Dynamic Value Discrimination in Recurring Consumer Relationships: Re-centering Business on Human Values in the Digital Era
Posted: 20 May 2019
Date Written: May 14, 2019
Purpose: The ongoing digital disruption is being shaped by two opposing forces that have yet to be reconciled. Computer mediation drives a shift to recurring relationships that center on lifetime value rather than transaction value (Tzuo 2018). However, for digital services, the invisible hand breaks down and fails to establish value: consumers question the relationship between value and price, while suppliers struggle to impose artificial scarcity (Anderson 2009, Kumar 2014). A novel approach is proposed for sustainably mass-customizing value propositions and setting prices to reflect the lifetime value of the relationship -- including broad measures of reciprocal and social value.
Contributions: This work offers a novel perspective leading to suggestions for action at two levels:
• A specific strategy for using ongoing dialogs about perceived value to achieve dynamic value discrimination, drawing on post-pricing of experiences and levels of participative pricing that seek a fair balance of powers to share value and risk (Bertini 2013, Reisman 2018).
• A unifying theory of collaborative value measurement and value-seeking over a spectrum of conventional and radical strategies, and of for-profits, cooperatives, and non-profits (Reisman 2016). Questions: How do three interacting choices shape how businesses profit by co-creating value with consumers? • Who takes the pricing risk? Business? Consumer? Shared?
• Who decides the price? Business (usual)? Consumer (Pay What You Want)? Jointly?
• When do they decide it? Before selection/experience? At…? After…?
Conceptual framework / methodology: A conceptual architecture is applied to use-cases drawn from current and emerging practice, with focus on how digital services force rethinking of conventional economics and business practice. This draws on scholarship in marketing (Ballantyne 2011, Bertini 2012, Egbert 2014, Frow 2013, Payne 2013, Prahalad 2004, Smith 2012, Vargo 2008), behavioral economics (Natter 2015, Santana 2014, Spann 2018), and game theory (Greiff 2016), as well as evolving practice in consumer digital services (Anderson 2009, Kumar 2014, Tzuo 2018).
Findings: Value propositions and how they are framed play an important role in shaping customers’ expectations of value and their willingness to pay. A firm can develop and frame value propositions dynamically through an emergent learning process with the customer. Customer segments can be determined based on value perceptions and traits, with each requiring discrete value propositions that are designed and nudged toward mutual relationship goals.
Discussion and conclusion: This architecture helps clarify the learning process between customer and firm, where value perceptions are shaped dynamically based on expectations and recognition of value-inuse and -in-context. Identifying how value propositions can be designed to optimize co-pricing decisions 2 in a two way learning process offers much opportunity for further empirical investigation -- and for new uses of artificial intelligence, machine learning, and analytics. For managers, using co-pricing as a means for gaining deep customer insights offers potential to expand profitability and markets. Models for co-pricing could provide new basis for segmenting customers, based on their perceptions of value and their fairness traits. Involvement in co-pricing decisions can also offer opportunities for developing more value-centered relationships -- shifting norms toward trust, fairness, transparency, and commitment between a supplier and customer.
Keywords: value discrimination, value proposition, co-pricing, learning, relationships, digital transformation
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