent, rules-driven workflows to create more relevant, personalized customer experiences. The hub thus links external, customer-related interactions and internal processes to provide the analysis and orchestrate actions.
This combination of products, if used properly, could help companies improve customer experiences that cross the boundaries between marketing, sales and service. However, as with CI the Decision Hub includes many applications and capabilities, and much of SAS’s messaging relates to marketing, which I don’t believe does the package justice. Potential customers should make the effort to understand what products are included in Decision Hub, what is involved in running it and the impact it is likely to have across the organization.
One of the strengths of SAS is its range of products, but this can also be a weakness. In their own right, each product supports a robust set of capabilities, but choosing the right set to meet a specific business need seems to be a complex process that often involves third-party consulting services. My colleagues wrote about SAS recently on its focus on business analytics and it work to unify big data across business and IT that also demonstrate how they are bringing many products to a singular focus for business and IT. Also in my view both SAS CI and the Customer Decision Hub focus too much on marketing and not for use across the business. Anything to do with customers is an enterprise issue, not a departmental one. Improving the customer experience is now such a critical issue that companies should look beyond some of the marketing messages and carefully evaluate how SAS can support their customer interaction and overall engagement efforts.
Richard J. Snow
VP & Research Director