A DATA MINING APPROACH TO BUSINESS MODELLING
Nicola Castellano – Roberto Del Gobbo University of Macerata – Department of Economics and Finance
[email protected] –
[email protected]
Agenda 1) 2) 3) 4) 5)
Business models: common concepts in literature Data mining applications: Structured Neural Networks (SNN) Research questions and case study Discussion of main findings Conclusions
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Business models: common concepts in literature Business models adopts an holistic and systemic perspective, based on activities, intended to describe dynamics, components and linkages through which value is created and captured (Zott et al. 2011). Business models, intended as scale models (Baden-Fuller and Morgan, 2010) describe a business in a simplified version and help researchers to describe “kinds of things” (or behaviors); to classify; and to create taxonomies and typologies, empirically and theoretically grounded (Baden-Fuller and Haefliger, 2013; Ostenwalder and Pigneur, 2010). Business Plan, describes how the company intend to meet specific customer needs, how the customers will be disposed to reward the value received, how the company is expecting to generate an adequate level of profit (Teece, 2010).
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Business models as cognitive devices The design of a business model requires creativity and a good level of knowledge about customers, suppliers and competitors. The business models may be considered as “cognitive” devices (Baden-Fuller and Haefliger, 2013). If the customer needs are clearly exploited, the managers will have the possibility to formulate a suitable value proposition (Euchner and Ganguly, 2014). Lot of knowledge about customers is implicit, then a discovery approach based on experimentation and learning may be needed (Teece 2010).
Information Technology, (Data Mining particularly), may support the generation of knowledge needed during the design of a business plan (Heinrichs and Lim, 2003): the managers’ strategic capability, intended as the speed needed to react to environmental changes and select appropriate strategic and tactical business models is improved; a fact-based consensus is developed: decisions are driven without exclusively relying on personal perceptions and past-experience.
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Data mining and Structured Neural Networks Data mining applications can be (Berry and Lynoff, 1997): Explorative, bottom-up approach, used to generate unknown information by sieving the available data, without any a priori assumption. Confirmative, top-down approach for hypothesis testing. The Structured Neural Networks (SNN) can be considered a valid solution for predictive modeling when the contextual and theoretical knowledge is available during the design of the network (Lee et al. 2005). SNN require the preliminary exploitation and sharing of personal knowledge, converted into an explicit cause and effect predictive model (particularly useful in support of business models design). SNN allow to test the robustness of the predictive model and provide insights about the relevance of the expected relations between the variables and the magnitude of the impacts produced.
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Research questions Our aim is to develop the previous study of (Heinrichs and Lim, 2003). We intend to analyze whether the adoption of a SNN in a real context may be influenced by organizational heterogeneity. RQ1: May the adoption of data mining tools (SNN) provide results perceived as useful by managers even in a context characterized by organizational heterogeneity? RQ2: Are there any organizational factors enabling or hindering the perceived usefulness of results?
Organizational heterogeneity refers to the presence of differences concerning the actors involved in the business process design, that can enable or hinder the effective adoption of the information tool. These differences may relate to: the level of authority; skills; level of experience; confidence with “numbers”; educational background. 6
Case study Explanatory case study, employed to explain how a set of qualitative variables impact on a complex phenomenon. The case study is well suited for many kinds of information systems and software engineering research, as the objects of study are contemporary phenomena which are hard to study in isolation (Runeson and Höst, 2008). Data are collected through direct observation, adopting an action-research approach, where the researcher is directly involved in the processes under investigation and covered the role of project coordinator. The introduction of the SNN has been divided in three steps: 1) business model design, through knowledge exploitation and sharing of personal beliefs; 2) data collection about customer perceptions through survey; 3) adoption of the SNN to test the robustness of the business model.
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The Lube Environment Lube is actually ranked as one of the top Italian kitchen producers. Only in Italy, the company gets in touch with its final users by mean of a wide network composed by 1.500 private resellers (multi-branded licensees) The resellers influence the final users’ purchasing decision, since they have room to promote the brands of companies they feel more satisfied with. The resellers satisfaction is connected to the products but also to the operating processes that the resellers need to manage in strict connection with Lube. In order to design an effective business model, the managers of Lube need to consider a double-layer customer perspective, centered either on the final users and the direct customers, the resellers.
