Demand Effects Of Customer Centric Marketing And Revenue Management Christine Mathies, University of Technology Sydney Siegfried Gudergan, University of Technology Sydney Abstract The concurrent use of customer centric marketing and perishable asset revenue management practices can dampen demand as a result of unfairness perceptions. In this paper, we provide a model for explaining variations in customer demand in capacity constraint firms taking into account fairness judgements. According to our model, purchase decisions for services are based on the evaluation of alternative service offerings and their prices. This evaluation is, in turn, influenced by the coding of these service offerings. The rationale underlying this coding phase is based on theories from behavioural decision making and psychology. Keywords: customer-centric marketing, revenue management, fairness, demand Introduction The aim of any marketing activity is to influence demand in such a way as to maximise return on marketing expenditures (Rust, Lemon and Zeithaml 2004, p. 105). Two of the most prevalent marketing investments in service firms are customer centric marketing (CCM) and, due to the perishability of services, revenue (yield) management (PARM). CCM relies on customer relationships in order to maximise the lifetime value of current and potential customers (Rust et al. 2004). PARM, on the other hand, allocates perishable inventory units to existing demand to maximise revenues using price discrimination (Kimes 2000). Estimating the financial impact of these marketing investments requires decoding their influence on customer demand for services. CCM usually takes shape in loyalty programs or other forms of beneficial customer treatments. A frequent flyer member, for example, is offered the opportunity to collect bonus points and enjoy special benefits waiting list priority, and the like; aimed at increasing the customers’ utility and hence their demand. To the same frequent flyer, and any other customer to that effect, PARM practices become visible as availabilities of different fares, and associated restrictions (cf. Kimes and Wirtz 2003). Both availabilities and rates are constantly adjusted by means of sophisticated demand forecasting (Weatherford and Kimes 2003). Customers in CCM programmes may, however, also experience unanticipated consequences originating from PARM initiatives. For example, a customer perceiving herself as a valued frequent flyer might try to redeem points for an award booking for the Easter weekend, a time of peek demand. She is likely to be unsuccessful because the allocation for award bookings has been utilised, although internet booking platforms clearly show her that there are still seats available. While this customer could of course purchase one of the remaining high-priced tickets, the unsuccessful request contradicts not only the promised benefits of CCM programmesi.e. better customer valuebut being asked to pay a higher price than anticipated can also create unfairness. As the frequent flyer example shows, these unfavourable consequences of PARM and CCM influence demand for services and, in turn, the return on marketing investments. Service firms would hence benefit from a better understanding on how precisely these practices affect demand.
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Background The existing body of research in the realms of CCM and PARM helps shed some light on how such a counterproductive outcome can occur and demand is influenced. Contrasting the intricacies underlying the CCM and PARM approaches provides a basis for articulating possible conflicts experienced by customers as a result of facing both CCM and PARM. Customer Centric Marketing The literature on CCM and related marketing techniques is rather fragmented and inconsistent, but shares the core notion of establishing and maintaining profitable customer relationships (Paas and Kuijlen 2001; Parvatiyar and Sheth 2001; Reinartz, Krafft and Hoyer 2004; Zablah, Bellenger and Johnston 2004). As not all customers are equally desirable, the more recent concept of customer equity management is built on the total of a firm’s current and future customer lifetime values (Hogan, Lemon and Rust 2002; Rust et al. 2004), which guide the optimal mix of customer acquisition and retention (Blattberg, Getz and Thomas 2001). Service offerings for the most profitable customers are tailored accordingly (Noone, Kimes and Renaghan 2003). This provides the basis for preferential treatment of a frequent flyer member, who has been identified as a customer with a high lifetime value. Revenue Management of Perishable Asset PARM deals with the efficient use of relatively fixed, perishable capacities (Kimes 1989; McGill and van Ryzin 1999). The fundamental idea is to charge different prices for the same product to different customers in an attempt to balance demand and revenues per capacity unit (Weatherford and Bodily 1992). The extensive research in the field of PARM is mainly concerned with improvements to forecasting methods, and the heuristics and algorithms to best approximate the optimal allocation of capacity units to existing demand (Weatherford and Bodily 1992). The basic assumption that such capacity allocation is the sole approach to maximise revenues in capacity constrained industries however remains unquestioned. Potential Customer Conflicts Treated as competing marketing investments, PARM and CEM have contradictive consequences which limit their respective revenue maximisation potentials. The main difference between PARM and CCM is the time horizon for revenue maximisation. PARM maximises the revenue from a single transaction, i.e. the revenues per capacity unit, but fails to consider possible long-term gains from individual customers (Noone et al. 2003; Shoemaker 2003). CCM however is based on the premise that “revenue management is fundamentally about making the right short-term trade-offs to increase long-term revenues and profits” (Lieberman 1993, p. 105) and focuses on the lifetime revenues per customer. The example of the frequent flyer member wanting to book an award flight illustrates that allocation optimisation without considering long-term effects on customer relationships might be myopic and insufficient. Also, customer segmentation in PARM is based on price elasticities (Kimes 1989), while CCM distinguishes customers based on their lifetime profitability (Jain and Singh 2002). For instance, while a profitable frequent traveller might be treated unfavourably for showing high price elasticity for a particular booking, current research does not address how to manage profitable customers in service industries with capacity constraints. PARM discusses the allocation of capacity units to different price buckets, but does not examine how “fair” or
