Chapter 7 ROI in text mining projects

Transcription

1 Chapter 7 ROI in text mining projects M. Ferrari Department of Business Economics, University of Modena and Reggio Emilia, Italy. Abstract The evaluation of text mining projects in terms of financial performance is the main topic argued by management involved in judging this kind of investment. However, text mining being a particular case of knowledge management, it has not only a tangible impact in terms of return on investment but also an intangible one: a lot of benefits coming from the investment in this knowledge management solution are not directly translated into returns, but they must be considered in the process of judgment to integrate the financial perspective of analysis with the non-financial ones. 1 Introduction Text mining is a particular form of data mining where the data are in textual format. Seen that the data are in an unstructured format, the data preparation step is longer than usual and requires a linguistic preprocessing [1] to solve (also if partially) the ambiguities. In this sense text mining extends the content analysis concepts [2], which didn t take into account the possibility of working on the text before analyzing it, in the direction shown by [3]. The objective remains, anyway, the same: discovering the knowledge contained in the database, without defining our topic of research [4]. In summary, text mining is the process of analyzing and structuring large sets of documents applying statistical and/or computational linguistics technologies [5], in order to extract previously unknown knowledge useful to take crucial business decisions [6]. These definitions point out that text mining is a particular form of the knowledge management. A typical text mining project takes into account different layers of a knowledge management project: applicative layer (e.g. doi: / /07

2 156 Text Mining and its Applications to Intelligence, CRM and Knowledge Management competitive intelligence), knowledge portals, knowledge management discovery services, knowledge map production, knowledge source integration. For a more general introduction, we make here reference to knowledge management projects, considering text mining one of them. Text mining is the applicative tool to measure and to manage knowledge, therefore it can be considered contemporarily from different points of view: 1. as a particular form of the knowledge management whose introduction in a firm causes an impact on the economic performance, performance that has tangible and intangible characteristics; 2. as the technology to detect and to measure Intangible Assets [7], above all to scan information in order to transform them into knowledge. Recently knowledge management and intangibles in general have received great attention on behalf of academic studies together with the movement of the value: [8] more and more the value creation is attributed to intangible elements with a particular attention to knowledge management. Scholars distinguish between Intellectual Capital and Knowledge management. Even if the two approaches could seem similar and have areas of overlapping, they have a different focus and different analysis perspectives [9]. Intellectual capital has a strategic perspectives, aimed to maximize and to renovate the intangible assets. There are three categories of intellectual capital: human capital is the most obscure part of intellectual capital. It consists of knowledge, competences, capabilities and creativity of the members of the organization. It includes also culture and the organization values [10]. In order to increase the corporate value, it is necessary that members knowledge becomes the organization s knowledge. Text mining has often been utilized to extract this type of knowledge. Some cases are reported in literature to extract corporate culture and employee motivations [11] or employees and candidates competences [12]; structural capital, e.g. the codified and structured knowledge, translated into some tangible elements (database, procedures, patents). It allows sharing and transmitting knowledge and its main function is to store knowledge in order to create customer value and to increase the knowledge diffusion inside the organization [13]; Text mining has been utilized to detect competitors strategies using patents [14] or magazines or briefings [15]; customer capital is represented by customer relations in terms of: information exchange about his own activities and exigencies, involvement of the customer in business decisions, establishment of agreements and in general any kind of relationship aiming to reinforce the link between customer and organization. Text mining is able to extract this information (e.g. claims about products

3 Text Mining and its Applications to Intelligence, CRM and Knowledge Management 157 and services) when expressed in format or, also, when extracted from chat lines, forms, mailing lists [16]. Knowledge management has a tactical approach with the goal to manage the knowledge activities. It can be considered as a set of techniques and of activities aiming to create, to develop and to sustain the diffusion of knowledge inside the organization [17]. This means to create value through an effective use of knowledge. In accordance with this definition, text mining represents a knowledge management solution with the goal of reaching, through a textual analysis, a higher level of knowledge, widespread in the internal business structure. The purpose of this chapter is not a deep dissertation about the different doctrinal models to measure intellectual capital in general, but the identification of a model that is able to capture the intangible impacts coming from the introduction of a text mining solution. After a review of the up-to-date studies in this field, we ll focus the attention on the model that we judge suitable to point out the link between the investment in a text mining solution and the performance indicators. 2 The evaluation of a text mining solution When corporate executives evaluate an investment in a project of text mining, the main question which is discussed is to determine the benefits in terms of economic and financial returns versus the cost. In general, the most common indicator used in financial literature to make comparisons between different alternatives and to give a judgment of a project is the ratio ROI. As well known, ROI stands for Return on Investment and it depends on the result of two elements: EBIT and the investment. EBIT 100 Investment EBIT is: earning before interests and taxes. It is not difficult to understand that the real problem to calculate this ratio is EBIT that, paradoxically, is what we are looking for. In fact, in this kind of evaluation we have an element investment that is certain and the other one that will be estimated on the basis of a hypothesis having a good degree of probability. But there is another aspect that cannot be underestimated: this kind of project, like any others of knowledge management in general or of business intelligence, has an intangible component, more or less dominant, that is very difficult to estimate. In fact, Dyché [18] distinguishes the hard ROI from the soft ROI. The former is referred to the real and concrete increase of savings or cash flows, the latter to customer satisfaction, productivity, prestige, technological leadership, personnel satisfaction and responsibility and cultural changes.

