Developing Data Driven Business Models for Interactive Media Companies

Amsterdam University of Applied Sciences

Developing Data Driven Business Models for Interactive Media Companies

Haaker, Timber; Groot, Wouter; Hekman, Erik

Publication date 2019

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The ISPIM Innovation Conference

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Citation for published version (APA):

Haaker, T., Groot, W., & Hekman, E. (2019). Developing Data Driven Business Models for Interactive Media Companies. In The ISPIM Innovation Conference : Celebrating Innovation:

500 Years Since daVinci, Florence, Italy on 16-19 June 2019 (pp. 1-8).

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Download date:26 Nov 2021

This paper was presented at The ISPIM Innovation Conference – Celebrating Innovation: 500 Years Since daVinci, Florence, Italy on 16-19 June 2019. The publication is available to ISPIM

members at www.ispim.org.

Developing Data Driven Business Models for Interactive Media Companies

Timber Haaker*

Saxion University of Applied Sciences, 28 M.H. Tromplaan, 7513 AB, Enschede, The Netherlands.

E-mail: t.i.haaker@saxion.nl

Wouter Groot

Amsterdam University of Applied Sciences, Rhijnspoorplein 1, 1091 GC Amsterdam, The Netherlands.

E-mail: w.j.c.groot@hva.nl

Erik Hekman

HU University of Applied Sciences Utrecht, Heidelberglaan 15, 3584 LB, Utrecht, The Netherlands.

E -mail: erik.hekman@hu.nl

* Corresponding author

Abstract: The growing availability of data offers plenty of opportunities for data driven innovation of business models for SMEs like interactive media companies. However, SMEs lack the knowledge and processes to translate data into attractive propositions and design viable data-driven business models. In this paper we develop and evaluate a practical method for designing data driven business models (DDBM) in the context of interactive media companies. The development follows a design science research approach. The main result is a step-by-step approach for designing DDBM, supported by pattern cards and game boards. Steps consider required data sources and data activities, actors and value network, revenue model and implementation aspects. Preliminary evaluation shows that the method works as a discussion tool to uncover assumptions and make assessments to create a substantiated data driven business model.

Keywords: business model; data driven; media company; innovation;

workshop format; patterns;

1 Introduction

The growing availability of data offers plenty of opportunities for data driven innovation

of business models. This certainly applies to interactive media companies. Interactive

media companies are engaged in the development, provisioning and exploitation of

interactive media services and applications. Through the service interactions they can

collect large amounts of data which can be used to enhance applications or even define

This paper was presented at The ISPIM Innovation Conference – Celebrating Innovation: 500 Years Since daVinci, Florence, Italy on 16-19 June 2019. The publication is available to ISPIM

members at www.ispim.org.

2

new propositions and business models. According to Lippell (2016) media companies can publish content in more sophisticated ways. They can build a deeper and more engaging customer relationship based on a deeper understanding of their users. However, these opportunities still remain largely untapped as especially SMEs lack the knowledge and processes to translate data into attractive propositions and design viable data-driven business models (Mathis and Köbler, 2016). Also because of the complexities that come with the fragmentation of data within the industry ecosystem. In this paper we investigate how interactive media companies can structurally gain more insight and value from user data and how they can innovate their business models.

Research objective

The main goal of this research is to design and evaluate a practical method for developing DDBM in the context of interactive media companies. The method builds on the DDBM framework of Hartmann and the process model of Brownlow. Business model patterns and pre-defined solutions are integrated into the method. In the evaluation, the goal is to test the efficacy of the method. In particular, the efficacy of the use of patterns vis-à-vis a method without such patterns, and the role and added value of a facilitator in a facilitated use of the method, will be evaluated.

2 Theoretical Background

There exist several generic approaches for developing service propositions and designing BMs like the Business Model Canvas (Osterwalder et al., 2010) or STOF method (Bouwman et al., 2008), and there are conceptual models, development approaches and patterns concerning data driven business models specifically (Hartmann (2016), Brownlow (2015), Mathis and Köbler (2016), Schaefer et al., 2017). Hartmann developed a conceptual model, i.e. the Data-Driven Business Model Framework, with typical business model components like offering, customers and revenue model but adding Data Sources and Key (data) Activities as key components of a data driven business model (DDBM). For each concept Hartmann also proposes typical solutions or patterns (Remane, 2017). Brownlow (2015) builds on Hartmann’s conceptual model by adding a development approach based on asking key business model questions. Schaefer (2017) did an analysis of data-driven business models in industry 4.0 and considered common characteristics in a hybrid Business Model Canvas. Mathis and Köbler (2016) developed the Data Canvas and Data-Need Fit to systematically document available data and a process to match available data with user needs, respectively.

