Using Bayesian Belief Networks for Modeling of Communication Service Provider Businesses Pekka Kekolahti Department of Communications and Networking Aalto University Espoo, Finland Abstract factors. These include new business players like Google and Facebook, technologies like the Internet, cloud computing and smart-phones, as well as a growing This paper analyses the usage of Bayesian Belief number and size of applications. It is clear that the CSP Networks (BBNs) for Communication Service value chain structure has to be re-evaluated. To respond to Provider (CSP) business modeling and these changes and customer requirements, and to adapt simulation. Large and complex BBNs have been successfully to new business challenges, CSP top created to describe the causal relationships in management needs reliable methods to model and to CSP business domains. As a part of the study, a analyze the essential factors driving the change, and to novel method to collect knowledge from a large understand the impact of these factors on their current and number of independent experts living in different future business. In addition, trusted and unified countries has been introduced. A BBN from each information is needed for strategy planning processes and expert result was created (referred to here as a day-to-day management. Today the strategic decisions are sub-BBN). Business model ontology was often made by a small group and they are based on utilized to combine sub-BBNs together into a insufficient knowledge due to lack of data or expert comprehensive model. The resulting BBN knowledge and under time constraints. represents typical business circumstances in the European telecommunications domain. The Bayesian Belief Networks (BBNs), also called belief experts participating in the study represented networks, Bayes Nets and causal probabilistic networks expertise in different business related categories are increasingly popular methods for modeling uncertain such as technology, processes, customer and complex domains (Uusitalo, 2007). In this paper we experience, regulation, organization and examine how the BBN methodology can be utilized to products. Experts were asked to list causality triplets for business categories including causal help CSP management in their day-to-day work, strategy connection strengths, in order to assess the belief planning and to better control the business. part as well. The triplets were manually converted to a graphical causal map and A BBN is a probabilistic model which represents a set of conditional probability tables constructed. The random variables and their conditional dependencies via a benefit of the method is the capability to directed acyclic graph. Two basic approaches are used to introduce rapidly a high number of variables and construct Bayes networks: data-based and knowledge- causal relationships. A challenge is that experts based approaches. Data-based methods use conditional use different terms with the same underlying independence semantics of Bayes networks to infer meaning. models from data whereas the knowledge-based approach utilizes causal knowledge from domain experts to construct BBNs. The benefits of BBNs in data analysis 1. INTRODUCTION are, according to Nadkarni, 2004; Uusitalo, 2007; Jensen, 2001; Lee, 2009: The Communication Service Provider (CSP) business is 1) Possibility to combine prior knowledge and data, facing major restructuring due to several disruptive 2) Managing situations where some data is missing, 3) Modeling of causal relationships, and validated with experts through subsequent interviews 4) Structural learning possibilities, and finally the probability assessment done either 5) Support for different kind of analyses, such as manually or by using noisy-OR method or weighted sum making inferences about probabilities of algorithm utilizing compatible parent configurations different causes given the consequences and (DAS, 2004) to reduce the number of probability 6) Fast response to queries from the model. assessments. Known challenges in BBNs are This study focuses on BBNs as a methodology for 1) Difficulty to obtain prior knowledge in a form modeling and analysis of CSP business. As part of the that can be converted into probability study, both multiple sub-BBNs (one per expert) and a distributions. However, for example a weighted comprehensive CSP BBN combining sub-BBNs have sum algorithm utilizing compatible parent been created. The experts were asked to list and configurations has been developed to ease the categorize the variables they considered to have an effect calculation of conditional probability tables in on CSP business and also how strong this effect would be. complex environments (Das 2004). The used seven categories are the same as in typical 2) Handling of continuous variables only in a Balanced Scorecards and business models (Kasperskaya, limited manner (Uusitalo 2007) and 2006; Osterwalder, 2002 and 2005; Faber, 2003) namely 3) Lack of support of feedback loops due to acyclic financial variables, customer-related variables, product nature of a BBN. Feedback loops are useful and service innovations, staff and internal processes, when analyzing phenomena like new disruptive technology and architecture, strategy and competition, CSP technologies as a function of time (Casey et local and global economy and legislation. al. 2010). The following types of information can be derived from According to our knowledge, BBNs have in the past not the comprehensive model and sub models: been used to model the CSP industry in a large scale. The  Financial variables: Effect of variables like utilization of causality itself is wide spread in business customer experience on revenue, OPEX management due to widely used performance measuring (operating expense) and CAPEX (capital and management tools such as the Balanced Scorecard expenditure). (BSC) and Tableau de Board. 