4 158 Text Mining and its Applications to Intelligence, CRM and Knowledge Management Customer satisfaction Productivity increase Personnel Satisfaction/ Responsibility Hard ROI Cost Savings Customers Maintenance Technological Leadership Cultural Changes Soft ROI New customer acquisition Increase of market share Figure 1: Hard and soft ROI. [19] In the next pages we try to evaluate these two components tangible and intangible separately. Then we will build an integrated model that synthesizes the criteria of evaluation with financial and non financial measures. 3 The evaluation of the tangible components As seen before, ROI is one of the most common ratio used to evaluate projects [20], but in this context it is necessary to determine EBIT following a process with the goal to estimate revenues and costs before and after the project of text mining. In particular, it means to determine the cost savings of the internal processes, as a consequence of the introduction of a text mining solution, with regard to the single cost areas involved in text mining analysis, and contemporarily/or the increase of revenues caused directly or indirectly by this solution of knowledge management. This approach, theoretically simple, in practice, requires a well implemented system of cost accounting and a process of data elaboration able to simulate the new scenario characterized by the presence of the text mining solution (consulting and services needed to make it operational). In this context the return of the project is represented by the Net Present Value (NPV) of the cash flows coming from the savings that the text mining solution has allowed. Consequently, the ROI formula becomes [21]: NPV of Savings 100 Investment

5 Text Mining and its Applications to Intelligence, CRM and Knowledge Management 159 This profitability index [22] (ROI so determined) is a ratio that combines the features of NPV, internal rate of return (IRR) and payback period, each of them with a specific focus in terms of analysis. In fact, CF1 CF2 CF3 CFn NPV = K+ (1+ WACC) 1 (1+ WACC) 2 (1+ WACC) 3 (1+ WACC) n CF is the cost savings of the text mining solution; e.g. the mathematical difference between the existing situation costs and the new one with the presence of the software; WACC is the average cost of capital n E D WACC = Ke + Kd(1 t) E + D E + D where: Ke = cost of equity Kd = cost of debt E/E+D and D/E+D represent respectively the weight of the equity and debt [23] t is the tax rate Kd = R f + spread for the insolvency risk R f = interest rate for investment without risk Ke is determined according to the Capital Asset Pricing Model (CAPM) [24] as follow: Ke = R f + β MRP MRP = market risk premium (R m R f ) where: R m = market rate for activities with risk β = coefficient for systematic risk; this is a beta levered that takes into consideration also the risk linked to the financial structure of the business that makes the investment [25] is the total number of years considered in the formula The Internal Rate of Return (IRR) represents the yield rate of the investment and is determined similarly to the NPV calculation with the exception that the equation is solved for the variable IRR [26]. In particular IRR is that rate which equals NPV to 0. The formula for IRR is as follows: CF1 CF2 CF3 CFn NPV = CF K+ (1+ IRR) 1 (1+ IRR) 2 (1+ IRR) 3 (1+ IRR) 0 = n 0

6 160 Text Mining and its Applications to Intelligence, CRM and Knowledge Management CF IRR n is the cost savings of the text mining solution; e.g. a mathematical difference between the existing situation costs and the new one with the presence of the software; is the variable for which is solved the equation; is the total number of years for which the IRR calculation is to be applied. The payback period determines the number of years that are required for the discounted estimated cash flows to equal the investment. So this ratio has a time focus. The payback period formula is: Investment Payback period = NPV of Savings Years n n is the total number of years for which the NPV calculation is to be applied [27]. This indicator reflects the subjectivity of the established period as time horizon of the project and, in our opinion, it cannot be considered stand alone, but together with the other ones, having a focus in terms of return, otherwise the only element of judgement becomes the time. This indicator, for example, could be useful in a process of evaluation of different projects coeteris paribus the other economic conditions. 3.1 The evaluation of the tangible components in a text mining project: an example In order to give an exemplification of the application of the above indicators, we consider a hypothesis of a text mining project applied in a competitive intelligence context with the following assumptions: 1. The ROI calculation is based on a 3 year time horizon because of the rapid obsolescence of technology. 2. The duration of the project is less than 1 year. 3. The investment is supposed to be completely financed with equity, so Kd is = 0 and Ke is estimated in 8.5% as the sum of: 4.75 as yield of a BTP [28] with the duration of 10 years [29]. We have prudentially assumed a period of 10 years even if we have considered 3 years of cash flow, because this is an assumption of this example and given the short period, it could cause misleading effects on the determination of the rate. Many scholars, in fact, advise to take into consideration a long-time period [30]; 3.75 as premium risk for the Italian companies [31].