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“right” prices should be set. The frequent flyer passenger in our example is likely to be sufficiently annoyed not to purchase a flight ticket for his Easter weekend. In summary, the consequences originating from CCM and PARM initiatives affect a customer’s evaluation of services and can lead to perceived unfairness and customer alienation, which in turn influences customers’ likelihood to repurchase, i.e. they can dampen the overall demand for a particular offering. Notwithstanding these implications, the existing body of research, with a few sparse exceptions (cf. Kimes 1994; Kimes and Wirtz 2003), fails to adequately address potential effects of yield pricing on customers’ fairness perception and hence their willingness to (re-) purchase. This paper presents an approach to estimate demand by modelling consumer choices in the light of fairness issues. Estimating Demand under CCM and PARM Our conceptualisation of customer demand is based on expected utility theory with the actual evaluation phase being preceded by a fairness coding phase (Kahneman and Tversky 1979) According to our model, purchase decisions for services are based on the evaluation of alternative service offerings and their prices. This evaluation is, in turn, influenced by the coding of these service offerings (see Figure 1). The rationale underlying this coding phase is embedded in a set of theories from behavioural decision making and psychology. Prospect theory (Kahneman and Tversky 1979), and the more general reference-dependent preference theory (Munro and Sugden 2003), explain how alternatives are coded relative to a reference point. Adaptation-level theory elaborates on how reference points of perception are formed (Helson 1948). The fairness and justice literature further informs which mechanisms individuals use in setting reference points and prices, and in editing alternatives. Distributive justice is concerned with whether outcomes are perceived as fair, and is intrinsically tied to equity theory and social judgement (Arino and Ring 2003; Konow 2003; Xia, Monroe and Cox 2004). Procedural justice explains issues of fair cost-profit distribution and the process leading to outcomes, and rests upon the principle of dual entitlement and attribution theory (Kahneman, Knetsch and Thaler 1986; Maxwell 2002). Interactional or transaction justice refers to fairness judgements of interpersonal treatment, including issues of asymmetrical power and trust (Huppertz, Arenson and Evans 1978; Bolton, Warlop and Alba 2003). Figure 1. Two-Staged Decision Process
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Two Stages in Consumers’ Choices Expected utility theory makes the assumption that individuals assess their options and select the alternative with the highest utility resulting from price and product/service attributes (Thaler 1980). In our formalisation, customer n obtains a certain level of utility unj from each available, mutually exclusive alternative j =[1;…J]. U nj = v nj + nj ,
(1)
where vnj is the systematic and nj the random component of utility (Louviere, Hensher and Swait 2000). Consumers’ real life choices, however, regularly violate the predictions of expected utility theory, and might be better captured by choice theories that account for decision framing and reference points. Kahneman and Tversky’s (1979) prospect theory comprehensively explains these systematic deviations from the principle of utility maximisation, and recognises that a coding phase precedes the actual evaluation and choice phase. PARM techniques, and especially the contradictory nature of CCM and PARM activities, implies that offerings are likely to be negatively edited as a result of perceived unfairness (Kimes and Wirtz 2003). In fact, fairness has been found to explain a large part of deviations from utility maximisation (Konow 2003). Fairness in general refers to the judgement of an outcome and/or the process to arrive at this outcome as reasonable, acceptable, or just (Bolton et al. 2003; Xia et al. 2004). It is therefore proposed that consumer choices are affected by a perceived fairness component. The systematic utility component can be rewritten as: v nj = nj + 1nj PPnj + 2 nj FAnj ,
(2)
where nj is a consumer and alternative specific constant, PPnj is the utility component derived from the alternative’s price and product/service attributes (PALnjx), and FAnj captures the utility changes created by fairness-based coding of these attributes PALnjx. The term representing the fairness adjustments to overall utility can be specified as follows:
FA nj = w njx * (PAL njx - RAL nx )
(3)
x
PALnjx is the perceived level of attribute x of alternative j, RALnx is the generic reference attribute level for all alternatives, and wnjx is the importance weight of PALnjx.- RALnx. The Role of Reference Attribute Levels Fairness adjustments are firstly a result of a comparison of the perceived attribute level PALnjx for each attribute x with the corresponding reference level RALnx (Kahneman et al. 1986; Frey and Pommerehne 1993; Maxwell 2002; Xia et al. 2004). This is the core idea of decision framing, where alternatives are coded relative to a reference point (Kahneman and Tversky 1979; Munro and Sugden 2003; Köszegi and Rabin 2004). Four factors are proposed to influence the reference levels RALnx. RALnx = f ( RE , RK , SP, CO ) = 0 + 1 * RE + 2 * RK + 3 * SP + 4 *CO + njx
