Customer Analytics: A Powerful Source of Competitive Advantage for Midsize Organizations

Issue 2 Customer Analytics: A Powerful Source of Competitive Advantage for Midsize Organizations 1 Introduction 2 Finding the Value in Customer Data 4 From the Gartner Files: A Customer Service Analytics Framework as CRM Engages the Social Customer 10 About IBM Business Analytics Introduction Businesses have an abundance of customer data available from an increasing number of sources, but many organizations struggle to turn this data into usable insights. In an economy in which multiple organizations sell similar products and services to a finite market, a company s ability to compete effectively depends strongly on how well it understands its customers. In a new research report, A Customer Service Analytics Framework as CRM Engages the Social Customer, Gartner states that applying analytics to key CRM customer service processes can have a dramatic impact on service costs, loyalty and the success of social media strategies. The report focuses on the need for customer service directors to become more fully engaged with individual customers, and the deeper forms of analysis required to create a comprehensive customer view that makes such interactions possible. Completing the customer picture today involves collecting and assembling data from multiple sources, including traditional sources such as transactions and surveys, as well as customer sentiments and other information that can be uncovered in comments made on social media channels. An effective customer analytics strategy enables businesses to use all of these insights to increase customer lifetime value, reduce turnover, conduct more precise targeting and segmentation and enhance up-sell and cross-sell opportunities. This strategy should include business intelligence and advanced analytics technologies, which empower midsize organizations to gain a thorough understanding of their customers and integrate that insight into every interaction. Source: IBM Featuring research from

Finding the Value in Customer Data Forward-thinking, analytics-driven businesses personalize their interactions based on information they collect about their customers, an approach that enables them to provide a higher level of service and cultivate strong customer relationships. Many companies have obtained the best results by using a combination of business intelligence (BI) software and predictive analytics. They monitor and measure their historical performance using BI tools which help them spot trends in customer behavior, such as increased sales of a specific product, or a greater number of complaints about a product line. Integrated planning, budgeting and forecasting solutions allow sales or marketing analysts to create reports and dashboards to communicate the trend to executives or others who need the information to take action, and align their operational tactics and financial goals in near real time. Meka Pro, a manufacturer of construction products, uses IBM business intelligence software built specifically for the needs of midsize businesses to gain deeper insight into sales and manufacturing data. Integrated reporting, forecasting and planning capabilities provide the company with a clear picture of its customers and the products they use, which in turn helps Meka Pro procure materials at reduced costs and provide more attractive pricing. Organizations can take this approach a step further by employing predictive analytics to understand what their customers will most likely want or do next. Sophisticated data mining technology can find hidden clusters of customers with similar attributes and characteristics that have purchased products or responded to campaigns in the past. The customer service team can then use that information to make more personalized recommendations in the future. Predictive models not only help determine what products or services are most likely to be purchased together, but also uncover behavior patterns of customers who defected vital information that customer service representatives can draw on to prevent future customer turnover. Telecommunications provider XO Communications turned to IBM business analytics software to help identify the factors that indicate whether a given customer is likely to change providers. Using a sophisticated statistical model that provides a monthly risk assessment for each customer, the company improved customer retention by 26 percent over two years through targeted interventions with customers, resulting in an annual net benefit of over $3.8 million. Social media analytics also play a critical role in today s customer service strategies. As businesses feel the pressure to gain new insights from platforms such as Facebook, YouTube and Twitter, they require the tools to transform this flood of information into strategies that can be acted on. Technologies such as data mining and sentiment analysis can help organizations detect possible risks to their reputation or potential opportunities through analysis of customer comments on social media sites, web pages and blogs. Businesses can then apply these insights to measure brand perception, deliver targeted marketing messages and turn satisfied customers into advocates. For financial services company BBVA, analyzing social media comments with IBM analytics software enables the bank to take a holistic view across all areas of its business. By monitoring the voices of current and potential clients on social media sites, identifying expert opinions about BBVA and its competitors on blogs and controlling its presence in news channels to gain insights and detect possible reputational risks, the bank can focus its actions on the most important topics of online discussions and immediately plan the most suitable response. In each of these examples, customer analytics makes it possible for businesses to draw from a blend of structured and unstructured data to shape personalized interactions in real time. For instance, when a customer calls a contact center, the agent can use analytics combined with a customer s historical information and additional data gathered during the conversation to drive the desired outcome. This might Customer Analytics: A Powerful Source of Competitive Advantage for Midsize Organizations is published by IBM. Editorial content supplied by IBM is independent of Gartner analysis. 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mean extending a relevant cross-sell or up-sell offer to increase customer value, or resolving a product or service problem to prevent a valued customer from defecting. An integrated IBM portfolio for deeper customer insights IBM Business Analytics solutions deliver the combination of powerful, easy-to-use capabilities midsize organizations need to discover what matters most to their customers. By deploying and integrating customer analytics solutions that are designed and priced for midsize firms, businesses gain valuable insights they can use to enhance the customer experience while adding to the bottom line. IBM Cognos Insight gives analysts and business users the tools to quickly find answers to business questions on their own at any time, using a combination of personal and organizational data. Realtime dashboards and compelling visualizations make it possible for business managers in customer service, marketing, sales or finance to act quickly and decisively on critical issues shaping business outcomes. For instance, a customer service manager who sees a sudden downturn in satisfaction scores can drill down to determine the root cause, and then take quick action to resolve the issue. IBM Cognos Express delivers the essential reporting, analysis, dashboard, scorecard, planning, budgeting and forecasting capabilities that workgroups and midsize companies need. This scalable, preconfigured solution can help organizations create an integrated view of their customer data so they can discover and act on new insights quickly. IBM SPSS Modeler is a powerful, versatile data mining and text analytics workbench that helps build accurate predictive models quickly and intuitively, without the need for programming. It drives positive return on investment by creating predictive intelligence that enables organizations of any size to proactively and repeatedly reduce costs and increase productivity. IBM SPSS Statistics data analysis software is used by customers of all sizes and in just about every industry to solve industry-specific business issues and drive quality decision-making. Methods like forecasting, trend analysis and assumption validation can provide a robust, user-friendly platform to understand data and solve complex business or research problems without needing to be expert analysts or programmers. These IBM Business Analytics software solutions are part of a comprehensive, unified platform that works smoothly to deliver a complete view of your customers to decision makers in every functional area of the business from the call center to sales, marketing, product development and manufacturing. By deploying the results of customer analytics to every customer touch-point within your organization, you can achieve greater effectiveness and profitability. Key capabilities are tightly integrated, so you can build or add capabilities as you need them. Through analytics, every piece of customer data from every system becomes the fuel needed to drive customer interactions and realize higher returns. Conclusion The amount of customer data now available through all channels and sources is staggering. Businesses need to gather and act on this data in order to stay ahead of their competitors. Companies that master customer analytics will find many new opportunities for profitable growth. Customer analytics uncovers hidden insights in customer data which help organizations create personalized experiences that win more business while reducing costs and increasing customer loyalty. Powerful analytical tools can help companies build customer loyalty, improve the return on marketing investment and generate new sources of revenue. Source: IBM 3

