WHITEPAPER Predictive Analytics for Telecom Service Providers to Target B2B Market Segments Better
Enterprise customers perceive telecom carriers as having no specialization and no locality; rather carriers are viewed as generalists operating in national and international markets, meaning purchase decisions revolve around price alone When telecom providers focus on vertical market solutions, they move away from commodity voice sale towards higher margin, value added services. The vertical approach to marketing strengthens customer loyalty by developing closer links to the customer s core business by way of customization of products (by vertical industry), services, sales reps, customer service support, documentation and training. Telecommunications Services In Vertical Markets: 2005 -- 2010 - The Insight Research Corporation Business Challenge The idea that Telecom Providers need to offer business clients higher value, industry-specific solutions to succeed in competitive and commoditized communications markets is not novel. Telecom Providers know this. Traditional carriers, wireless and Internet service providers, independent solution vendors, virtual network operators and other resellers of telecom products and services have been working for years to combine voice, data, and Internet connectivity services with differentiated value added business solutions. This shift from commoditized capabilities to value added, targeted offerings is also not just telecom industry specific; it applies equally to all providers of products and services to business clients. This white paper describes how leading Telecom Providers are using data mining and predictive analytics systems to mine their business customer and channel partner data, use these insights to strategically target highest value B2B growth opportunities, and align their marketing budgets and sales activities with more data driven, highly targeted business strategies. Angoss provides an incremental approach, with stepwise refinement, enabling clients to build on, strengthen and extend existing segment based strategies for successfully executing vertical business solutions oriented strategies. By using this approach, carriers are able to discover best opportunities for faster revenue growth, predict which marketing and sales initiatives will most impact on revenue growth, and act on these insights, to increase customer revenue growth and retention rates, optimize their marketing and sales budgets, and measure the results. Insight Research has noted that of the $175 Billion wireless US market, for example, $88 Billion was spent by businesses in 2004. A significant portion of that $88 Billion is spent by large global corporations. Verizon Wireless recently noted, for example, that a significant portion of its business revenues were concentrated in a relatively small group of large global corporate accounts. At the outset it is important to note that this white paper is not about enterprise telecom services. These enterprise business customer relationships are significant, and are often even strategic, for both parties. With increasing consolidation among top tier businesses across all industries, and the growing sophistication of their needs in a global economy, these relationships will continue to evolve and create new demands for higher value added telecommunications business solutions into the future. 2
These relationships are relationship based, supported by global sales and service delivery organizations, and involve complex customer specific solutions. Data mining may be relevant for carriers in such areas as revenue assurance, and for improving the quality, consistency and scope of their services to enterprise customers, but adds little value to creating or sustaining these relationships. Similarly, this white paper is not about the consumer telecom marketplace, the small office, home office (SOHO) marketplace, or the small business telecommunications marketplace. Some telecom carriers have begun to apply data mining successfully to improve their marketing and targeting efforts, and to better understand the behavioral drivers of customers within these markets. However, this has been geared primarily to improving the lift of marketing spend, increasing average revenue per user (ARPU), reducing customer churn, and increasing wallet share through bundled offerings key metrics associated primarily with their consumer businesses. Many telecommunications carriers currently use targeted marketing and data analytics tools from Angoss and others to support improved marketing effectiveness of commodity products through customer segmentation, profiling and rules based targeted marketing to acquire new customers, reduce churn, increase revenues, and up/cross-sell existing customers. Instead, this white paper is about the tens of billions of dollars in telecom services revenue opportunities for Telecom Providers with mid-market business enterprises. It outlines how predictive analytics systems can be used to target business accounts more successfully, grow revenues, and increase client loyalty --- while improving the effectiveness of marketing efforts to win and retain these clients, and improving the productivity and efficiency of Telecom Providers sales organizations in closing and expanding business with them. In short, this white paper is about achieving higher revenues, lower costs, and better margins in B2B telecom markets using predictive analytics. Compared with consumer telecom markets, the adoption and successful use of data mining in the B2B telecom marketplace has been less well understood. This has resulted from the use of B2C based approaches and