Pages: 8

This chapter reviews pertinent empirical studies, analyzes price-setting and price-getting practices in the industry, and outlines the capabilities required to master one important challenge: the move from subscription-based pricing to consumption-based pricing models. It is a unique roadmap for software product and pricing developers to conduct a successful transition from perpetual licenses to subscription-based pricing. For this chapter, Matt Johnston and Maciej Wilczy ski have joined forces to present two pricing research techniques that can yield impressive insights to get digital pricing right. This chapter helps with understanding all the dimensions of the pricing problem and opportunity in digital marketplace-platforms. Finally, we offer a full section on the emergence of artificial intelligence in pricing. This chapter focuses on how AI can assist B2B sellers in identifying and interpreting unmet customer needs and creating value-based offers that will delight customers and exceed their expectations. The metaverse connects several of the topics we touch on here: Web 3.0, marketplaces, and AI-based pricing.

Pages: 52

No chapter abstract available.

Pages: 8

This chapter highlights key aspects of value-based pricing for digital products and services. Technology, especially big data, analytics, and artificial intelligence, can help to automate some aspects related to value quantification for digital products and services.

Pages: 8

This chapter makes the case for increased price transparency for digital products and services.

Pages: 12

Dynamic and personalized pricing emerged as novel pricing approaches in the digital age; shifting to these approaches can increase company profits by 3% to 25%. Dynamic pricing allows firms to smoothen demand and supply and to remain competitive facing price pressure. Personalized pricing enables companies to tap into consumers’ different willingness to pay. However, such profit enhancements only set in if managers make good choices when implementing their company's pricing approach. Managers need to overcome potential adverse consumer reactions and comply with privacy regulations. They need to create an appropriate IT infrastructure, synchronize prices in their on- and offline channels, harmonize prices in their product portfolio, and nurture a mindset that is open to automating prices. This chapter introduces and discusses a systematic dynamic pricing process that provides practical guidance for decision-makers. This discussion enables managers to decide whether and how to implement a dynamic pricing approach.

Pages: 10

This chapter discusses the challenges of customer value management for SaaS products. The authors highlight three aspects: first, sales teams need to discover value and sell on outcomes; second, delivery and customer success teams need to ensure that they deliver on value for shared success; finally, product and marketing teams need to continually optimize the proposition based on customer insight. Customer value management is thus a process of mutual discovery and value co-creation.

Pages: 12

This chapter presents a process of value quantification for digital products and services. Value quantification is essential in order to implement value-based pricing and value-based selling. This chapter presents several examples of value quantification for digital platforms and SaaS products.

Pages: 88

No chapter abstract available.

Pages: 8

This chapter highlights the importance of pricing for SaaS businesses. Several tools, such as diagnosing current discounting practices, are helpful in order to increase profits via pricing. An important instrument in this respect is the pocket price waterfall that visually highlights differences between list, invoice, and net realized price levels.

Pages: 12

The main contribution of this chapter is the critical discussion of pricing approaches in the SaaS industry. The chapter reviews pertinent empirical studies, analyzes price-setting and price-getting practices in the industry, and outlines the capabilities required to master one important challenge: the move from subscription-based pricing to consumption-based pricing models. Several case studies provide best-practice examples.

Pages: 14

This chapter presents a framework for developing a pricing strategy for digital products. Key elements are offer design, sales enablement, and pricing execution. The chapter presents several approaches to acquiring, expanding, retaining, and regaining customers of digital products. The chapter furthermore presents several pricing approaches for digital products, including one-size-fits-all-pricing, good-better-best, functional, modular, customized, and hybrid pricing approaches. The chapter concludes by presenting some software pricing metrics: access-based, architecture-based, content-based, usage-based, outcome-based, and revenue sharing are some possible examples.

Pages: 22

This chapter presents best practices in subscription packaging, especially in the construction of a good/better/best (GBB) model. Leveraging numerous client engagements and industry-leading practices, they have developed a 3D pricing and packaging (P&P) framework to help companies launch and refine a GBB-based packaging model. Along the way, we have embedded real-life examples, specific guidance, and benchmarks.