3 Research design

This research is part of a national project on DDBM and involves researchers and

students from academia and practitioners from interactive media companies. The project

investigates how interactive media companies can structurally gain more insight and

value from user data and how they can use it to innovate their business model. The

development of the DDBM design method is part of the project and follows a Design

Science Research (DSR) approach (Gregor and Hevner, 2013). In this research in

progress paper, we present the development and design of the DDBM method and some preliminary evaluation results. Table 1 summarises the steps in the DSR approach, adapted from Gregor and Hevner (2013), together with the objectives of this paper and the type of research methods used.

Table 1. DSR approach, objectives and research methods (adapted from Gregor and Hevner, 2013).

Steps Objectives this paper Research Methods

(1) Introduction Problem statement, research

objective Literature review,

practitioners’ interviews (2) Theoretical background Theories behind the problem Literature review (3) Research design Description of the DSR

approach Literature review

(4) Results Development and description

of the DDBM design method (the artifact)

Action research; iterative development with practitioners (5) Evaluation First evaluation results from

pilot with master students Experimental design;

reflection

(6) Discussion Interpretation of results Qualitative analysis of data (7) Conclusions Communicating main findings

so far and collect feedback on future steps

Interpretation, peer review

For the first activity (‘Introduction’) we held interviews with interactive media companies to better understand their needs and opportunities regarding developing data driven business models. For the second step (‘Theoretical background’) we reviewed literature on data driven business models, business model design methods and data driven innovation in the context of media companies. The third step (‘Research design’) about the DSR approach that was followed is described in this section.

For the fourth step (‘Results’) we first developed the conceptual basis for our DDBM design method by extending the framework of Hartmann (2016) with concepts from the process model of Brownlow (2015), the concept of value networks, and concepts from business model implementation (De Reuver et al., 2017). Based on this adapted framework, a first version of the method was developed. The method was practically elaborated into a workshop format with accompanying templates and supporting materials. Subsequently, the method was iteratively tested and redesigned in a series of workshops with practitioners from the interactive media companies involved in the project and with students. The formative evaluations have led to a first full version of the DDBM design method, a step-by-step approach for developing a DDBM. Templates and materials were designed together with an external design agency.

For the fifth step (‘Evaluation’), we evaluate the DDBM design method regarding

efficacy and efficiency in developing data driven business models. In particular, we will

evaluate the added value of applying patterns and pre-defined solutions within business

model design methods. This paper presents a first evaluation with students from a master

course on Data Driven Design. In the sixth step (‘Discussion’) we discuss preliminary

results and in the seventh step (‘Conclusion’) the main contribution and practical

implications.

This paper was presented at The ISPIM Innovation Conference – Celebrating Innovation: 500 Years Since daVinci, Florence, Italy on 16-19 June 2019. The publication is available to ISPIM

members at www.ispim.org.

4

4 Results

We briefly describe here our main results, i.e. the DDBM design method as our main artifact and some preliminary evaluation results.

Artefact description

The main result is a step-by-step approach for developing DDBM. The step-by-step plan starts with determining the intended business goals and intended data innovation.

Consequently, in steps 2 and step 3, the required data sources and data activities are determined, respectively. In step 4 we develop the value network, i.e. the set of business roles and actors fulfilling these roles and value flows between them, necessary to deliver the business model. Next in step 5, the business model is complemented with the revenue model(s) and the financial flows between the actors. Finally, in step 6, the actions to be taken to implement the (new) business model are prioritized and positioned on a roadmap with desired actions.

Table 2 summarizes the approach for each step. Indicating goal, intended result, and available support materials.

Table 2. Overview of DDBM design method with steps, goals, results and support materials

Step and goal Intended results Support materials

(1) Determine Goals Company goal, data

innovation goal, target group, proposition

Theory card, inspiration cards, goal cards, Goal-Data- Innovation game board (2) Select Data Sources Overview of required data and

data sources Theory card, data source cards (3) Define Data Activities Overview of required data

activities Theory card, data

activity cards, data activity game board (4) Sketch Business Model Business model as a value

network with actors and value flows

Theory card, actor cards, business model game board

(5) Choose Revenue Model Business model as a value network with revenue model and financial flows

Theory card, revenue model cards (6) Develop Roadmap Overview of actions in

roadmap Priority cards,

roadmap template

Each step is supported by a card deck with pre-defined solutions or patterns for

inspiration, see Figure 1 . In step 1, the method provides inspiration cards highlighting

companies with successful data driven business models. At step 2, the method provides

cards with typical data sources (customer data, partner data, open data, etc.), and for step

3, typical data activities (data acquisition, data aggregation, etc.). For each step, users

design solutions by selecting appropriate pre-defined cards and/or formulate their

solutions on blank cards. Game boards are available to collect and annotate cards. For

example, in step 4, users create actor cards for each involved actor. Actor cards describe the key activity and key resource that the actor brings to the network. The game board allows users to arrange cards in a value network and to add the value exchanges and flows between the actors. The end result is a clear overview of a new DDBM. In the final step, users think of practical steps towards implementation of the DDBM and place them on the roadmap game board.