7 Text Mining and its Applications to Intelligence, CRM and Knowledge Management β is supposed equal to 1, considered that there is not a specific reference to a particular industry. However, in the specific context it is necessary to determine the beta levered of that particular industry with reference to the financial structure of the business that invests in this project. Initial Costs Software: Text Mining solution (licences) 200, Annual rent for maintenance support 0 Services: Installation of the software and user training 150, Applicative consulting 150, Total costs 500, Annual Costs Software: Annual maintenance support 32, Services: Annual application maintenance Acquisition of information (databank, reviews ) 20, , Total recurring costs 102, With these assumptions the expenditures for the three years considered as a life cycle of the project would be: Year n Year n+1 Year n+2 Year n+3 Software 200, , , , Services 300, , , , Total 500, , , , The existing situation relative to a case of competitive intelligence, with the analysis of data and information about competitors, has the following estimated costs: Internal annual service 350, Year n Year n+1 Year n+2 Year n+3 Services 350, , , , Total 350, , , , In this example, we have simplified the determination of the costs before and after the introduction of a text mining solution, avoiding long and complicated calculations that would not have given any additional value to our analysis.

8 162 Text Mining and its Applications to Intelligence, CRM and Knowledge Management However, it is obvious that many complex contexts, to determine the hypothetical situation after text mining solutions, could require simulations of the new scenario through a cost accounting system. Year n Year n+1 Year n+2 Year n+3 Text mining 500, , , , Current situation 350, , , , Net savings * 150, , , , * This result could not be meaningful if the project is not finished within the end of the year. In that case it could be prudential to consider the total investment in the NPV calculation (500,000.00), instead of the negative cash flow determined by the difference of the two situations as shown above. 248, NPV= 150, (1+ 0,085) , (1+ 0,085) , (1+ 0,085) 3 3 Consequently, ROI is equal to: NPV = 483, Discounted net savings Investment 483, , ROI = 96.68% The internal rate is determined as follows: , ,00 + (1+ IRR) ,00 + (1+ IRR) ,00 + (1+ IRR) = 0 IRR = % IRR is greater than the cost of capital so the evaluation of the project is positive. Payback period In order to determine the payback period, it is necessary to calculate the average total discounted net savings: Discounted net savings Numbers of years assumed 483, = 161,

9 Text Mining and its Applications to Intelligence, CRM and Knowledge Management 163 Payback period: Investment average total discounted net savings 500, = , The payback period is 1 year, 1 month and 6 days. In synthesis: ROI Internal rate Payback period % % 1 year, 1 month and 6 days From the results of the financial indicators - ROI and internal rate - the judgement on text mining project is extremely good. From a time point of view, in case of comparison with other investments, having the same financial performance, it has a payback period little greater than 3 years, a time horizon that we have considered for this calculation, with regard to the obsolescence of technology. Consequently the period of payback can be valuated positively. The calculation of these indicators can be made also with a sensitive analysis, e.g. the future cash flows (net savings) can be estimated according to an optimistic, middle and pessimistic scenario. In this case the management is conscious of the different probability of verification of cost savings related to the investment [32]. The calculation above considers a middle perspective. 4 The evaluation of the intangible components If it is true that what you measure is what you can manage [33], it is also true that to translate the intangible effects of knowledge management solutions, like text mining ones, into a number or into a financial indicator is not only difficult, but it could be also misleading. In fact, the implications of this kind of solution are extended to many areas that require the considerations and, consequently, the analysis of different perspectives. The measurement of knowledge has been tackled by two different research profiles [34]. The first aims to study the implications that knowledge has on the entire management structure. The Balanced Scorecard, the EVA model, Lev s and Joia s models, and all the studies supporting the use of balance sheets to represent intangible assets both in the scheme and in the explanatory notes, belong to this research line. The second does not consider the involvement of the entire business management, but it has a more circumscribed focus on a quantitative measure for the knowledge, such as an indicator to estimate the effectiveness of the teaching