66% of enterprises used  Customers: The causes and consequences related BSC in 2007 (Rigby, 2007). Both the BSC and the to customer satisfaction. Tableau de Board rely on causal assumptions  R&D organization: How do organization agility, (Kasperskaya, 2006). Causal mapping tools like fishbone managerial structures, salary and incentives diagrams, cause-and effect diagrams, impact wheels, issue affect on efficiency, productivity, OPEX and trees, strategy maps, and risk-assessment mapping are customer experience. tools to help managers to understand and improve  Technologies: How do new technologies like complex systems in the areas of quality, strategy, and rapid growth of smart-phones affect on CAPEX, information systems. (Scavarda et al., 2006). The revenue and data traffic. causalities in the performance measuring and strategy creation have been normally deduced by using human BBNs that include all the seven categories are very interaction techniques such as brainstorming or complex. The number of variables and arcs, and interviews. These methods rely on person-to-person or especially the size of conditional probability tables play group interaction in eliciting the knowledge and are great effect on the practical usability of the BBN for CSB fraught with biases associated with inter-person business analysis purposes. Optimization between dynamics. Methods to elicit a non-biased knowledge in practical usability and model granularity and accuracy is large scale have been developed (Nadkarni et al., 2004; examined through creating the comprehensive BBN from Scavarda et al., 2006). Scavarda introduces a formal sub-BBNs. Collective Causal Mapping Methodology (CCMM), which collects information asynchronously from an expert The remainder of this article is structured as follows: group which is dispersed and diverse. Person-to-person interaction possibility is eliminated and a large amount of Chapter 2 introduces a novel method for the collection of experts can be utilized in a controlled way. Nadkarni the expert knowledge, and describes how the expert introduced a procedure for constructing BBNs from knowledge is converted into BBNs. domain knowledge experts, where through four steps of a text analysis process the first round interview results can Chapter 3 describes the constructed sub-BBNs and be converted into causal relationships. Once the causal comprehensive BBN and elaborates on key variables and map is available, the states of the variables can be defined their analysis states. Also some result examples are given. Chapter 4 discusses challenges in eliciting and conversion Table 1: Part of given example triplets. of prior knowledge into BBN and how well these models truly represent different aspects of CSP businesses. Also future research topics for this line of study are identified. Causing - List of variables Effected variable(s) variable(s) 2. METHODS Number of staff 2 Marketing effort 2 Market share, 1 aver. 2.1 THE KNOWLEDGE COLLECTION METHOD service usage, 2 OPEX Five targets were set for the developed method: 1) to Network equip. need 1, Number of staff 3 OPEX, 2 marketing combine different expert knowledge from various current network effort business categories with the help of a broad expert team equipment capability 1 and 2) give the experts freedom to focus on those causalities they feel, by their expertise, to be important in order to make sure that new innovative cause- consequence –relationships would arise, 3) to discover as much as possible variable candidates from CSP business domains, 4) to ensure that the experts acted as individuals and no group –thinking possibility existed and 5) to facilitate also disruptive proposals. Thus a pre-defined variable list was not introduced but instead experts had freedom to also name the variables. The financial category was seen more a deterministic than a probabilistic cause- consequences structure and thus it was decided that only a few experts need to be dedicated to financial topics. An email was sent to 100 expert candidates working in 12, mostly European countries, for CSPs, universities, CSP infrastructure vendors and software and consulting companies which offer services to CSPs. The email included extensive background information about the study targets, introductions of BBN and causality, example variables and an excel template based on the Figure 1: A causal map of two triplets from Table 1 seven CSP business categories. With the help of the including strengths. template, experts were asked to list variables they considered to have effect on CSP businesses and to categorize the variables to the correct category. Basically 2.2 SUCCESS OF KNOWLEDGE COLLECTION experts were asked to list causality triplets of “variable X Out of 100 expert candidates, 48 answered with survey has some cause on variable Y, which has some effect on results. The resulting causal models were reviewed with variable Z”, see table 1. It was supposed, that with this 60% of these 48 experts. The distribution of expertise method, an expert can easily just start to write the triplets was: without need to first have a big picture in mind. In addition, experts were asked to estimate the strength of  Product and service innovations 21% effect by using numbers:  Technology and architecture 20%  Staff and internal processes 20% Strong effect=3,  Strategy and competition 19% Moderate effect = 2,  Customers-related 11% Weak effect = 1  Local & global economy and legislation 5% These values were used for measuring the expert’s  Financial 4% degree-of-belief value for causal connections. The plan Experts used between 1 and 5 hours for the survey, with was to use a simplistic method, where both weight and the average being 2,5 hours. More than 2200 variables belief parts originate from this strength of effect. and 3400 arcs and 40 sub- BBNs were created from the Triplets are in fact mini causal maps (see Figure 1) and survey results. Text analysis (www.textanalyser.net) was constructing of one full BBN required combining these used in order to understand word frequencies used in triplets together. This was done with a BBN tool called variable names. Out from about 5000 used words, 40% BayesiaLab (www.bayesia.com) by hand. The plan was to were unique. The top 12 used words for variable names review the achieved model with each expert. were “product and service” 80 times, “customers” 60 times, “costs” 56 times, “market” 50 times, “product” 36 times, “brand 28” times, “new” 22 times, “revenue” 20 times, “price”, “marketing”, “personnel”, “network” 16 times. From the text analysis it was clear that:  The process to create a comprehensive BBN is challenging because of the high number of different variable names that have closely the same meaning. The plan was to give full freedom to experts in order to make sure that there were innovative approaches, but this study demonstrated clearly the need of business dictionary if Bayes Belief Networks are to be widely used in CSP business modeling and simulation.  