Pages: 16

This chapter presents key challenges of digital pricing: selling value propositions, data-driven quantification of value, the design of value-driven pricing models, and the definition of subscription-based price metrics. To structure the pricing for smart-product-service offerings promisingly, a framework with four specific elements has been developed. To address the value propositions properly, this chapter presents four archetypes for offering smart-product-service systems. The chapter concludes by presenting an approach to quantify customer value for digital products and services.

Pages: 14

This chapter discusses market research approaches that may be useful for pricing digital products and services. The specific focus is on the price sensitivity meter and conjoint analysis. Several examples highlight the strengths and limitations of these market research approaches.

Pages: 54

No chapter abstract available.

Pages: 8

This chapter presents a nonexhaustive list of possible challenges related to the digital transformation. The focus of this chapter is on the team, the product, the technology, the data, and the need for continuous experimentation.

Pages: 14

This chapter presents an approach to segment industrial markets for smart services. It argues that the value of smart services depends not only on the digital maturity of organizations but also on the degree of buyer–supplier interdependencies and flexibility to make sense of data that has already been collected and processed. It concludes by discussing performance-based pricing mechanisms.

Pages: 14

This chapter discusses the steps to monetize data, outlines how to craft a data strategy that creates value, and discusses the role of value engineering. Key features of this chapter are considerations that help to understand and monetize the value of data.

Pages: 8

This chapter focuses on the economics of AI and how to extract value to boost revenues. Data science, machine learning, and AI platforms are tools to enable enterprise AI by allowing people within the organization to scale, providing transparency and reproducibility throughout—and across—teams. The two dimensions of AI success are capitalization and reuse.

Pages: 8

This chapter focuses on the pricing of data and how firms can benefit from the vast amount of data in their possession. While data is readily available, experts argue that the key to success in this area is the ability to reuse data models across applications and industries. The same principles of pricing and economics used in products and services must be applied to data as well.

Pages: 46

No chapter abstract available.

Pages: 10

This chapter discusses current approaches to pricing and monetizing in digital platforms and marketplaces and outlines recent trends. Current approaches include commissions, access fees, subscriptions, and supplementary service fees. This chapter discusses these approaches in detail and provides pertinent examples.

Pages: 8

This chapter discusses pricing strategies for digital marketplaces and platforms. It presents several approaches and makes the case for the need to invest in instrumentation and flexible service usage tracking.

Pages: 12

This chapter presents possible pricing approaches for platforms. Approaches include commissions, access fees, subscriptions, and supplementary service fees. The chapter discusses several examples of each approach.

Pages: 14

This chapter argues that the ability to run price experiments is a key benefit of online pricing. However, realizing this advantage requires a structured method for designing, running, and evaluating pricing experiments. It requires statistical sophistication to accurately evaluate results in the face of cannibalization. Furthermore, to avoid biases, the group designing, running, and interpreting experiments should be separate from those who have a stake in their outcomes. For these reasons, companies that plan on running multiple pricing experiments should develop an internal science-led group to manage the design and execution of the experiments as well as the validation of the experimental results.

Pages: 56

No chapter abstract available.

Pages: 8

This chapter outlines how artificial intelligence can help to increase the effectiveness of pricing. Potential benefits include improved price customization, improved segmentation, reduced errors, and improved sales forecasts.

Pages: 6

This chapter argues that artificial intelligence can be fruitfully applied to pricing and price optimization. It highlights several potential benefits and possible applications.

Pages: 10

This chapter makes the case for applying digitalization to the offer process. Automated pricing increases speed and thus possibly win rates, consequently benefitting suppliers and customers. The chapter argues that artificial intelligence works best when process and culture are transformed simultaneously.

Pages: 12

This chapter focuses on how artificial intelligence can assist B2B sellers in identifying and interpreting unmet customer needs and creating value-based offers that will delight customers and exceed their expectations. The chapter argues that mixed logit customer choice models provide an economic theory-based measure of perceived value for product and service solutions and of price sensitivity. Machine learning methods can be used to further optimize the predictive accuracy of mixed logit models.

Pages: 18

This chapter makes the case for price automatization. Digital technologies can be used to automatically generate offers that in the past required manual intervention. Price automatization increases speed and thus benefits suppliers as well as customers. Several examples highlight the potential benefits.