Capacity and value based pricing model for professional services

Wardell, Clarence; Wynter, Laura; Helander, Mary E.

doi:10.1057/rpm.2008.18

Cited by 6 publications

(5 citation statements)

References 5 publications

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“…Many studies have used choice modeling in estimating price effects since the late twentieth century. Examples include a study on the apartment rental market (Elrod et al 1992), water resources (D. Hensher et al 2005), camera and automotive (Sonnier et al 2007), tourism (Masiero et al 2015), airline industry (Newman et al 2014;Ratliff et al 2008;Talluri and Van Ryzin 2004;Wardell et al 2008), nursing homes (Milte et al 2018), healthcare (Regier et al 2009), and transportation (Li et al 2010).…”

Section: Choice Modeling In Support Of Pricing Decisionsmentioning

confidence: 99%

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Optimizing B2B product offers with machine learning, mixed logit, and nonlinear programming

2021

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In B2B markets, value-based pricing and selling has become an important alternative to discounting. This study outlines a modeling method that uses customer data (product offers made to each current or potential customer, features, discounts, and customer purchase decisions) to estimate a mixed logit choice model. The model is estimated via hierarchical Bayes and machine learning, delivering customer-level parameter estimates. Customer-level estimates are input into a nonlinear programming next-offer maximization problem to select optimal features and discount level for customer segments, where segments are based on loyalty and discount elasticity. The mixed logit model is integrated with economic theory (the random utility model), and it predicts both customer perceived value for and response to alternative future sales offers. The methodology can be implemented to support value-based pricing and selling efforts.Contributions to the literature include: (a) the use of customer-level parameter estimates from a mixed logit model, delivered via a hierarchical Bayes estimation procedure, to support valuebased pricing decisions; (b) validation that mixed logit customer-level modeling can deliver strong predictive accuracy, not as high as random forest but comparing favorably; and (c) a nonlinear programming problem that uses customer-level mixed logit estimates to select optimal features and discounts.

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Section: Choice Modeling In Support Of Pricing Decisionsmentioning

confidence: 99%

“…The importance of measuring in B2B markets rests upon the literature stream on value-based pricing (Hinterhuber 2004(Hinterhuber , 2008Wardell et al 2008;Wu et al 2019), value-based selling (Töytäri and Rajala 2015), and customer value quantification (Hinterhuber 2017).…”

Section: Value-based Pricing and Value-based Selling In B2b Marketsmentioning

confidence: 99%

Optimizing B2B product offers with machine learning, mixed logit, and nonlinear programming

2021

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“…There are also complicated mathematic models designed mainly for academic purposes (e.g. Wardell et al, 2008). The most common approach in customer-oriented measurement is the customer satisfaction survey utilized in practically every organization.…”

Section: Performance Measurement In Knowledge-intensive Organizationsmentioning

confidence: 99%

Overcoming the specific performance measurement challenges of knowledge‐intensive organizations

Jääskeläinen

Laihonen²

2013

International Journal of Productivity and Performance Management

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Purpose -The purpose of this paper is to identify practical ways to overcome the specific performance measurement challenges of knowledge-intensive organizations. By reviewing performance measurement, service management and human resources management literature the paper recognizes two aspects that are crucial for the success of knowledge-intensive organizations: the performance and well-being of individual knowledge workers and the ability to provide value for the customer. The authors evaluate three measurement solutions for measuring these aspects, in three empirical settings. Design/methodology/approach -Qualitative case approach, carried out as an action research, is used with empirical data obtained through interviews, workshops and analysis of documentation related to measurement systems. The empirical study consists of three case studies where two main phases were carried out: investigating the status of existing measurement practices; and development and evaluation of new measurement approaches and tools. Findings -The results of the evaluation of proposed measurement approaches provide understanding of their potential in different workplaces. Eventually, this potentially supports the managers of knowledge-intensive organizations in developing not only the measurement practices but also the overall performance of their organizations. Originality/value -As the key academic contribution, the study provides new understanding on the potential of selected measurement approaches in overcoming the specific performance measurement challenges in knowledge-intensive organizations. The results take into account the perspectives of an individual knowledge worker, a customer and an organization as a whole. In many previous studies, the main focus has been solely on the organizational perspective.

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“…Other papers in this group of 18 expand the use of RM beyond the traditional airline, hotel and rental car service providers to: K variable capacity outsourcing contracts (Kenyon, 2005); K differential pricing in the allocation of inventory and production capacity by a home construction products manufacturer (Kumar and Frederick, 2007); K capacity and pricing in professional service firms (Wardell et al, 2008); K manufacturing process industries (Kolisch and Zatta, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying revenue opportunities via capacity analysis

Huefner

Largay

2013

J Revenue Pricing Manag

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Although capacity considerations are an integral part of revenue management (RM), there is relatively little literature on the subject. We discuss the dimensions of capacity and describe the capacity analysis model developed by Computer Aided Manufacturing -International (CAM-I). Capacity is not only physical, but includes personnel, processes and purchases (supply chain). We illustrate how examining why physical capacity is non-productive or idle can lead to revenue enhancement opportunities. We also illustrate how knowledge of capacity utilization can aid in revenue projection and in evaluating revenue forecasts. CAM-I capacity analysis can be a valuable RM tool.

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Capacity and value based pricing model for professional services

Cited by 6 publications

References 5 publications

Optimizing B2B product offers with machine learning, mixed logit, and nonlinear programming

Optimizing B2B product offers with machine learning, mixed logit, and nonlinear programming

Overcoming the specific performance measurement challenges of knowledge‐intensive organizations

Identifying revenue opportunities via capacity analysis

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