10 164 Text Mining and its Applications to Intelligence, CRM and Knowledge Management activities or the curves to measure the learning rapidity, etc. This second kind of approach is particularly widespread in the northern European countries and it has the objective of underlying the role of the intangible management and not only of the knowledge management. The Navigator and the Intangible Asset Monitor belong to this research line [35]. However, none of these developed models and studies have defined the measurement unit of the knowledge. This is comprehensible if we consider that knowledge consists of a personal quality that cannot be translated into a quantitative measure. Nevertheless, a lot of scholars have proposed indicators with the goal of trying to establish a link between different aspects of business performance and the knowledge. These contributes are not original in terms of new indicators, but for the research of a criteria in order to establish a link between them, the areas of knowledge management and the value creation [36]. The areas involved in developing indicators are those mentioned above [37]: human capital, represented by internal competences belonging to people working inside the organization and composed of components of tacit knowledge and values; both elements are difficult to control and to measure; structural capital that consists of codified knowledge, easier to measure than the human capital, because in many cases is closed in some tangible elements like patents or a registered trade mark; customer capital, referred to the customer relations, business image and knowledge about customer taste and preferences [38]. 4.1 The value chain scoreboard On the subject of intangible we have to mention the Baruch Lev s methodology to identify the intangible component of the corporate performance: Economic Performance = α(physical Assets) + β(financial Assets) + δ(intangible Assets) α β δ represent the contribution of a unit of asset to the enterprise performance [39]. The Lev s methods consists of normalizing past earnings (excluding extraordinary items) and future earnings or growth potential. The values of physical and financial assets come from the firm s balance sheet and footnotes. The estimation of α and β, the normal rate of return of physical and financial assets is based on economic studies and analyses. The return on physical and financial assets, respectively α(physical Assets) and β(financial Assets), are subtracted from the estimated economic performance. What remains is the contribution of intangible assets to the enterprise performance, defined by Lev as intangibles-driven earnings. Lastly, the discounted value of expected intangibles-driven earnings over the

11 Text Mining and its Applications to Intelligence, CRM and Knowledge Management 165 estimated period, using a discount rate which reflects the riskiness of the earnings, yields the Intangible Assets. This interesting approach however, in our opinion, is not suitable to our objectives because it determines a value of intangible assets, after a lot of estimations, that includes all the intangible components explaining the economic performance. In these terms it is not simple to isolate a knowledge management component like text mining solution. Lev has then developed an information system with the goal to provide the needs of individual investors, business partners, managers and policy makers [40]. This system has a focus on the value chain as the fundamental economic process of innovation that starts with the discovery of new products or services or processes, proceeds through the development phase of these discoveries and the establishment of technological feasibility, and culminates in the commercialisation of the new products or services [41]. The Lev s value chain Scoreboard is composed of three phases: discovery and learning, implementation and commercialization. It has the objective of underlining the enterprise s capabilities in creating economic value with the stress on innovation and intangibles (R&D, patents, brands and so on). The value chain scoreboard is an information system that has both an internal target, for the decision making process, and an external one for the investor disclosure. Therefore the indicators should be: quantitative, standardized, in order to be compared across time and space for valuation and benchmarking purposes, empirically linked to value, or, in other words, they should be confirmed by empirical evidence as relevant to users. Past Earnings + Future Earnings Normalized Earnings Subtract: Subtract: Equal: Return on Physical Assets Return on Financial Assets Intangibles-Driven Earnings Capitalize: Intangible Assets Figure 2: Lev s intangibles value measurement procedure. [42]

12 166 Text Mining and its Applications to Intelligence, CRM and Knowledge Management Discovery and learning Implementation Commercialization 1. Internal renewal Research and development Work force training and development Organizational capital, processes 2. Acquired capabilities Technology purchase Spillover utilization Capital expenditures 3. Networking R&D alliances and joint ventures Supplier and customer integration Communities of practice 4. Intellectual property Patents, trademarks and copyrights Licensing agreements Coded Know-how 5. Technology feasibility Clinical tests, Food and Drug Administration approvals Beta tests, working pilots First mover 6. Internet Threshold traffic Online purchases Major Internet alliances 7. Customers Marketing alliances Brand values Customer churn and value Online sales 8. Performance Revenues, earnings, and market share Innovation revenues Patent and know-how royalties Knowledge earnings and asset 9. Growth prospects Product pipeline and launch dates Expected efficiencies and savings Planned initiatives Expected breakeven and cash burn rate Figure 3: The value chain scoreboard. [43] These three characteristics ensure that the information system satisfies current needs of users, but at the same time they represent, in our opinion, a limit for our purpose. In fact, the model has the intrinsic goal to inform stakeholders with a specific focus on the innovative process. The investment in text mining solution could belong to the fourth phase: intellectual property. However, when there is an investment in text mining solution, management s interest is on the internal consequences with particular attention to the economic performance and related benefits versus costs. The Lev s scoreboard is oriented to the business performance on the whole and it does not link through an explicit cause and effect relation the impact of the introduction of a new intangible element. Even if the author argues that the model has an internal target, it reflects the main purpose for which it has been elaborated: the improvement of the disclosure towards external stakeholders.

