When managing their growing service portfolio, many manufacturers in B2B markets face two significant problems: They fail to communicate the value of their service offerings and they lack the capability to generate profits with value-added services. To tackle these two issues, we have built and evaluated a collaborative filtering recommender system which (a) makes individualized recommendations of potentially interesting value-added services when customers express interest in a particular physical product and also (b) leverages estimations of a customer’s willingness to pay to allow for a dynamic pricing of those services and the incorporation of profitability considerations into the recommendation process. The recommender system is based on an adapted conjoint analysis method combined with a stepwise componential segmentation algorithm to collect individualized preference and willingness-to-pay data. Compared to other state-of-the-art approaches, our system requires significantly less customer input before making a recommendation, does not suffer from the usual sparseness of data and cold-start problems of collaborative filtering systems, and, as is shown in an empirical evaluation with a sample of 428 customers in the machine tool market, does not diminish the predictive accuracy of the recommendations offered.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Addelman, S. (1962). Orthogonal main-effect plans for asymmetrical factorial experiments. Technometrics, 4(2), 21–46.
Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749.
Alderson, W. (1957). Marketing behavior and executive action: A functionalist approach to marketing theory. Homewood: Richard D. Irvin.
Ansari, A., Essegaier, R., & Kohli, R. (2000). Internet recommendation systems. Journal of Marketing Research, 37(3), 363–375.
Balabanovic, M., & Shoham, Y. (1997). Fab: content-based, collaborative recommendation. Communications of the Association for Computing Machinery, 40(3), 66–72.
Becker, J., Beverungen, D., Knackstedt, R., & Müller, O. (2009). Model-based decision support for the customer-specific configuration of value bundles. Enterprise Modeling and Information Systems Architectures, 4(1), 26–38.
Bergen, M., Ritson, M., Dutta, S., Levy, D., & Zbaracki, M. (2003). Shattering the myth of costless price changes. European Journal of Management, 21(6), 663–669.
Bichler, M., Kalagnanam, J., Katircioglu, K., & King, A. J. (2002). Applications of flexible pricing in business-to-business electronic commerce. IBM Systems Journal, 41(2), 287–302.
Bouwman, H., Haaker, T., & de Vos, H. (2007). Mobile service bundles: the example of navigation services. Electronic Markets, 17(1), 20–28.
Butler, J., Morrice, D. J., & Mullarkey, P. W. (2001). A multiple attribute utility theory approach to ranking and selection. Management Science, 47(6), 800–816.
Chen, L.-S., Hsu, F. H., Chen, M. C., & Hsu, Y. C. (2008). Developing recommender systems with the consideration of product profitability for sellers. Information Sciences, 178(4), 1032–1048.
Choi, S. H., Kang, S., & Jeon, Y. J. (2006). Personalized recommendation system based on product specification values. Expert Systems with Applications, 31(3), 607–616.
De Bruyn, A., Liechty, J. C., Huizingh, E. K. R. E., & Lilien, G. L. (2008). Offering online recommendations with minimum customer input through conjoint-based decision aids. Marketing Science, 27(3), 443–460.
Desiraju, R., & Shugan, S. M. (1999). Strategic service pricing and yield management. Journal of Marketing, 63(1), 44–56.
Elmaghraby, W., & Keskinocak, P. (2003). Dynamic pricing in the presence of inventory considerations: research overview, current practices and future directions. Management Science, 49(10), 1287–1309.
Frei, F. X. (2006). Breaking the trade-off between efficiency and service. Harvard Business Review, 84(11), 93–101.
Fricker, S., Galesic, M., Tourangeau, R., & Yan, T. (2005). An experimental comparison of web and telephone surveys. Public Opinion Quarterly, 69(3), 370–392.
Gebauer, H., Fleisch, E., & Friedli, T. (2005). Overcoming the service paradox in manufacturing companies. European Management Journal, 22(1), 14–26.
Gorman, M. F., Salisbury, W. D., & Brannon, I. (2009). Who wins when price information is more ubiquitous? An experiment to assess how intermediaries influence price. Electronic Markets, 19(2–3), 151–162.
Green, P. E. (1977). A new approach to market segmentation. Business Horizons, 20(1), 61–73.
Green, P. E., & DeSarbo, W. S. (1979). Componential segmentation in the analysis of consumer trade-offs. Journal of Marketing, 43(4), 83–91.
Green, P. E., & Krieger, A. M. (1988). Choice rules and sensitivity analysis in conjoint simulators. Journal of the Academy of Marketing Science, 16(1), 114–127.
Green, P. E., & Srinivasan, V. (1990). Conjoint analysis in marketing: new developments with implications for research and practice. Journal of Marketing, 54(4), 3–19.
Green, P. E., Krieger, A. M., & Agarwal, M. K. (1993). A cross validation test of four models for quantifying multiattribute preferences. Marketing Letters, 4(4), 369–380.
Grover, R., & Vriens, M. (2006). The handbook of marketing research: Uses, misuses, and future advances. Thousand Oaks: Sage.
Gustafsson, A., Herrmann, A., & Huber, F. (2007). Conjoint measurement: Methods and applications (4th ed.). Berlin: Springer.
Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis—a global perspective. Upper Saddle River: Pearson.
Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1), 5–53.
Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. Management Information Systems Quarterly, 28(1), 75–105.
Homburg, C., & Garbe, B. (1999). Towards an improved understanding of industrial services: quality dimensions and their impact on buyer-seller relationships. Journal of Business to Business Marketing, 6(2), 39–71.