The six steps are embedded in a format for a facilitated workshop, which we consider the main use case for the method. However, the method with the materials can also be used by practitioners in a self-service setting.

Figure 1. Examples of predefined cards for data source and data activity, respectively.

5 Evaluation

The first "play test" for the DDBM workshop format was held with 20 students from Utrecht University of Applied Sciences that follow a master Data Driven Design. These students already have knowledge of data science and are aware of the possibilities and applications of data and algorithms. The students often had a technical bachelor background and limited knowledge of business models. The workshop was led by two facilitators, both involved in the development of the method.

The starting point of the workshop were two actual cases. One about Johan Cruyff Arena in Amsterdam and the other about NPO (Dutch Public Broadcast Organisation).

Students were divided into four groups of five students each and each group developed a

business model for the given case. Students indicated that there are many steps and

workshop materials. It takes time to get acquainted with the material as cards contain

This paper was presented at The ISPIM Innovation Conference – Celebrating Innovation: 500 Years Since daVinci, Florence, Italy on 16-19 June 2019. The publication is available to ISPIM

members at www.ispim.org.

6

quite a lot of text. Some students indicate that the predefined cards somewhat steer thoughts and limit creativity but others find them useful and supportive as they provide ideas to work from. The entire methodology works as a discussion tool to uncover assumptions, make assessments, understand why you make certain choices, and thus create a substantiated business model story. Students are mostly positive, "It helps you focus. The cards really kick you around", as one student phrased it.

6 Discussion and conclusion

Contribution

First, the conceptual model underlying the DDBM design method extends current conceptual models. In particular, the value network view adds a multi-actor perspective to DDBM, and provides insights in the complex value flows within data-ecosystems.

Next, as part of the design science approach, design knowledge regarding data-driven business models will be generated. In particular knowledge about the added value of applying patterns and pre-defined solutions within business model design methods.

Practical implications

The research provides a practical and tested approach for developing DDBM for interactive media companies and the broader community of data-rich service providers.

With this method, practitioners will be able to develop and test new DDBM assumptions much faster.

7 Acknowledgement

This research has been carried out within the project ‘Meer Profijt uit Data’, which was (partly) financed by the Taskforce for Applied Research, part of the Netherlands Organisation for Scientific Research (NWO).

8 References

Bouwman, H. De Vos, H. and Haaker, T. eds. Mobile service innovation and business models. Berlin Heidelberg: Springer-Verlag, 2008.

Brownlow, J., Zaki, M. Neely, A. and Urmetzer, F. “Data and Analytics - Data-Driven Business Models: A Blueprint for Innovation.” Working paper Cambridge Service Alliance, University of Cambridge.

De Reuver, M., Bouwman, H., and Haaker, T. “Business model roadmapping: a practical approach to come from an existing to a desired business model.” International Journal of Innovation Management 17/01, 2013.

Gregor, S. and Hevner, A. “Positioning Design Science Research for Maximum Impact.”

Management Information Systems Quarterly, 37, 337–355.

Hartmann, P., Zaki, M. E., Feldmann, N., & Neely, A. D. “Capturing value from big data

– a taxonomy of data-driven business models used by start-up firms.” International

Journal of Operations and Production Management, 36 (10), 2016, 1382-1406.

https://doi.org/10.1108/IJOPM-02-2014-0098.

Lippell, H. “Big Data in the Media and Entertainment Sectors.” In: Cavanillas, J.M., Curry, E. & Wahlster, W. (eds.) New Horizons for a Data-Driven Economy: A Roadmap for Usage and Exploitation of Big Data in Europe, 2016, Springer International Publishing.

Mathis, K., and Köbler, F. “Data-Need Fit – Towards data-driven business model innovation.” Proceedings of ServDes 2016, Copenhagen, Denmark.

Osterwalder, A., Pigneur, Y., Clark, T., and Smith, A. “Business model generation: A handbook for visionaries, game changers, and challengers.” Hoboken:John Wiley, 2010.

Remane, G., Hanelt, A., Tesch, J. and Kolbe, L. “The business model pattern database—

a tool for systematic business model innovation.” International Journal of Innovation Management, 21(1), 2017.

Schaefer, D., Walker, J., and Flynn, J. (2017). “A data-driven business model framework

for value capture in Industry 4.0.” Advances in Transdisciplinary Engineering, 6, 2017,

245-250.

This paper was presented at The ISPIM Innovation Conference – Celebrating Innovation: 500 Years Since daVinci, Florence, Italy on 16-19 June 2019. The publication is available to ISPIM

members at www.ispim.org.

8

Areas for feedback & development

1. How to best perform (summative) evaluation of our approach regarding efficacy of individual steps? Especially, how to set-up experiments, measurements and how to collect relevant data?

2. How to determine the efficacy of the use of pre-defined solutions and patterns vis-à-

vis a method without such patterns? In particular, what kind of experiments to

conduct and how to analyse them?