techniques, applied to the very different B2B marketplace, without adapting the approaches used, and processes followed, to reflect the inherently different structure and nature of B2B market segments. Most Telecom Providers are not making effective use of their current B2B business data to maximize the value of their relationships with this important, potentially high value customer segment. However, when best practices in predictive analytics are applied properly in B2B applications, the opportunities to drive revenue growth using analytics as an integral component of their sales and marketing efforts are large and readily measurable. While consumer and small business markets remain constrained by commodity pricing issues, and while enterprise business relationships tend to be global in scope and highly specialized in execution and delivery, the mid-market telecom segment presents interesting business opportunities for revenue growth and for realigning telecom provider marketing, sales and service delivery capabilities to achieve unique differentiation, higher margins and sustainable competitive advantage. This white paper identifies some of the trends supporting the use of predictive analytics in B2B telecom marketing and sales activities, describes why they are emerging, summarizes some of the key challenges they present to telecom organizations, managers, and analysts, and outlines how Angoss KnowledgeSTUDIO predictive analytics with the Angoss StrategyBUILDER strategy design system can help address these challenges while creating a more flexible, affordable and deployable approach to market segmentation, strategic targeting of marketing and sales resources, and the use of data driven predictive intelligence to drive revenue growth. 3
Industry Trends: Increased Competition, Innovation and Margin Erosion Competitive pressure and innovation continue to drive margins down in the telecom carrier market, even as revenues grow through traditional services cannibalization, and new product and service introduction and innovation. Declining toll and commodity service based revenues, shrinking market capitalizations, and defensive mergers all speak to these issues. Competition comes not just from other traditional carriers, but is being played out against a backdrop of declining core business telecom revenues for the industry as a whole, accelerated consolidation among key customers in finance and other bandwidth intensive industries, slowing growth rates in substitution markets such as wireless, maturing new and disruptive technologies such as VOIP and Wi- Fi that present entirely new competitive threats and marketplace dynamics, new consumer behaviors and preferences towards lower cost media of communication (Voice to Email to Instant Messaging and Text Messaging), and the emergence of new competitors, services and network offerings (ebay s Skype service, Clearline s planned Wi-Fi national network, and numerous North American and European electrical utilities offering or planning wireless high speed Internet access being examples). In this environment, net new business customers are harder to come by, and even new offerings are quickly becoming commoditized, with little differentiation in core offerings between vendors. These trends are occurring even as telecom penetration in major markets continues to grow, with wireless usage in some markets already past the 100% mark It is for this reason that we see telecom carriers focusing in their consumer markets on such activities as bundling as many service offerings as possible, rapidly introducing new consumer appliances and services (rich media content and applications), and focusing on key metrics such as post-paid subscriber acquisition rates and average revenues per user. Telecom Providers will also inevitably move from growth-based business models, where success is measured in numbers of subscribers or ARPU, to value-based models where success is measured (internally at least) in numbers of quality subscribers and by per subscriber margins. These trends are reflected as much in consolidation within the telecom industry supplier market and in continuing declines in appliance costs as much as in access costs, as demonstrated by recent forecasts of slower growth in mobile phone production: 4
In B2C markets, while execution challenges are significant, the path from growth to value based business models is straight-forward. B2C markets inherently involve commodity lowest common denominator offerings (ideally with a minimum amount of after-activation client support cost and minimal investments in client service), heavy investments in brand building and marketing, and a concerted effort to shift low revenue, marginally profitable basic access customer relationships to higher value, higher margin premium access ones. Using Predictive Analytics to Better Target B2B Market Segments In B2B markets, the desire to shift customers from commodity offerings to more value added use of the telecom provider s network, systems and services parallel s that of consumer markets. However, fundamental differences exist in marketing and sales strategies, tactics and operational realities. While commodity pricing pressures are significant issues, of equal importance are the relatively higher amount of annual revenues per customer, the significantly smaller (order of magnitude lower) numbers of customers and potential customers, the existence of distinct, independent direct and indirect sales channels from Telecom Providers to the end user business consumer marketplace, the direct marketing and sales costs of securing new customers, and the impact on margins and earnings of providing