13 Text Mining and its Applications to Intelligence, CRM and Knowledge Management 167 Consequently, in our opinion it is not suitable to capture the effects of text mining solution; we abandon thus both Lev s models for our goals. Another example similar to Lev s one is that elaborated by Joia [44] where it is assumed that: Information = data + Σ (attributes, relevance, context) Knowledge = Information + Σ (experiences, values, patterns, implicit rules) and Market value = Book value + Intellectual capital where: intellectual capital = human capital + structural capital, structural capital = innovation capital + process capital + relationship capital consequently, Market value = Book value + human capital + innovation capital + process capital + relationship capital The author has elaborated a model in which there is a correlation between the market book value and the changes in intellectual capital (intellectual capital elasticity). Also this model has a focus on the business performance, considering the influence of the intellectual capital on the whole, so we think that it presents the same limits to our purpose mentioned above with regard to Lev s models. 4.2 The Intangible Asset Monitor and The Skandia Navigator In doctrine we can find also measurement systems that have tried to measure intellectual performance, interpreting it as a signal of the future business performance. Two examples are: Sveby s Intangible Asset Monitor (IAM) and Skandia Navigator, whose author is Edvinsson [45]. The Intangible Assets Monitor is a method for measuring intangible assets and a presentation format which displays a number of relevant indicators for measuring intangible Assets in a simple fashion. The choice of indicators depends on the company strategy. The format is particularly relevant for companies with large intangible assets, such as Knowledge Organizations. According to Sveiby, The invisible intangible part of the balance sheet can be classified as a family of three: [46]. As shown by fig. 4, the approach followed has a patrimonial nature, e.g. intangible assets are the difference between market value and tangible net book value [47]. Intangible Assets are composed of three categories: external structure, internal structure and individual competences. The first one corresponds to the relations with customers and stakeholders, the internal structure corresponds to the structural capital and the competences to the human capital.

14 168 Text Mining and its Applications to Intelligence, CRM and Knowledge Management Intangible Assets Monitor Market Value Tangible Net Book Value Indicators: Intangible Assets Growth Renewal External Structure Efficiency Indicators: Stability/Risk Growth Renewal Efficiency Stability/Risk Internal Structure Indicators: Growth Renewal Efficiency Stability/Risk Individuals Competence Indicators: Growth Renewal Efficiency Stability/Risk Figure 4: The invisible intangible part of the balance sheet classified as a family of three. [48] In the Intangible Asset Monitor the impact of a text mining solution, with regard to the intangible effects, could be identified in the internal and external structure. In addition, there could be also consequences on the area of the individual competences, but, in our opinion, they could be indirect and probably not short-time perceivable, in dependence on the business context. The IAM is a Stock-Flow theory, the same as traditional accounting theory. When using the IAM one perceives the three Intangible Assets as real assets. We are interested in indicators that indicate change and knowledge flows, i.e. growth, renewal/innovation, efficiency/utilization and risk/stability measures [49]. The Intangible Assets Monitor can be integrated in the management information system. The Monitor itself should not exceed one page. It should be accompanied by a number of comments. Only a few of the suggested indicators [.] should be selected. The most important areas to cover are growth/renewal, efficiency and stability. The purpose is to get a broad picture, so one or two indicators in each category should be designed [50]. As fig. 5 above shows, the intangible assets, relative to external and internal structure and competences are measured with regard to the aspects of: growth, innovation, efficiency and stability/risk. This last dimension represents the capability to sustain future competitiveness. The indicators chosen for each category and referred to each dimension are suitable, according to Sveiby, to explain a trend of development rather than stock elements.

15 Text Mining and its Applications to Intelligence, CRM and Knowledge Management 169 Figure 5: Intangible assets Monitor. [51] Also Edvinsson [52] has a patrimonial approach: intangible assets are the difference between market value and financial capital, but the classification of Intellectual capital is different from Sveiby s one. In fact, Intellectual capital is composed of human capital and structural capital; the second, in turn, is composed of customer capital and organizational capital. The last one coincides with the structural capital mentioned above, while customer capital highlights the author s focus on customers rather than stakeholders in general. Also in this model text mining have the main effects on the organizational capital and customer capital. In particular, there could be significant consequences on the relationship with customer (for example analyzing competitors data, or claims about products and services...) and on the organizational capital concerning the detection of unknown knowledge, using patents, magazines and so on. With regard to human capital, we can confirm the same considerations made above about Intangible Asset Monitor. The Skandia Navigator proposed by Edvinsson as a measurement system of intellectual capital is used both for external communication and internal management of intellectual capital. It is another model that integrates intellectual perspective with the financial one and is composed of five areas of focus: financial, customer, process, people, and renewal and development. Navigator has a lot of similarities with the balanced score card but it amplifies the dynamics of the interrelationships between the five areas. With this, we are able to have a supplementary accounting system that gives you four times the information than just financial information [53].