Homburg, C., Günther, C., & Faßnacht, M. (2004). Wenn Industrieunternehmen zu Dienstleistern werden. Lernen von den Besten. In C. Homburg (Ed.), Perspektiven der marktorientierten Unternehmensführung. Wiesbaden: DUV.
Howells, J. (2003). Innovation, consumption and knowledge: Services and encapsulation. CRIC discussion paper, No.62, University of Manchester.
Johnston, W. J., & Lewin, J. E. (1996). Organizational buying behavior: toward an integrative framework. Journal of Business Research, 35(1), 1–15.
Kauffman, R. J., & Wang, B. (2001). New buyers’ arrival under dynamic pricing market microstructure: The case of group-buying discounts on the internet. In Proceedings of the 35th Hawaii’ International Conference on Systems Sciences, Maui.
Lee, T., Chun, J., Shim, J., & Lee, S. (2007). An ontology-based product recommender system for B2B marketplaces. International Journal of E-Commerce, 11(2), 125–155.
Manouselis, N., & Costopoulou, C. (2007). Analysis and classification of multi-criteria recommender systems. World Wide Web, 10(4), 415–441.
March, S. T., & Smith, G. F. (1995). Design and natural science research on information technology. Decision Support Systems, 15(4), 251–266.
Martin, F. (2009). Top 10 lessons learned developing, deploying, and operating real-world recommender systems, industry keynote 3rd ACM Conference on Recommender System 2009, New York, http://recsys.acm.org/invited_talk_strands_martin.pdf, Accessed: 2009-11-30.
Moore, W. L. (1980). Levels of aggregation in conjoint analysis: an empirical comparison. Journal of Marketing Research, 17(4), 516–523.
Nagle, T. T., & Holden, R. K. (2002). The strategy and tactics of pricing: A guide to profitable decision making. Upper Saddle River: Prentice Hall.
OECD (2005). Enhancing the performance of the service sector, online resource: http://www.value-chains.org/dyn/bds/docs/497/WolflOECDEnhancingPerformanceServicesSector.pdf, Accessed: 2008-02-20.
Oliva, R., & Kallenberg, R. (2003). Managing the transition from products to services. International Journal of Service Industry Management, 14(2), 160–172.
Pessemier, E., Burger, P., Teach, R., & Tigert, D. (1971). Using laboratory brand preference scales to predict consumer brand purchases. Management Science, 17(6), 371–385.
Rai, A., & Sambamurthy, V. (2006). Editorial notes: the growth of interest in services management: opportunities for information systems scholars. Information Systems Research, 17(4), 327–331.
Rossignoli, C., Carugati, A., & Mola, L. (2009). The strategic mediator: a paradoxical role for a collaborative e-marketplace. Electronic Markets, 19(1), 55–66.
Roster, C. A., Rogers, R. D., Albaum, G., & Klein, D. (2004). A comparison of response characteristics from web and telephone surveys. International Journal of Market Research, 46(3), 359–373.
Schafer, J. B., Konstan, J. A., & Riedl, J. T. (2001). E-commerce recommendation applications. Data Mining and Knowledge Discovery, 5(1–2), 115–153.
Schneider, T. (2005). Preference-base-recommender-systeme. Individuelle neuronale Präferenzmodellierung am Beispiel von Investmentfonds. Wiesbaden: DUV.
Scholz, M. (2008). From consumer preferences towards buying decisions. Conjoint Analysis as a preference measuring method in product Recommender Systems. Proceedings of the 21st Bled Conference, Bled.
Schuman, H., & Presser, S. (1996). Questions and answers in attitude surveys: Experiments on question form, wording, and content. New York: Academic.
Schwind, M. (2007). Dynamic pricing and automated resource allocation for complex information services: Reinforcement learning and combinatorial auctions. In: Lecture notes in economics and mathematical systems, No. 589. Springer, Berlin.
Shardanand, U., & Maes, P. (1995). Social information filtering: Algorithms for automating ‘Word of Mouth’, Proceedings of the Conference on Human Factors in Computing Systems 1995, Denver.
Stremersch, S., Wuyts, S., & Frambach, R. T. (2001). The purchasing of full-service contracts; an exploratory study within the industrial maintenance market. Industrial Marketing Management, 30(1), 1–12.
Thaler, R., Kahneman, D., & Knetsch, J. (1986). Fairness as a constraint on profit-seeking: entitlements in the market. American Economic Review, 76(4), 728–741.
Varian, H. R. (1996). Differential pricing and efficiency, First Monday, Vol. 1, No. 2, URL: http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/viewArticle/473/394, Accessed: 2009-11-30.
Voeth, M., Herbst, U., & Tobies, I. (2007). Customer insights on industrial markets—a new method to measure complex preferences, Proceedings of the IMP Group Conference 2007, Manchester.
Wang, S., Archer, N., & Zheng, W. (2006). An exploratory study of electronic marketplace adoption: a multiple perspective view. Electronic Markets, 16(4), 337–348.
This publication was written in the context of the research project ServPay. ServPay was funded by the German Federal Ministry of Education and Research (BMBF), funding number 02PG1010, and managed by the Project Management Agency Karlsruhe (PTKA). The authors are responsible for the content of this publication.
Responsible editor: Martin Spann
About this article
Cite this article
Backhaus, K., Becker, J., Beverungen, D. et al. Enabling individualized recommendations and dynamic pricing of value-added services through willingness-to-pay data. Electron Markets 20, 131–146 (2010). https://doi.org/10.1007/s12525-010-0032-0
- Collaborative filtering
- Dynamic pricing
- Service science
- Design science