service level agreement based support for a diverse range of business customers, across industries, regions and needs. As a consequence of all of these considerations, in B2B markets, the customer acquisition and retention process must inherently be more tactical in nature, with marketing being used less to sell commodity offerings, and more to help the field and inside sales forces by targeting regions, company size, and industry segment types where the profits and revenue will be highest. These marketing efforts need to take into account direct and channel business development strategies and the need to balance commoditized offerings from a service delivery and cost stand-point, with targeted execution capability to achieve differentiated positioning the eyes of the customer. Similarly, sales organizations must leverage all available information, as efficiently as possible, and prioritize territorial coverage plans, management of channel partner relationships, and execution of account level sales activities to maximize the impact of customer level sales engagement. 5
Many Telecom Providers see B2B telecommunications as an area for revenue growth, from the perspective of new customers and equally if not more importantly from increased spend through the use of value-added services, such as handheld convergence devices that allow the delivery of basic offerings plus vertically configured and relevant network-intensive services that are at least industry specific, and ideally client specific. In its 2005 survey of growth opportunities in the telecommunications business services markets, Insight Research reviewed 14 vertical markets, based on SIC type classifications and representing approximately 80% of US business establishments. It found that four vertical markets (wholesale and distribution; financial, insurance, and real estate; professional business services; and communications) represented 70% of business expenditures. If three other segments durable manufacturing, health care and retail were added, this share grew to 85%. Looked at purely from the wireless point of view, three business categories (financial, insurance and real estate; healthcare; and transportation) accounted for over 40% of revenue growth. Insight Research also noted that in B2B markets, telecommunications expenditures are a function of four key factors number employed type of occupation, size and number of establishments, and proliferation of Internet access. Given this overriding stratification of the market opportunity, how, then, does a given telecom provider assess and pursue revenue opportunity in the medium sized business market? We see in the approach implied by Insight Research s vertical market solutions orientation, a first level segmentation system emerging, and a basic framework for marketing activities and sales prospecting. Quality telecom provider B2B marketing and sales organizations have always performed market segmentation analyses to determine which industry segments have the best potential for current and future growth, with current customers and prospects. They might then key off of key industry metrics (revenues, number of employees, types of service requirements etc). Finally, as much as a risk check as for any other reason, they may want to ensure the identified targets meet basic cut off criteria. This segmentation system largely driven by external sales and marketing database resources such as D&B and Hoovers, for example -- might be internalized as a targeting tool the marketing and sales organization can use to handle existing customer relationships and to drive prospecting for new clients. Gold clients and prospects who share the same attributes might receive particular benefits; while Bronze category members receive less. These segments are communicated to and understood by the territorially distributed sales organization which is covering contacts and working to identify and pursue opportunities for business development and sales. To illustrate this, a targeting search of the D&B Hoovers (www.hoovers.com) database of companies contains the following metrics: Depending on an organization s marketing and sales strategy, the cut off for mid-market clients may come in the $25 Million 50 employees range and end at the $5 billion 500 employees range with roughly 60,000 potential client and prospect accounts to be covered by internal and partner sales channels. In reality, for customers at least (and by inference for prospects) the segmentation system will key off of telecom revenue spend per year, rather than top-line revenues. Building from this marketing work, and to lay the foundation for these sales strategies, most Telecom Providers currently use a combination of standard targeted marketing tools borrowed from their B2C 6
business (direct marketing based), supplemented by B2B specific sales strategies (seminars, webinars, demand generation programs, and inside and field sales organizations distributed geographically, with territorial and account level assignments etc). In most cases, these techniques achieve mixed results. Targeting vertical markets and promoting business solutions designed to address perceived business pains of specific industries with generic solutions offerings that can be configured to address industry specific requirements and client specific needs is a reasonable strategy. It does result in useful leads for the sales organization. It does result in successful sales, particularly for the better sales team performers. However, leads are fewer then hoped for; and roughly the same segmentation techniques and systems are employed by competitors across the industry making a relatively