16 170 Text Mining and its Applications to Intelligence, CRM and Knowledge Management Market Value Financial capital Intellectual capital Human capital Structural capital Customer capital Organizational capital Innovation capital Process capital Figure 6: The value scheme of Edvinsson. [54] The metaphor of navigation constitutes a search for another language of dynamic reporting beyond management. It aims to highlight the continuous process of adding to the long-term sustainability of the organization and nurturing the roots for sustainable value generation [55]. Financial Focus Past Intellectual Capital Customer focus Human Focus Process Focus Present Renewal & development focus Future Operating focus Figure 7: The Skandia Navigator. [56]

17 Text Mining and its Applications to Intelligence, CRM and Knowledge Management 171 In this model the focus on human resources is evident and is so pervasive that human resources are in the centre of the Navigator, strictly connected with the others focus of the business management. The model underlines the link between the different perspectives and time: as the Navigator shows, the present performance is linked to customer and process focus, while the future one is related to the renewal and development focus, a part of the intellectual capital together with human resources. These two models the Intangible Asset Monitor and the Skandia Navigator have the goal to measure intellectual capital in all its components, as we have defined above, in order to establish a link of cause-and-effect more or less direct with financial performance. It is clear that, in spite of their differences, both models are oriented to stress the fundamental role of intellectual capital on future economic and financial performance. Our purpose is not a deep dissertation about the intellectual capital, but a review of the main doctrinal models to verify which of them can be applied in our context. The focus of our question is the examination of the intangible impacts of a text mining solution that is a more circumscribed goal than the two model s one. Both Intangible Asset Monitor and Skandia Navigator, in fact, require an intangible sedimented culture widespread inside the organization. As there are not yet a set of standards about different techniques of measurement, the organizations that have developed this kind of project, determine the indicators with a high degree of subjectivity [57]. In this sense some authors suggest that the role of these models is not an ex-post control of knowledge management but is to make the organization members aware of the importance of the intangible resources in general and of knowledge in particular [58]. For these reasons and for the pervasive focus on intangible on the whole, in our opinion, these models present a level of discretion too high to value the soft impacts of text mining solutions. Consequently, we argue that the Balanced Scorecard, the Kaplan and Norton s model, could offer a more objective interpretation of the results coming from the introduction of a text mining project. 4.3 The balanced scorecard The Balanced Scorecard (BSC) is by now a consolidated and tested model applied in different contexts and its goal is to offer a useful guide to managers to translate strategy into action. The Scorecard composed by four perspectives financial, customer, internal business process and learning and growth provides a picture of the current performance and of the future one through the drivers linked to each other by a cause-and-effect relationship.

18 172 Text Mining and its Applications to Intelligence, CRM and Knowledge Management The four perspectives of the scorecard permit a balance between short-term and long-term objectives, between desired outcomes and the performance drivers of those outcomes, and between hard objective measures and softer, more subjective measures [59]. Financial. The financial measures define the long run objectives of the business unit that serve as the focus for the objectives and measures in all the other scorecard perspectives. Financial objectives and measures play a dual role: they define the financial expectations from the strategy and they represent the ultimate targets for the objectives and measures of all the other scorecard perspectives. Customer. It identifies the customer and market segments in which the business unit competes and the strategic measures of the business unit performance in its targeted segments [60]. Customers value propositions consist of the attributes of products and services through which companies create loyalty and satisfaction in the targeted segments where they compete. Internal business processes. This perspective identifies the critical processes in which the companies decide to excel. Internal processes allow business units to satisfy their customer value propositions and to meet shareholders expectations in terms of financial returns. The internal objectives and measures are typically developed after having formulated objectives and measures for the financial and customer perspectives [61]. Learning and growth. It represents the infra-structure that the organization must build to create long-term growth and improvement. Organizational learning and growth come from the three principal sources: people, systems and organizational procedures. The financial, customer, and internal business process objectives on the Balanced Scorecard will typically reveal large gaps between existing capabilities of people, systems and procedures and what will be required to achieve targets for breakthrough performance. To close these gaps, businesses will have to invest in re-skilling employees, enhancing information technology and systems, and aligning organizational procedure and routines. [62]. Objectives in the learning and growth perspective provide the infrastructure and they are the drivers to enable ambitious outcomes in the other perspectives to be achieved. In summary, the Balanced Scorecard is like a transformer that translates vision and strategy into a coherent and linked set of objectives and performance measures relative to the four perspectives [63]. After having examined the dimensions of the Balanced Scorecard, the construction of the model has been mapped by Kaplan & Norton as patterns into a framework called strategy map.

19 Text Mining and its Applications to Intelligence, CRM and Knowledge Management 173 FINANCIAL Objectives Measures Targets Initiatives To succeed financially, how should we appear to our shareholders? CUSTOMER Objectives Measures Targets Initiatives To achieve our vision, how should we appear to our customers? Vision and Strategy INTERNAL BUSINESS PROCESS To satisfy our Objectives Measures Targets Initiatives shareholders and customers, what business processes must we excel at? LEARNING AND GROWTH To achieve our Objectives Measures Targets Initiatives vision, how will we sustain our ability to change and improve? Figure 8: The Balanced Scorecard. [64] Financial Customer Process Potential Development of strategy Strategy in action Budgeting processes Operating Control BSC Cockpit Figure 9: The capability of the Balanced Scorecard to transform strategy into action. [65] A strategy map for a Balanced Scorecard makes explicit the strategy s hypotheses. Each measure of a Balanced Scorecard becomes embedded in a chain of cause-and effect logic that connects the desired outcomes from the strategy with the drivers that will lead to the strategic outcomes. The strategy map describes the process for transforming intangible assets into tangible customer and financial outcomes. It provides executives with a framework for describing and managing strategy in a knowledge economy [66].