From the Gartner Files: A Customer Service Analytics Framework as CRM Engages the Social Customer Applying analytics to key CRM customer service processes can have a dramatic impact on service costs, loyalty and the success of social media strategies. A framework is necessary to understand how directors of customer service are supported by analytics. Key Findings Social media resources such as Facebook, Twitter and SMS add a new dimension to what needs to be analyzed in order to understand the customer. Beyond the use of analytics by role is a second consideration: the customer engagement phase supporting customer information searches, managing a support request and intervening in unsatisfactory experiences. Customer service analytics have four separate targets: The customer service representative (CSR) supporting the website or social media/community, or the contact center The customer requiring insight into an issue, or problem resolution The business users, such as the customer service manager planning a strategy with the marketing manager or social media director The IT team or business process or quality team building the customer service processes and workflow need to understand the information flow, and the success of the support processes Recommendations The customer service director should work with the heads of marketing and digital media to extend the organization s analytical capabilities to include the infrastructure for social media, CSRs, sales executives, salespeople and the customers/prospects. Inventory the decisions and analyses each main service role needs to make decisions. Identify the relevant information sources (e.g., CRM systems, social media management systems, site traffic, social graph and feedback management). Specify the required analytical capabilities (e.g., offline/predictive, real-time/ customer-facing, social and contextual). Apply analytics to the various pace layers: the systems of record, systems of differentiation and systems of innovation. Prioritize the urgency of the analysis and the business value. What You Need to Know Enterprises around the world are beginning to understand that interacting with (or just reacting to) customers is insufficient. The rise of Internet-enabled mobile devices (ipads, iphones and Android smartphones), social media platforms such as Facebook, YouTube and Twitter, and thousands of community sites has enabled billions of interconnections among consumers and businesses. The strong emphasis in service is to engage the customer in order to be more fully engaged with their issues, questions and experiences. This is often referred to as being proactive, or acting on the customers behalf, consistent with long-term profit objectives. It also entails the use of social media to create a more complete picture of a customer s situation. In order for a director of customer experience or customer service to understand what goes into satisfying individual customers, much deeper and/ or better orchestrated forms of analytics are necessary. As a result, the CIO and business intelligence team will become closer allies with customer service. The vice president (VP) of service must address everything from traditional metrics, such as call-handling statistics (traditional metrics), all the way to the big data issues of mixed media content such as video, voice recording and structured database information (unstructured data analytics). In customer service, there is a tremendous urgency regarding the topic of analytics, especially contextual analytics. The creation of this closed-loop engagement system will contribute to refinements in marketing efforts. Strategic Planning Assumption Through 2015, 80% of businesses will fail to improve customer service costs due to the absence of a cross-functional approach to creating an analytics framework that includes social media. Analysis Key Analytics Tools for the Director of Customer Support The purpose of this research is to help heads of customer service (with titles such as VP of customer support, customer experience officer, director of client services and director of student affairs) and IT department heads supporting the customer service function to understand the categories of customer service analytics. This will save them time as they sift through the competing marketing messages of various solution providers. Engineering successful customer service interactions is aided by various analytical disciplines. No one type of analytics can be applied generally; rather, a range of analytics can be targeted to the specific type of customer interaction under way. Our research creates a framework by which users can understand the types of analytical systems that align with the various decisions that require analysis in the customer support domain. The result should be the 4