small number of clients (60,000 nationwide in our example above out of a total business base of 13 million) highly desirable, and highly sought after, and ensuring that all Telecom Providers compete vigorously for their patronage, driving prices down. Leads are fewer than hoped for, and connections between leads and closing rates are not directly evident; causing sales efforts to become unfocused, and resulting in taking on less desirable business to meet quarterly revenue objectives, regardless of the fact that these bookings tend to be most likely to orient themselves to commodity based buyer behaviors (margin erosion and defection). While top tier sales performance is unaffected, organizations struggle to achieve higher productivity and better sales performance from their sales teams as a whole. By overlaying traditional segmentation based approaches, with data driven, data mining based segmentation analyses; substantial improvements in results can be observed and measured for all members of the sales team. In order to best understand the impact of predictive analytics we need to better understand some of the key differences between consumer, business and enterprise markets. These differences are represented on the following chart: we have already discussed, predictive analytics is irrelevant for enterprise relationships. They involve multi-million dollar, multiyear, comprehensive provider agreements with highly configured product and service offerings. Similarly, consumer and small business market segments are driven larger by marketing efforts, net acquisition rates, and shifting average customer spend through promotions and new service option introductions designed to acquire new clients and retain existing ones. While predictive analytics can help in such areas as kiosk store location optimization, and handset forecasting, to ensure optimal coverage for points of presence and optimized supply chains, sales themselves are cost prohibitive. 7
Comparing and contrasting consumer / small business and mid-market business segments, however, is illustrative. With a given portfolio of 10 million wireless customers, each spending an average of $1,000 per year, a telecom provider has a consumer / small business portfolio worth $10 billion a year. Getting each customer to increase spend by 10% ($100 a year) results in an incremental $1 billion in revenues. With an industry average voluntary churn rate (i.e. defecting good quality customers) of 1% per month, 100,000 subscribers (or $100 million in annualized revenue potential) are at risk, each month. This revenue erosion can be addressed by such marketing expense heavy tactics as bundling offers, handset promotions, switch and win-back program offerings and the like. With a given portfolio of 20,000 mid-market business clients, each spending an average of $100,000 per year, a telecom carrier has a business portfolio worth $2 billion. Getting each customer to spend an additional 10% results in a $200 million revenue increase if successful, but requires a per customer increase in expenditure of $10,000. With the same churn rate of 1% per month, 200 business customers ($20 million in annualized revenue potential) is at risk, each month. The numbers are all smaller taken as a whole, but for every individual mid-market business customer relationship, given the commodity dynamics of the business marketplace, the sales challenges are significant. How can this new revenue opportunity be created, and how can the risk of revenue erosion be addressed? The mass marketing based strategies commonly applied are generally ineffective with this audience. Although deep enterprise to enterprise relationships do not exist, each customer has a business relationship with the carrier. Which relationships can and should be covered through the optimal combination of marketing expense and sales coverage. Which relationships, if supported, show the best potential for revenue growth and fit best within an overarching strategic business plan for this lucrative market segment? These are exactly the kinds of questions that a well implemented data mining and predictive analytics system helps B2B Telecom Providers get the best available, data driven answers to. Best Practices Approaches for Applying Data Mining to Target B2B Markets for Telecom Providers Reduced to their lowest common denominators, consumer telecom markets are commodity volume plays, where differentiation comes from providing the best combination of branded and conveniently packaged commodity services (the better bundle ) at the lowest price. Enterprise telecom markets are relationship coverage based, where differentiation comes from providing the most comprehensive, tailored client solution at the lowest price. Mid-market business clients, however, present an interesting confluence for both buyers and providers. Business clients seek personalized or at least tailored client service with completeness of capability, but also represent high relative sales cost to size of prize. The following are some of the key implications of this changing environment as demonstrated by the best practices based approaches of telecom industry market leaders: Creation of Data Driven Business Customer Segments. Market leading telecom carriers are increasingly extending their focus from generic externally defined business segment classifications (such as D&B databases, incorporating SIC, financial and related information) to more highly targeted and segmented analyses of their own business customer base. By combining the data elements contained in external sources with their own data driven experience with B2B clients, Telecom Providers can achieve better insight into the key drivers of revenue growth, profitability and associated risk assessments that can be used to better leverage this data to capture new market 8