20 174 Text Mining and its Applications to Intelligence, CRM and Knowledge Management The Balanced Scorecard Strategy Map Financial Perspective Improve Shareholder Value Revenue Growth Strategy Shareholder Value ROCE Productivity Strategy Build the Franchise Increase Customer Value Improve Cost Structure Improve Asset Utilization New Revenue Sources Customer Profitability Cost per Unit Asset Utilization Customer Perspective Customer Acquisition Customer Retention Market Share Product Leadership Customer Intimacy Customer Value Proposition Operational Excellence Product/Service Attributes Relationship Image Price Quality Time Function Service Relations Brand Customer Satisfaction Internal Perspective Build the Franchise (Innovation Processes) Increase Customer Value (Customer Management Processes) Achieve Operational Excellence (Operations & Logistics Processes) Be a Good Neighbor (Regulatory & Environmental Processes) Learning & Growth Perspective Strategic Competencies A Motivated and Prepared Workforce Strategic Technologies Climate for Action Figure 10: The strategy map. [67] In other words, strategy map is an architecture that describes the strategy and gives a complete snapshot of the links existing between objectives and the key performance drivers which reflect the uniqueness of the business unit strategy. A properly constructed Balanced Scorecard should have a mix of outcome measures and performance drivers. In fact, on one hand, outcome measures need the presence of performance drivers in order to communicate how the objectives are to be achieved and to provide an early indication about whether the strategy is being implemented successfully; on the other hand, without outcome measures, performance drivers (like cycle time, defect rate, and so on) may enable the business unit to achieve short term operational improvements but will fail to reveal whether the operational improvements have been translated into expanded business with existing and new customers, and eventually, to enhance financial performance. [68]. A good Balanced Scorecard needs the presence of a mix of outcomes (lagging indicators) and performance drivers (leading indicators) customized to the business unit strategy. A strategy is in fact, a set of hypotheses about cause-and-effect relationship that can be expressed by a sequence of if-then statements. The chain of cause and effect should pervade all four perspectives of a

21 Text Mining and its Applications to Intelligence, CRM and Knowledge Management 175 Figure 11: The Balanced Scorecard provides a framework to build strategy focused organizations. [69] Balanced Scorecard and the measurement system should make the hypotheses among objectives and measures explicit so that they can be managed [70]. We have always referred to an organizational structure composed of a lot of business units, but it does not matter whether there is the presence of one or many of them. What it should be evident is that Balanced Scorecard requires an involvement of the organizational structure and that this structure plays a key role in implementing Kaplan and Norton s model. As Kaplan and Norton argue, the Balanced Scorecard is not a rigid model but a flexible one that must be adapted to the business characteristics and to the company strategy. This means that the model reflects the organizational structure of the company and is to be structured according to the strategic objectives, increasing or diminishing the four dimensions, if necessary. The flexibility of the Balanced Scorecard permits integrating the intellectual capital performance and the business performance under the different dimensions. In particular, with reference to the evaluation of a text mining solution, the model can give the possibility to capture the intangible impact through the lag and leading indicators of the customer, internal and learning and growth perspective. Balanced Scorecard, in fact, integrates internal and external perspective, short-time and long-time performance and economic, financial and operating

22 176 Text Mining and its Applications to Intelligence, CRM and Knowledge Management performance [71]. As a consequence, it seems suitable to allow management to evaluate an investment that has implications widespread in the organization. The introduction of the text mining solution according to the Balanced Scorecard view has an impact, first of all, on the financial perspective that, in this case, could be measured, as we have indicated above, through financial indicators like ROI or NPV (together with other measures like payback period). Therefore, what we called the hard ROI at the beginning of this chapter, represents a synthetic indicator of the Balanced Scorecard financial perspective, while in the other three dimensions we can find the drivers of what has been called the soft ROI. In particular, soft ROI, e.g. the intangible effects, is widespread in the other perspectives with a weight that varies in dependence on: the context of text mining application, the industry, the competitive arena, and so on. The customer dimension plays a key role in this kind of project (especially in the example above where we have supposed that the software is competitive intelligence targeted), so it would be necessary to define strategic performance indicators able to link the diffusion of knowledge to the customer objectives expressed in quantitative and qualitative measures. Internal process dimension should point out the cause-and-effect relationship that determines cost savings thanks to the introduction of text mining solution. In alternatives or in addition it could point out the process improvement in terms of defect rate or time rate or of other leading indicators that are linked in a cause-and effect relationship to the strategic objectives made explicit through the strategy map. Learning and growth perspective, finally, is the dimension where the knowledge management solution has a great impact. Here we find indicators relative to the intangible component in terms of future potential linked to knowledge diffusion and to the intellectual capital component: human, structural and customer. In this sense the image of the Skandia Navigator is significant where there is a link between time and the different perspectives. This dimension represents the future or, in other words, the potential resources to compete. These kinds of indicators need a mix of quantitative and qualitative measures, due to the intangible nature of learning and growth aspects. The Balanced Scorecard model, that we have presented as able to capture the hard and soft effects of this kind of solution, is in, our opinion, so flexible that it can be adapted to a lot of contexts with different culture backgrounds in accordance with the vision. Consequently, as it is easily understandable, it is impossible to define valid indicators for any situation because of the different model application and the variety of industry that can be interested in this kind of knowledge management project. In our opinion, thus, the presence of a Balanced Scorecard, as a tool of strategic control, could help to integrate the business performance with the