The competition for customers and tight cost control in European CSP markets might explain Figure 2: Sub-BBNs derived from expert surveys covered the top 12 used words, as the majority of experts in 70% of cases all seven categories of CSP businesses were from European countries. but granularity varied greatly depending on expert. 2.3 CONSTRUCTION OF A SUB-BBN converted them in the following way: 3=> 90%, 2=> 75%, 1=> 60%, -3=>10%, -2=>25% and 1=>40%. It was quickly concluded that the creation of a comprehensive CSP business model directly from triplets In further studies, when the model(s) will be tested in was a too complicated task. It was decided that individual CSP environment, the dual review method with experts BBNs, called sub-BBNs would be first created. One sub- will be used, namely first a causal model review with BNN was created per expert and then the comprehensive states alone, and after it second review with weights and BBN was merged from these sub-BBNs. This approach confidence values. has two benefits: 1) it filters out excess of variables with the same meaning in the sub-BBN review –process with 2.5 CONSTRUCTING OF COMPREHENSIVE BBN the expert and 2) innovative sub-BBNs will be documented individually. The 40 sub-BBNs varied in granularity and coverage (Figure 2) because experts were not asked to focus solely The creation of a sub-BBN is straightforward: Variables on their own expertise topic. Merging the sub-BBNs to a and their causal connection were created manually from comprehensive BBN became challenging without a triplets by using BayesiaLab-tool (www.bayesia.com). A standard “kernel”. The Osterwalder business model model review was organized whenever possible with the ontology (Osterwalder, 2002) is used as a standardized expert including the states. Each variable has typically causal kernel (Figure 3) to which sub-BBNs was merged. only two states which describe best the variable in question like true/false, big/small, high/low, positive/negative, fast/slow. 2.4 PROBABILITY CALCULATIONS FOR A SUB- BBN The conditional probability tables were calculated with weighted sum –algorithm utilizing compatible parent configurations defined by Das (Das, 2004). This algorithm allows for simplification of the calculation through the utilization of compatible parent Figure 3: Osterwalder business model blocks, which configurations for the evaluations performed by the are used as “a kernel” for comprehensive BBN. expert, limiting the need of individual probability state combinations needed to be evaluated. The comprehensive BBN can be seen as an onion-like For this study a simplistic method was used in structure, where the kernel is from the business model calculation: The weights 3, 2, 1, -1, -2, -3 were used as ontology and surrounding layers represent experts’ sub- relative weights and the same weight as probability after BBNs (Figure 4). Figure 4: The comprehensive BBN constructs onion type of layers (black and blue circles) around the kernel model (yellow circles) Comprehensive BBN with different granularities (number merged from individual BBNs. Chapter 3.1 gives three of variables and arcs) were created to test the tool and examples of innovative sub-BBNs, which can be used, not computer environment constraints. When the number of only as an input to the comprehensive BBN but also variables exceeds 100, and at the same time the independently. Chapter 3.2 presents results on the relationship between number of arcs divided by number comprehensive BBN. of variables is on the average greater than three and if a few of variables have five to ten common effects, the practical utilization of the comprehensive BBN for different kind analysis decreases due to slowness of the 3.1 SUB-BBN EXAMPLES PC-environment. The objective of this study is not to Example 1: A generic purpose financial causal map with focus on the tool usability nor model complexity topics 32 variables and their relationships (Figure 5). Many of but to discover a Bayes Belief Network which can be the variables and causalities are more deterministic than utilized in practice, contains all the seven business probabilistic and values are results of mathematical categories and which reflects the expert’s common view equations like calculation of EBITDA (Earnings before about CSP variables effecting on business. Interest, Taxes, Depreciation and Amortization). This The merge process was performed manually, with map can be used to analyze the effect of non financial variables and arcs being combined from each sub-BBN to variables analyzed in other sub-BBNs connected to a the comprehensive network around it’s kernel. If certain comprehensive set of financial variables in this model. variable and causal connection existed in many sub- Example 2: The variable “new business opportunities” is BBNs, the weights (used in sub-BBNs) were summed a parent variable for many new business opportunities for together. Thus, if 10 sub-BBNs have a variable “customer CSPs in a electric-car ecosystem (Figure 6), The business satisfaction” affecting with weight 3 “customer loyalty”, opportunities vary from traditional bit-pipe services to then the combined weight is 30. The