share. Their own predictive models for market segmentation can be applied not only to find new revenue opportunity within their own client base, but enable them to score and find new like customer prospects most likely to drive revenue growth. These customers may be identifiable to some extent by generic information from sources such as D&B, but the key drivers for selection are pattern based using the telecom provider s own data. This trend is not unlike the trend in the banking industry to extend generic FICO scores for credit decisions, through the creation of proprietary customer acquisition models which use external data or scores as one of many second order variables that are informative, but not determinative, of the customer and prospect segments to pursue. Extension of Analytics from Acquisitions to Growing Revenues and Improving Retention. Market leading Telecom Providers are also increasingly applying behavioral and transactional level predictive analytics to their business customer databases at every phase of the customer lifecycle from acquisition, through ongoing management and extension, to problem detection and retention analysis. By using predictive analytics proactively, they are able to model the profiles of business customers, forecast spend potential at the individual customer level (and assess the reasons for variances from projections), align their sales activities with prioritized calling and contact management activities and campaigns, and create automated alerts for their sales organizations to target activities towards highest value customer and prospect segments. Automated Strategy Deployment & List Generation. Market leading Telecom Providers operationalize data mining results -- contracting the cycle time for continuously detecting, optimizing and prioritizing sales efforts and providing actionable insight to their internal and partner sales channels. These improved capabilities allow organizations to react to changing conditions faster, reduce the expenses associated with new strategies, increase the level of sophistication of strategies and score cards, and reduce the operation risk of errors during this process. Integration and Optimization of Marketing and Risk Elements in Individual Segment and Customer Level Strategies. Market leading Telecom Providers will also increasingly combine bureau and internally developed credit scores with associated customer and transactional level information, so that they can more effectively align corporate and marketing objectives with the credit decisioning process, and focus on delivering highest margin customer relationships, (although this will flow from their consumer and SOHO business approaches, where this approach will be deployed first with positive impacts in revenue growth and risk mitigation). The result will be increased involvement of risk managers and analysts in the design, development, marketing, targeting and sale of products to specific consumer segments. In a business environment increasingly focused on the value of customer relationships, and less focused on growth for the sake of growth this marriage of marketing tactics, sales activities and risk policies will help Telecom Providers strategically exploit a far broader range of business opportunities with higher risk clients, but with appropriate mitigation of this risk. This trend is not unlike the increasing use of credit scores (traditionally reserved primarily for banks decisioning systems) by insurers to drive underwriting and pricing decisions for existing customers and prospects. These trends are noteworthy not only for traditional telecom carriers focused on growing their B2B relationships, but for all market intermediaries within and around the telecom industry who wish to explore business expansion with this market segment of small and mid sized business organizations who have a communications requirement as part of an overall solution delivery need. 9
Operationalizing Best Practices in Market Segmentation and Sales Targeting With Proprietary Capabilities Leading telecom carriers no longer find it acceptable to simply buy databases, contact information, or other externally provided leads or sales information to drive their marketing efforts and their sales initiatives. Increasingly, predictive analytics capability, and the ability to apply that capability across their portfolio of small and medium sized business clients and prospects, is viewed as a critical strategic competitive advantage for superior bottom line business performance, market share growth and competitive success. This development places increased pressure on marketing and sales organizations, for managers and analysts. The first and most significant challenge all business to business marketing and sales organizations face is that typically they are resource constrained. These resource constraints flow from two key areas firstly, lack of budgeted financial resources to support systematic improvements in their sales management tools, systems and processes in a business environment where carriers primary focus is on cost-containment and reduction; secondly, lack of knowledgeable personnel resources that can take on the significant intellectual and technical work effort required to apply predictive scorecard development approaches and implement new ones in areas of the business. Sales and marketing business analysis team face the following key challenges: Need to continuously respond to data-driven questions, often on