Pricing plans for a financial advisory service

Abstract

Purpose – In cooperation with a German online retail bank, the aim of this paper is to investigate how the bank should price a new fee-only financial advisory service. Two types of pricing plans differ in terms of their strategies for determining monthly prices: a fixed monthly price that is identical for all clients (i.e. a flat pricing plan) or a monthly price that varies as a function of each client's assets under management (i.e. a volume pricing plan). Design/methodology/approach – With a discrete choice experiment, this article studies client preferences for the two types of plans. To ensure that the respondents understood the financial consequences of their decisions, a price calculator was embedded into the discrete choice experiment to enable the respondents to determine their individual monthly prices based on their assets under management. Findings – Methodologically, the price calculator is useful for simplifying mathematically complex decisions, and it provides additional valuable information for analysis. Substantively, the results show that clients perceive both types of pricing plans as equally attractive; however, the service provider's revenues would increase by up to 12 per cent if it uses the volume pricing plan. Originality/value – This research extends the stream of literature on the measurement of pricing plan preferences and offers guidance for service industries, such as telecommunications, cloud computing services, insurances, or transportation. It extends the use of discrete choice experiments to study client preferences for different pricing plans and also integrates a decision aid, i.e. a price calculator, in the experiment to assist clients in comparing alternatives more effectively.

Untangling the brand name from the branded entity

“…A statistical technique, used in an experimental setting, which aims to isolate individual attributes is conjoint analysis. This technique is situated within the multivariate data analysis family (Hair et al, 2010) and has been increasingly used in academic research within the marketing (Carroll and Green, 1995;Schlereth, 2014;Simpson and Radford, 2014) and branding (MacLachlan and Mulhern, 1991;Vriens and Frazier, 2003;Sonnier and Ainslie, 2011) domains. Conjoint analysis uses a trade-off decompositional approach, premised on multi-attribute utility theory.…”