conditional content service opportunities. The model contains probability tables have been calculated with the same variables such as the effect of regulator actions, method as described in sub-BBN-case. However, a dual environmental circumstances, renewal energy portion, review method is planned to be used when the model will new technology, price of electricity, price of a electric car, be tested in real life. number of electric cars and emergence of new business opportunities. The model offers ways to analyze the 3. RESULTS effect of different ecosystem variables on potential new services. The states and probabilities of key variables in This chapter presents both sub-BBNs, created based on the model are shown in Figure 6. individual expert’s survey and the comprehensive BBN, Figure 5: A generic purpose financial –related causal map. The red circles represent financial, yellow customers, blue staff and processes, greed product, black technical and pink competition-strategy –related variables. This model in its many parts is deterministic in nature and don’t contain probabilities in this study. Sub-BBN’s end up often to Sales, ARPU, R&D etc (blue disks) and with this map the financial analysis towards EBITDA , Earnings per share, expected cash flow and investment decisions (orange disks) can be extended. Figure 6: Key causal structure (upper part), states and conditional probabilities (lower part) of some variables in electric car ecosystem model. The variable “New business opportunities” represents potential new business for CSPs. Figure 7: High level sub-BBN for typical European CSP Operations Support System (OSS) based on one expert’s views. OPEX and CAPEX targets have been set to 100% in order to test the consequences: Automation rate needs to be enhanced, similarly more investment, head count reduction and activities to enhance perceived user quality are needed (red arrows). Example 3: The task of Operations Support Systems Belief Network as a methodology for business reasoning (OSS) is to take care of day-to-day infrastructure and what-if analysis. management so that the network and related services Also other innovative sub-BBNs were created, such as work properly with high quality and in an optimized way. IPTV model, customer experience & satisfaction model, OSS BBN parent variables are the number of today’s regulator causalities model. management platforms (rather low), investment capability of the company (often restricted), current OSS 3.2 THE COMPREHENSIVE BBN architecture (often complex), network performance (often not enough) and harmonization need (typically high). The comprehensive Bayesian Belief Network was created The target variables in the model are OPEX and from sub-BBNs as described in chapter 2. The BBN revenue/profitability. The model covers variables like contains the kernel shown in Figure 3. The model training needs, technology, head count, perceived quality (Figures 8 and 9) contains the 32 most used variables and seen by customer, customer experience and automation their 93 causal connections. It is remarkable that three need (Figure 7). The model, even though it is on a rather variables are very central in the model: 12 variables have high level, demonstrates the great potential of Bayes variable called “Customer experience & satisfaction”, 9 Figure 8: The comprehensive Bayesian Belief Network representing the overall feedback of the survey with granularity of 32 variables and 93 causal connections. The blue disks are the six variables, which have highest node forces as a sum of entering and outing arcs forces. Three from them, namely Customer experience & satisfaction, Product portfolio and Activity & efficiency are the central variables in the model. Figure 9: The states and probabilities of comprehensive BBN. The probability of the first state of variable “Product portfolio”, “Customer experience and satisfaction” has been set to 100% (green bars). variables “Product portfolio” and 5 variables “Efficiency” environment where customer experience, efficiency and as a common variable. On the other hand there is only one portfolio play important role. The 32 variables and 93 arcs purely technical variable even though 20% of experts had in the model were selected as a compromise between technical expertise. The reason for the lack of technical model granularity and usability and based on response variables might be the fact that most of the experts were times in analysis. from Europe and the model represents thus mostly a A light validation has been done for the model to verify mature European mobile and convergent operator’s whether it gives logical results especially because a simplistic conditional probability calculation method, 50 experts, who spent hours by documenting their where both weights for arcs and probabilities originate opinions, deserve a big thank. Special thanks to Gareth from same strength of effect value given by experts, has Smith and Matti Aksela, who created the excel and R – been used. Figure 9 gives an example of the tests: It based macros for calculation of conditional probability shows that when the probability of “Product portfolio” tables and finally big thanks to Antti Myllykangas, who state “competent” is set to 100% and “Customer has altruistic supported me in many aspects of this study. experience and satisfaction” state “high” is also set to 100%, the consequence will be that the revenue will References increase clearly, when it is assumed that product pricing can be higher, efficiency of internal processes will be T. Casey (2009). Analysis of Radio Spectrum Market better, innovation capacity will increase, technical assets Evolution Possibilities. Communications and Strategies, are modern and competent staff will be in place. These No. 75, 109-130, September 2009 validations demonstrated that the model yield logical B. Das (2004). Generating Conditional Probabilities for results. Bayesian Networks: Easing the Knowledge Acquisition Problem. 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