5 establishment of a comprehensive capability to analyze the performance of the customer service function across all channels. Who Benefits From Analytics in Customer Service and Support? Customer service and support (CSS) applications that are under the responsibility of the director of customer service support five main customer-centric processes (see Note 1): Case management/customer service/ problem resolution: Agent-facilitated problem resolution (the traditional resolution of problems in a customer service center) Collaborative answers and case resolution using the customer peer-topeer community CSR workforce optimization (WFO) Capturing the voice of the customer/ customer experience Web customer service (resolving problems through self-service) Creating an integrated, multichannel customer engagement hub open to social media The four primary processes impact four different participants in customer service for (and about) whom customer service analytics insight is required: CSRs supporting the traditional telephone channel or the website via chat, email, blogs and posts, or managing social media/community communication, comments and requests Customers requiring insight into an issue, or the resolution of a problem (see Note 2) Business users, such as the VP of service, customer service managers planning their strategy with marketing managers, or directors of demand generation (see Note 3) IT teams or business process or quality teams building the customer service processes and workflows needed to understand the flow of information, and the success of existing support processes Analytics assists in every phase of supporting the customer. Beyond the use of analytics by role is a second consideration an understanding of the customer engagement phase, for example: In the search process on a website, analytics can be used to identify why a customer fails in searching for and finding information. In the selection process, the use of realtime decision analytics could help direct customers to the products or services for which they have the greatest affinity. For the feedback process on a forum, analytics can be applied to identify positive and negative sentiments, and to use these to improve product or interaction steps. The Responsibility of the Director of Customer Service Will Expand The need to create a more complete customer engagement strategy binds marketing efforts with those of customer service, product delivery and ongoing customer life cycle activities. As such, we see a director of customer service as needing analytical insight along six areas: Customer experience: The overall health of the customer s affinity for the enterprise, and its products and services. Agent efficiency: The ongoing costeffectiveness of the CSR. Revenue generation: An understanding of how CSRs are fulfilling expectations beyond cost containment. Business application analysis: An ongoing assessment of the appropriateness of the tools used by customer service personnel and customers to complete tasks and interactions. This includes social media channels. Customer service pace-layered process analysis: Gartner has defined the critical components of a customer service organization. Customer service leaders need to continually assess which tools and technologies are required to provide a differentiated experience for customers, as well as those that will ensure innovation around the customer service process. These systems, listed below, are detailed in Use Gartner s Pace-Layered Application Strategy to Structure Customer Service Applications Based on Business Value. Social analytics: An inevitable partnership is forming between marketing and customer service as social media engagement moves beyond simply listening for clues regarding product placement and campaign management. Customer service leaders will need to participate in, and often lead, a cycle of monitoring, analyzing, engaging and following up with process improvements. For each executional process and insight area essentially tasks and/or initiatives we ask strategic and tactical questions: Strategic: In each of these areas, what decisions does the director of customer service need to make? Tactical: What information sources are needed to make these decisions? 5