an ad-hoc basis. Deal with disparate data sources that are owned by IT. Lack of data analysis expertise on the team and slow unfocused response from overburdened analytic teams (where they exist), which are often independent of the sales and marketing function. Demand for a wide range of analysis from revenue and inventory forecasting to location and competitive analysis. Lack of trust from the sales force that is naturally driven by gut instinct. These challenges cause business analysts to be disorganized, spend little time in analysis, randomly capture bits of knowledge in personal silos, and generally stumble in the attempt to effectively roll out sales optimization and other marketing initiatives. In the B2B space these challenges many times results in analysts belief that revenue growth with data analytics is practically impossible. A structured phased approach to predictive analytics is an effective means of addressing these challenges. Predictive analytics can help focus the specific revenue objectives and associated tactics, to re-purpose financial and operational data, to build stable and reliable models for forecasting revenue and identifying potential sales targets and profitable customers, as well as to strategically deploy implied sales tactics consistent with the structure of the sales teams. Finally, a predictive analytics program can help track true ROI in a convincing and unequivocal fashion, helping to motivate sales teams. 10
Overview of the Angoss Solution Telecom Marketing Analytics is a sales marketing support solution for the resource constrained and highly competitive Telco industry, tasked with growing profitable revenue in mid-market business segments. Telecom Marketing Analytics is based on applying predictive analytics at each stage of the marketing, sales and risk process -- strategic segmentation of customer base for customer profiles, estimation of potential value for existing and new customers, followed by prioritized leads to sales aligned with territory plans, and risk reward score carding at time of original relationship and at every stage thereafter. These are delivered with and supported by world-class predictive analytics software and related implementation services from Angoss Software (www.angoss.com). Using a phased in approach, the program is designed to quickly deliver measurable ROI, while minimizing any hardware, software or technical issues. With increasing demand for improvements in areas of business to business marketing and sales, Angoss has introduced to the market new tools and systems to lower time and cost to analytics results, and put more effective capability in the hands of both power users and a broader audience of business analysts within the marketing and sales organization to enable this process. This white paper assumes the reader is familiar with the white papers for Angoss KnowledgeSEEKER and KnowledgeSTUDIO, as well as the standard StrategyBUILDER module included in both products, which provide detailed discussion of the deployment and use of these Angoss software programs for marketing, sales and risk management applications. This white paper illustrates for the reader how Angoss technology can help Telecom Providers to better assess, manage and monitor sales performance for mid-market business telecom clients through the lifecycle process by providing a fully comprehensive platform to develop and monitor prospecting and customer models, scores and strategies. It explains how these Angoss systems are used to enable organizations to speed up the processes, increase automation while monitoring results and broaden the human resources capable to complete the analysis, by using not only highly skilled statisticians but also more business focused analysts. Angoss Implementation Approach for Creating Data Driven Segments The Angoss Telecom Marketing Analytics solution applies our predictive analytics system to the client s own data and the 3rd party data it uses to build its segment and target based marketing and sales strategies. An ROI model is also developed and return on investment is tracked and measured at each stage of the implementation process. Angoss delivers Telecom Marketing Analytics using a phased implementation approach, consisting of three essential steps: Discover: During this phase, Angoss completes a Strategic Segmentation of the client s mid-market business opportunities, working with the client s personnel to assemble, analyze and mine their data. The key deliverables include a Strategic Segmentation system that can be used in tandem with or as an overlay for their marketing and sales segmentation tools. Predict: During this phase, Angoss build on the Strategic Segmentation system a series of predictive models and scorecards to rank and prioritize best sales opportunities for both existing customers and prospects. These predictive models include customer profiling and acquisition models, customer 11