23 Text Mining and its Applications to Intelligence, CRM and Knowledge Management 177 intellectual capital one, in general, and in particular, with reference to text mining solution, that represents a tool to make knowledge management culture pervasive. In order to integrate the knowledge management (or intellectual capital) aspects, in the Balanced Scorecard perspectives, it may be necessary to point out further dimensions that could play a key role in determining the corporate performance. In that case it is fundamental to map the relationship between leading and lag indicators, even if these links are not so strong and in certain circumstances not well defined [72]. Last but not least, knowledge management aspects should be observed not only through a quantitative lens, but also through a qualitative lens with a consequent increase of management s attention to qualitative indicators. Although the presence or the construction of a Balanced Scorecard can permit evaluating also intangible components, we are conscious that this model is not always widespread in medium dimension realities. In this case we think that the possibility to capture the soft aspects linked to the introduction of text mining solution is not as strong as with the Kaplan and Norton s Balanced Scorecard. However, in order to have a wider set of elements to evaluate the project, the quantitative indicators (ROI, NPV, payback period) should be integrated with others that offer a reading of the project through qualitative lenses. When the strategic measures have been chosen, it could be useful a what if analysis to evaluate the consequences of different hypotheses. In this situation the evaluation is narrower than the Balanced Scorecard one and the focus is on a set of significant indicators chosen to reach strategic objectives. What lacks is the strategy map with the cause-and-effect relationship between leading and lag indicators. Therefore, the narrower evaluation, based on a number of qualitative measures simply added to quantitative ones, could be affected by a sort of short-sighted analysis, with the consequence of not capturing completely the intangible benefits coming from the knowledge management solution. For these reasons if management decides in favour of this second narrower focus path, it must be aware of this kind of limits and it should look for a tradeoff between control system structure and results. 5 Conclusions If we point out the differences and the common elements existing in the three models mentioned above, we can see that the two models - Intangible Asset Monitor and Navigator - have both a patrimonial approach coming from the consideration that intellectual capital is the difference between market value and net book value or financial capital, respectively. In addition, both models have a double target: internal for the management decision making process and external for investor disclosure.

24 178 Text Mining and its Applications to Intelligence, CRM and Knowledge Management However, the indicators of the two authors - Sveiby and Edvinsson - measure each category of the intellectual capital, but not the relations between them, consequently they are mainly related with stock, even if the two models are Stock-Flow theories [73]. If we consider Balanced Scorecard, Intangible Asset Monitor and the Navigator concepts (table 1), they categorize the intangible areas into three perspectives: Table 1: Balanced Scorecard, Intangible Asset Monitor and Navigator concepts. Sveiby Internal Structure External Structure Competence of Personnel Kaplan and Norton Internal Processes Perspective Customers Perspective Learning & Growth Perspective Edvinsson Organizational Capital Customer Capital Human Capital The Balanced Scorecard, the Intangible Asset Monitor and the Navigator have a fourth category: financial perspective (BSC), tangible assets (IAM), and financial capital (Navigator). Both Balanced Scorecard and Intangible Asset Monitor theories have the strategy as the driver for the metrics design [74]. However, the Intangible Asset Monitor has the goal of finding metrics measuring the change in the three intangible assets (external structure, internal structure, competence), such as growth, renewal, efficiency, stability and risk. Balanced Scorecard simply adds to the traditional perspective three nonfinancial ones that could be even many more: it s a flexible model adaptable to the exigencies of the management. Another difference is that the IAM s External Structure contains customers, suppliers and other external stakeholders and one selects the ones that are relevant. In most cases this will be Customers, which is why BSC advocates a singular Customer Perspective [75]. Edvinsson, instead, points out the focus on the customer capital, like the Balanced Scorecard. Finally, Kaplan and Norton s BSC regards the firm as a direct consequence of strategy and it does not take into consideration the elements that constitute it. On the other hand, the IAM is based on the concept of knowledge perspective, consequently, according to Sveiby, it becomes a more demanding option for a management team; to get the best value, one should start by re-designing the