(4)
RE Reference Experiences. Customers rely on their personal experiences with a specific product/service category and/or suppliers, because consumers’ memory for chosen as opposed
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to rejected options is particularly strong (Briesch, Krishnamurthi, Mazumdar and Raj 1997). A frequent flyer has a good idea of typical rates and availabilities based on past purchases. RK Reference Knowledge. Customers also include knowledge about offerings that they have not chosen in the past (Kalyanaram and Winer 1995). This knowledge is a result of past observations and personal experiences of peers. Equity theory in fact substantiates that consumers look at their own past experiences and comparative others to ensure equality of outcomes (Xia et al. 2004) Our loyal airline customer might have talked to friends and family about their recent flight bookings, and might have seen favourable rates advertised in the past. SP Semantic Presentation. The way in which objective differences in offerings are presented also has an influence on the perceived utility, i.e. whether they are communicated as gains or losses (Kahneman and Tversky 1979; Burton and Babin 1989). In our example, a standard rate could be promoted as an Easter special, suggesting that it is a bargain buy. CO Contextual Offerings. Firms using PARM might advertise a range of price-product combinations at any given time. Although other present stimuli have an effect on perception (Helson 1948) and reference price formation (Rajendran and Tellis 1994), this effect has been neglected in previous studies. Airline customers, for example, may search for flights online, and find that the cheaper booking classes listed by a particular airline are no longer available. Importance of Perceived Differences Not every discrepancy between the actual attribute level and the reference attribute level PALnjx - RALnx is equally prevailing in an individual’s fairness judgements. Due to the subjectiveness of fairness judgements (Maxwell 2002), the difference is subject to an importance weighting wnjx which comprises of three elements. wnjx = + pe * penjx + fa * fanjx + de * denjx,
(5)
penjx Perceived Entitlement. Procedural justice argues that a customer’s knowledge about price setting and bundling procedures, i.e. a firm’s entitlement to charge a given price, influences how an offering is perceived (Kachelmeier, Limberg and Schadewald 1991; Maxwell 2002). For a consumer it is hard to understand why different passengers would pay different fares for the same flight. fanjx Future Availability. Customers differ in both their willingness to postpone decisions under uncertainty, and their knowledge and experience about whether an offer is still likely to be available in the near future (Kalyanaram and Winer 1995). Speculations about future choices, a special case of determining probabilities of outcomes under uncertainty (Kahneman and Tversky 1979), are expected to impact consumers’ perception of current offerings. More experienced frequent flyers tend to be more confident about the quality of an offer. denjx Distribution of Expectations. The chances of perceived unfairness increase with the closeness and frequency of transactions (Huppertz et al. 1978), as argued in interactional justice theory. The more consistent experiences a customer has with a product/service category and/or supplier, the more rigid and narrow becomes his decision frames. If a frequent flyer always flies to Brisbane for $200, his price expectations will have little leeway.
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Conclusion We provide a model to explain variations in customer demand in capacity constrained firms, taking into account fairness judgements. In our model, purchase decisions for services are based on the evaluation of alternative offerings and their prices. This evaluation is, in turn, influenced by the coding of these service offerings. The rationale underlying this coding phase is embedded in a set of theories from behavioural decision making and psychology.
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Reinartz, W.J., Krafft, M. and Hoyer, W.D. 2004, "The Customer Relationship Management Process: Its Measurement and Impact on Performance", Journal of Marketing Research, vol. 41, no. August 2004, pp. 293-305. Rust, R.T., Lemon, K.N. and Zeithaml, V.A. 2004, "Return on Marketing: Using Customer Equity to Focus Marketing Strategy", Journal of Marketing, vol. 68, no. January, pp. 109-27. Shoemaker, S. 2003, "The Future of Pricing in Services", Journal of Revenue & Pricing Management, vol. 2, no. 3, pp. 271-79. Thaler, R. 1980, "Toward a Positive Theory of Consumer Choice", Journal of Economic Behaviour and Organization, vol. 1, pp. 39-60. Weatherford, L.R. and Bodily, S.E. 1992, "A Taxonomy and Research Overview of Persishable-Asset Revenue Management: Yield Management, Overbooking, and Pricing", Operations Research, vol. 40, no. 5, pp. 831-44. Weatherford, L.R. and Kimes, S.E. 2003, "A Comparison of Forecasting Methods for Hotel Revenue Management", International Journal of Forecasting, vol. 19, no. Issue 3, p. 401. Xia, L., Monroe, K.B. and Cox, J.L. 2004, "The Price Is Unfair! A Conceptual Framework of Price Fairness Perception", Journal of Marketing, vol. 68, no. October, pp. 1-15. Zablah, A.R., Bellenger, D.N. and Johnston, W.J. 2004, "An Evaluation of Divergent Perspective on Customer Relationship Management: Towards a Common Understanding of an Emerging Phenomenon", Industrial Marketing Management, vol. 33, pp. 475-89.
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