Tactical: What analytical requirements are needed to make sense of the information? Tactical: Which target systems can be fed the output of the analyses and insights? Tactical: Which key performance indicators () indicate success? Tables 1 through 6 show the executional process and insight areas. Reach Out to Others in the Organization Many other departments must participate to support an analysis of the executional processes and insight areas: Other business leaders: Chief customer officers or directors of customer service must work with business leaders such as chief marketing officers, CFOs and heads of digital media, sales, e-commerce and customer experience. This ensures alignment with business goals, and the measure of customer service objectives across business domains. Social media team: If your organization has a separate social media team that does not include customer service, then idea sharing must occur between the two so that insights and interactions observed in social media channels do not become silos outside customer service analytics. Analytical specialists: Specialists will need to perform analyses in highly specialized areas. These may include customer profitability or lifetime value analysis, pricing, media planning, promotion planning, market analysis, customer segmentation or profiling and demand forecasting. The Importance of Analytics to the Customer Experience Customer service is a critical aspect of how the customer experience takes shape and is sustained over time. It determines the health of the relationship with the individual customer, as well as the strength of the overall customer community. Analytics will emerge as a key discipline to look beyond a focus on agent efficiency and into areas Table 1. Examples of Customer Service Insights and Analyses for Executional Processes in Customer Experience Executional Process and Insight Areas Customer Experience Decisions to Make Understand competitor benchmarks Assess verbatim records from customers using engagement channels Assess sentiment on all communication channels, including social media Summarize pain points requiring redress Prioritize customer experience projects Information Sources to Make the Decisions Surveys Feedback Management systems First-call resolution data Call recording Quality assurance Quality monitoring Customer database (e.g., demographics, psychographics, life stage and life cycle) Analytical Capabilities to Analyze the Information Speech analytics Customer data analysis Text analytics/ mining Video analytics Channel process efficiency analytics Customer sentiment matching Capturing feedback from mobile devices Business Applications That Fed the Analyses Customer service Contact center Customer Web portal In-store systems Campaign management Product development Customer satisfaction dashboards Defection rates Net Promoter scores Satisfaction surveys Increase in website traffic Growth in knowledge articles consumed Change in sentiment monitored across channels Natural-language processing and self-service analytics 6

7 Table 2. Examples of Customer Service Insights and Analyses for Executional Processes in Agent Efficiency Executional Process and Insight Areas Decisions to Make Information Sources to Make the Decisions Analytical Capabilities to Analyze the Information Business Applications That Fed the Analyses Agent Efficiency Call duration First-call resolution WFO Quality assurance Contact center performance management Training Scheduling Call length Repeat calls Calls per shift Interaction recording Screen analytics WFO First-contact resolution Average cost per call Monitoring Real-time decisioning CRM and CSS process Escalation Blend of media handled email, phone, chat, social/ peer-to-peer assistance Staff churn Time to train Strategic planning software forecasting, scheduling and coaching E-learning Desktop unification tools Customer sentiment matching Interaction analysis (speech, text, screen and call flow analytics) Automatic call distribution (ACD)/ routing Staffing volume Training hours Training gaps Performance management postcall surveying Table 3. Examples of Customer Service Insights and Analyses for Executional Processes in Revenue Generation Executional Process and Insight Areas Decisions to Make Information Sources to Make the Decisions Analytical Capabilities to Analyze the Information Business Applications That Fed the Analyses Revenue Generation Offers made and accepted Sales force automation software Customer data hub Master data Real-time decisioning Sentiment analysis Customer profitability analysis Predictive modeling for retention and opportunity Customer profiling and psychometric analysis Social media monitoring Campaign management systems Impact on customer satisfaction Agent retention, revenue and profitability Customer lifetime value improvements 7