retention and churn models, customer and prospect predicted spend models and customer up-sell opportunity models). They key deliverables resulting from this phase include customer and prospect level ranking scores identified by combining various tactical predictive models (e.g. predicted revenue for a given product line, likelihood to defect, etc.) coupled with strategic segmentation (e.g. customer segment defined above) and other ad-hoc criteria, to determine the appropriate sales strategy to be applied in terms of message, sales channel (telesales vs. an in-person meeting), and timing and frequency of sales interactions. Act: During this phase Angoss works with the client to distribute leads, measure and refine contact strategy (e.g. meetings vs. calls vs. no coverage). Angoss also defines and measures the ROI, often against a baseline of control samples. In the example shown in Figure 1 below from the Angoss system, segmentation has been performed on the telecom client s small and medium sized business client base using its own data. The segmentation naturally shows the patterns and relationships indicative of higher value customer relationships. The segments are all fully documented, described in detail, and best of all reflect the realities of the telecom provider s own customer data. Figure 1: Interactive segments report summary showing mid-market customers and revenues. Segments 1,2,6,7 and 8 are significant contributors to revenues, despite representing a small proportion of total customers. Segments 3, 4 and 5 are the opposite. 12
The figure below (Figure 2) shows an example of a deployment strategy to acquire high potential value customers, develop under-penetrated existing accounts, and retain high value existing profitable accounts. The strategy is based on customer value scores and churn model scores. Figure 2: Deployment matrix showing contact strategies based on a revenue potential model and customer value segmentation. The red cells represent customers that are highest priority based on potential value. The Angoss Telecom Marketing Analytics system follows a disciplined, methodology based approach to assess market opportunity, perform segmentation, and create targeting opportunities, including the following: Data Preparation Audit data quality, preview variable distributions Study relationship between variables Build mining views data mining mini-mart Strategic Segmentation Determine variable mix for clustering Perform additional variable transformations Determine number of segments Build detailed profiles 13
Modeling & Model Validation Translate business objectives into dependent variables Test a variety of modeling approaches Select best model based on validation \Generate model drivers and associated business rules to enhance data insights Build economic model and associated revenue estimates Generate associated reports Conduct deployment exercise with field team Revise and refine model based on feedback from field team Revise and refine reporting style based on feedback from field team Design deployment strategies Construct deployment scenarios by applying segments, model scores, and revenue estimates Produce finalized management reports Potential revenue and penetration reports Detailed segments and target profiles Projected ROI Execute initiatives and monitor performance The Angoss solution is also delivered with market proven software, that Angoss clients can migrate in house at any time. 14
Angoss flagship product KnowledgeSTUDIO can help on every step of the model development process. With powerful data profiling capabilities, analyst will be able to assess the quality of the data in no time and produce graphs and tables to review every variable individually as well as inter variable relationships (Figure 3). Figure 3: Profiling in KnowledgeSTUDIO 15
The Angoss system includes extensive data mining and modeling techniques such as cluster detection, regression analysis, neural networks, and decision trees. For each of the data mining processes and predictive models created to perform customer segmentation, predict future spend, create churn risk scores and the like, the modeling scenarios can be analyzed and validated within the Angoss system using a variety of model validation techniques (Figure 4). In the example shown in Figure 5 from the Angoss system, a potential revenue analysis has been developed for the medium sized business portfolio so that the value of market segments may be assessed. Figure 5: Sample potential revenue by Mid-Market segment: Angoss TeleGUARD system 16
Angoss Implementation Approach for Deploying Targeting Tools to Sales As noted above, the capability to conduct strategic segmentation, and provide proprietary scores that can be used to support marketing efforts and customer level sales targeting is only one aspect of the differentiated capability of leading telecom carriers to use predictive analytics to drive business value. A second source of competitive advantage is derived from combining predictive models and scores with user defined strategies, constraints and thresholds to enable highly targeted strategies to be designed and executed across their customer base as a whole faster, and also at extremely granular subsegments of their customer populations where risk-reward trade-offs and result in new sources of revenue and profitability, if appropriately managed. The standard Angoss StrategyBUILDER module within KnowledgeSTUDIO empowers analysts with extensive strategy design, authoring and assessment capabilities fully integrated with the KnowledgeSTUDIO predictive modeling environment. With StrategyBUILDER analysts can: Design, update and optimize strategies to be applied across their client base and at segment levels, by supporting the ability to: -- Interactively define segments and develop treatments for them by examining and incorporating multiple performance variables, including predictive variables and scores -- Assign specific actions or decisions (treatments) for each segment -- Verification and estimation of return on investment, profit and other benefits of any given strategy or strategies -- Provide the analytical and documentary support to enable executive sign off and approval of strategies, speeding deployment cycles while lowering time to strategy Convert analyses and designed strategies into execution formats that can be deployed to common sales force automation and contact management systems, such as Goldmine, Act!, Salesforce.com, the Microsoft Dynamics CRM platform, and Oracle Siebel. Monitor and track back results achieved from strategies deployed to the sales organization, including calculation of return on investment, return per targeted customer and similar metrics. Angoss supports many approaches for executing strategies: Translation, with the support of code generators, into code that runs in different environments for batch and real time scoring and decisioning. Automated (or manual) batch scoring (or decisioning) to populate databases or operational applications Using KnowledgeSERVER to support automated batch or real-time scoring and decisioning 17