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Business Model Design The participants to a focus group have been asked to express their opinions about the significance of the variables included in the ECSI framework and their suitability in representing a simplified model of the relations between Lube and its customers.
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Structured Neural Network A survey realized by sending questionnaires to a statistic-significant sample of customers. The respondents, were asked to evaluate every manifest variable by mean of a 10 levels qualitative scale.
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Managerial impications All the managers agreed that Image was the main driver of customer satisfaction since it showed the largest positive weight in connection to the Satisfaction output neuron. 0,35
Impact on the customer satisfaction
Image 0,30
0,25
Perceived Value
0,20
Customer expectation
0,15
0,10
0,05
0,00 7,00
Perceived Quality
7,20
7,40
7,60
7,80
8,00
8,20
8,40
Average score
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Discussion of findings The results of the SNN have been discussed during a meeting participated by all the managers involved in the focus group and by the CEO. The managers hold extremely different profiles for what concern past working experience and educational background. Focus Group Partecipant
Level of Instruction
Years of experience in the company
Previous experiences in other companies
Information Systems skills
CEO
no
High School Certificate
> 40 years
no
Low
Sales Dir.
yes
> 25 years
yes
Low
Marketing Dir.
yes
> 20 years
yes
Low
Production Dir.
yes
> 30 years
yes
Low
Finance Dir.
yes
> 20 years
no
Low
R&D Dir.
yes
> 30 years
no
Low
Project Coord.
yes
< 15 years
no
High
High School Certificate Degree in Economics High School Certificate Degree in Economics High School Certificate PhD in Management
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Discussion of findings All the managers, despite their differences, considered reliable the results obtained and did not show any skepticism, neither when the results unexpectedly did not confirm their expectations and prior beliefs. RQ1. The results are in line with the study of Heinrichs and Lim, 2003: data-mining may improve the managers’ strategic capability; data-mining may help in developing a fact-based consensus, even when the operating managers do not hold similar managerial and technical competencies, past experiences and educational background (slight empirical evidence). RQ2. The CEO played a key role in determining the general acceptance of the results by all other managers and the tool effectiveness in supporting decision making. His mind-set positively influenced all the participants that aligned their mental attitudes with that of the CEO. RQ2. The company attitude to learn, either if originally shared between managers or produced by a top-down persuasion, is necessary to determine the effectiveness of the information tool.
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Conclusions The cognitive map developed by managers , the architecture of the SNN, can be considered an explicit vehicle of information useful to transfer, share and discuss company knowledge throughout the organization and foster a general consensus about company policies and strategies. The single case study does not allow to generalize the results obtained. Supplementary case studies in different competitive environment needed. Knowledge generation about final users’ needs not included in the case study (difficulties in the collection of suitable data). Further research might investigate the impact of market and competitive variables on the effective employment of data-mining on business models design.
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References Baden-Fuller, C., Haefliger, S., 2013, Business Models and Technological Innovation, Long Range Planning 46, 419–426. Baden-Fuller, C., Morgan M.S., 2010, Business Models as Models, Long Range Planning, 43, 156-171. Berry M., Lynoff G., 1997, Data Mining techniques for marketing, sales, and customer support, Wiley, New York. Euchner J., Ganguly A., 2014, Business Model Innovation in Practice, Research-Technology Management, November—December 2014, 33-39. Heinrichs J.H., Lim J.S., 2003, Integrating web-based data mining tools with business models for knowledge management, Decision Support Systems, 35, 103– 112. Lee, C., Rey, T., Mentele, J., Garver, M. (2005). Structured neural network techniques for modeling loyalty and profitability. SAS SUGI, 30. Osterwalder, A., Pigneur, Y. 2010. Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers. Hoboken, NJ: John Wiley & Sons. Runeson, P., Höst, M., 2008, Guidelines for conducting and reporting case study research in software engineering, Empirical Software Engineering. An International Journal, DOI: 10.1007/s10664-0089102-8. Teece, D.J., 2010, Business Models, Business Strategy and Innovation, Long Range Planning, 43, 172-194. Zott C., Amit R., Massa L., 2011, The Business Model: Recent Developments and Future Research. Journal of Management, July, 37, 1019-1042. 15