Table 4. Examples of Customer Service Insights and Analyses for Executional Processes in Business Application Analysis Executional Process and Insight Areas Decisions to Make Information Sources to Make the Decisions Analytical Capabilities to Analyze the Information Business Applications That Fed the Analyses Business Application Analysis Training time Time required to set up, navigate and close an application Process steps Agent satisfaction with application Customer feedback on agent competency Channel process efficiency (e.g., search to Web to chat phone) All CRM systems for customer service Unified communications Web customer Service software Mobile applications Channel process efficiency analytics Customer profitability analysis Predictive modeling for retention and opportunity Feedback management system Natural-language processing and selfservice analytics Customer service contact center Customer Web portal In-store systems Campaign management Product development Customer satisfaction dashboards Impact on customer satisfaction Agent retention, revenue and profitability improvements Customer lifetime value Table 5. Examples of Customer Service Insights and Analyses for Executional Processes in Customer Service Pace-Layered Process Analysis Executional Process and Insight Areas Decisions to Make Information Sources to Make the Decisions Analytical Capabilities to Analyze the Information Business Applications That Fed the Analyses Customer Service Pace-Layered Process Analysis: System of Record System of Differentiation System of Innovation Analysis How well the challenges are met of customer experience excellence through systems of record, systems of differentiation and systems of innovation See Use Gartner s Pace-Layered Application Strategy to Structure Customer Service Applications Based on Business Value See Use Gartner s Pace- Layered Application Strategy to Structure Customer Service Applications Based on Business Value Case management and problem resolution Collaborative peer-to-peer answers and case resolution WFO Voice of the customer Web customer service Text analytics/ mining Video analytics Channel process efficiency analytics Customer sentiment matching Capturing feedback from mobile devices Natural-language processing and self-service analytics Customer service contact center Customer Web portal In-store systems Campaign management Product development Customer satisfaction dashboards Cost of business applications versus cost to service Change in mix of service channels used Change in cost of service delivery by channel (mobile, Web, agent, direct and selfservice) Delta between customer service organization efficiency metrics and those of the competition Impact of service delivery on customer satisfaction Agent retention, revenue and profitability improvements, Customer lifetime value 8

9 Table 6. Examples of Customer Service Insights and Analyses for Executional Processes in Social Analytics, Social Media Monitoring, Listen/ Analyze/Respond, Peer-to-Peer and Community Health Executional Process and Insight Areas Decisions to Make Information Sources to Make the Decisions Analytical Capabilities to Analyze the Information Business Applications That Fed the Analyses Social Analytics Social Media Monitoring Listen/Analyze/Respond Peer-to-Peer Community Health Whether the owner is customer service Whether the customer community is healthy Whether the key influencers are satisfied What content the community is creating that needs to be harvested for the enterprise to share externally and internally Participation by demographic Twitter Fan page Facebook Geographical or industrydesirable forums Peer-to-peer communities Blogs LinkedIn Video, photos Text analytics Efficiency analytics Influencer impact Sentiment analysis Campaign management Customer satisfaction dashboard Product development CRM, customer sales and service records Positive or negative sentiment Share of voice Time to first response Percentage of posts requiring a company response Change in channel distribution Percentage of nonemployee posts Audio: YouTube and others Location data from GPS Social graph analysis such as revenue generation, the efficacy of the deployed technologies, and the interplay between what social media channels are saying about the business and the challenges faced by the VPs of customer service and customer experience. This is an enterpriselevel concern, rather than an initiative run at a departmental level. Note 1. Pace Layering of Customer Service Applications We describe the details of functional components in Use Gartner s Pace-Layered Application Strategy to Structure Customer Service Applications Based on Business Value. Note 2. for Problem Resolution Analytic insight answers questions such as: What channel are they on? What service path did they follow? How does the service path match the service path designed for the process? What service level does the request have? What influence do customers have on the customer community, or beyond the customer base? What is the current sentiment toward the enterprise? Has the interaction been captured in a CRM database? Note 3. Shared by Customer Service and Marketing Analytics insight might include: Which customers or prospects should be exposed to an offer? How should CSR metrics be adjusted to allow for marketing activity? Do CSRs have the right skills and measurement to handle social media interactions? How can marketing and customer service coordinate the handoffs from social media to the customer service process when negative sentiments are encountered? Is it possible to market into an online peerto-peer community? Source: Gartner Research, G00231327, M. Maoz, 4 May 2012 9

About IBM Business Analytics IBM Business Analytics software delivers data-driven insights that help organizations work smarter and outperform their peers. This comprehensive portfolio includes solutions for business intelligence, predictive analytics and decision management, performance management, and risk management. Business Analytics solutions enable companies to identify and visualize trends and patterns in areas, such as customer analytics, that can have a profound effect on business performance. They can compare scenarios, anticipate potential threats and opportunities, better plan, budget and forecast resources, balance risks against expected returns and work to meet regulatory requirements. By making analytics widely available, organizations can align tactical and strategic decision-making to achieve business goals. For further information please visit ibm.com/business-analytics. Request a call To request a call or to ask a question, go to ibm.com/businessanalytics/contactus. An IBM representative will respond to your inquiry within two business days. 10