Angoss Telecom Marketing Analytics Reporting and Analytics Angoss can also assist in the implementation of and deliver solutions for reporting and CRM systems required to monitor a small and medium sized business portfolio of accounts. Typical reports for telecom carriers are: Customer Portfolio Profile Customer Value Report National Summary Report Industry Spotlight Report Sales Penetration Reports Account Value Breakdown Report Segment Migration Report Key Drivers and Business Rules Insight Reports Model Performance Report Angoss Solution - Key Features and User Benefits Angoss supports many approaches for executing strategies: Usability. Exceptional ease of use and low learning curve enabling business analysts to become more actively engaged in the knowledge discovery, analytics and strategy design and evaluation process, with lower overall systems, resource and user training and support costs. Extensibility. Enabling business analysts and power users to have access to a fully integrated suite of advanced capabilities for credit lifecycle management, including reporting, ad hoc analysis, and strategic capabilities as well as batch and real time scoring Flexibility. Enabling analysts and power users to support underwriting and origination optimization, account management, collections and default analysis and regulatory compliance from a single unified environment. Scalability. Industrial strength analytics and deployment capabilities across large scale enterprise environments. Deployability. Enabling systems administrators to support multiple users across consumer and small business portfolios from a single client-server configuration Interoperability. Open APIs, enabling organizations to easily fit Angoss within their existing legacy analytics environments, with the ability to read and write SAS files and automatically generate production code in a variety of languages including SAS, SQL, Java and XML, and the ability to integrate batch and real time scoring and decision execution capabilities with a variety of enterprise applications, databases and file formats. 18
Telecom Marketing Analytics can help your organization to: Grow wallet share for current business customers Acquire higher value new customers, with more focused tactics Improve marketing effectiveness by delivering data-driven segmentation and targeting tools Optimize sales team effectiveness and improve team performance, with prioritized leads and analysis of sales results. Business value returns are identifiable and measurable, making return on investment easy to determine. Telecom Marketing Analytics implementation features including the following: Strategic Segmentation. The initial implementation phase (Discover) not only provides valuable insight into the current customer database, but also provides the return on investment framework and estimates needed to support full deployment. Predictive Intelligence Enables Efficient Targeting. Telecom Marketing Analytics analysis, modeling, and reporting provide complete predictive insight for marketers and sales managers. Managers can prioritize and map sales activities and marketing initiatives to best sales opportunities, by rep, region and product type. Easy Implementation and Capability Transfer. Telecom Marketing Analytics is easy to implement because it uses currently available data assets. Flexible Deployment Options. Telecom Marketing Analytics deliverables can be configured to meet client needs. Deployment options range from a fully outsourced solution to installed software at the customer site, and anything in between. The deployment options can also be changed as the client requirements change. Industry-Leading Software. Telecom Marketing Analytics is based on industry-leading software and best practices built on over a decade of deployments at major Telcos and financial services organizations. 19
About Angoss Software As a global leader in predictive analytics, Angoss helps businesses increase sales and profitability, and reduce risk. Angoss helps businesses discover valuable insight and intelligence from their data while providing clear and detailed recommendations on the best and most profitable opportunities to pursue to improve sales, marketing and risk performance. Our suite of desktop, client-server and indatabase software products and Software-as-a-Service solutions make predictive analytics accessible and easy to use for technical and business users. Many of the world's leading organizations use Angoss software products and solutions to grow revenue, increase sales productivity and improve marketing effectiveness while reducing risk and cost. Corporate Headquarters 111 George Street, Suite 200 Toronto, Ontario M5A 2N4 Canada Tel: 416-593-1122 Fax: 416-593-5077 European Headquarters Surrey Technology Centre 40 Occam Road The Surrey Research Park Guildford, Surrey GU2 7YG Tel: +44 (0) 1483-685-770 www.angoss.com Copyright 2011. Angoss Software Corporation www.angoss.com 20