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Simulating consumer behaviour a perspective on how to model consumer behaviour for environmental policy Wander Jager University of Groningen (NL) Dept. of Marketing Environmental relevant behaviour Environmental problem awareness plays at best a limited role Finances and daily hassles play a dominant role in deciding to behave environmentally friendly Environmental (relevant) behaviour We have to realise that environmental outcomes often hardly affect the decision-making of consumers. Consumers may behave environmental friendly without environmental concern playing a (relevant) role as driver of that behaviour. Labelling behaviour as ‘environmentally relevant’ does not provide additional understanding of the drivers of that behaviour, nor does it provide extra cues for effective policy measures. Hence, in analysing and changing environmental relevant behaviour, standard behavioural analysis applies. Behavioural analysis Needs as motives of consumer behaviour Consumer decision making: cognitive effort & individual and social dimensions Human needs: Maslow (1954) Human needs: Max-Neef (1992) Subsistence Protection Affection Understanding Participation Leisure Creation Identity Freedom Human needs Lower needs (related to daily hassles) are experienced on a more frequent basis and more directly than higher needs Higher needs, especially related to the environment, are more cognitive (not directly and personally experienced), and are more likely to dominate cognitive responses (questionnaire) Lower needs are more emotionally driven, and may have a strong effect on behaviour without cognitively being experienced. Human needs - Behaviour may have effect on different needs simultaneously: - destroyers or violators - pseudo-satisfiers - inhibiting satisfiers - singular satisfiers - synergistic satisfiers Human decision-making - Cognitive effort - Social versus individual orientation Decision-making People have limited cognitive resources. The allocation of our limited cognitive resources depends on a.o: - Consumer involvement (relevance of needs) - Complexity of the decision - Newness of the decision - Constraints (e.g. time, cognitive capacity) Low involvement will result in the use of simple strategies using limited amounts of information. Decision-making Using social information is often a smart strategy to save on cognitive effort. Social decision-making is more likely when: - Lack of clear information (complex, social relevant) - Visibility of behaviour is high or communication is easy - Higher social susceptibility (personality) Decision-making - Exchange of information is more likely if people are similar on the relevant dimension of comparison - Network effects play a pivotal role Why multi-agent simulation? Complexity Many interacting people/consumers Heterogeneity of population Easy experimentation with policy measures GLM versus social simulation SocSim: How to respond to different (unanticipated) SocSim: what are more and SocSim: How to sail? developments? less likely routes? GLM: How do changes in GLM: the instrumentarium where do we arrive affect GLM: Where do we arrive? given the expected the current arrival? situation? What behaviour to simulate? - Environmental behaviour does not exist - We should model consumer behaviour in general Why not to use too simple rules - Simple rules – e.g., as used in econophysics – do not represent the behavioural drivers and dynamics that typify consumer behaviour - Many policy measures are aimed at changing behavioural drivers and processes - A simple (too) model may mimick the real system (calibration), but that does not guarantee that testing ploicy measures in such a context lead to realistic behavioural changes What to include in agent rules? - Human needs - Human decision-making Example - Lakeland Goals of market simulation - Understand market dynamics emerging from individual consumer behaviour (micro-macro dynamics) - Explore the effects of various interventions/producer behaviour on these dynamics Market dynamics and social simulation - Several simulation models have been developed to model market dynamics - These models generally lack an integrative perspective on marketing strategies - Marketeers (and policy makers in general) would benefit from having simulation tools that also allow for testing marketing strategies Market dynamics and social simulation - Agent architecture has to reflect the keydrivers of consumer behaviour - Marketing strategies can be implemented as affecting these key drivers The four P’s of Marketing (McCarthy, 1960) product pricing placement promotion Product brand name functionality styling quality safety packaging repairs and support warranty accessories Services Price pricing strategy (skimming, penetration) retail price volume discounts and wholesale pricing cash and early payment discounts seasonal pricing bundling price flexibility and price discrimination Placement use of distribution channels market coverage specific channel members inventory management warehousing distribution centres order processing transportation and reverse logistics Promotion promotional strategies (push & pull) advertising personal selling & sales force sales promotions public relations and publicity the marketing communications budget Formalising the four P’s Formalising all the factors just mentioned would – if possible – result in a model not accessible for research A simplification is required for developing a transparent simulation model The formalisations as presented are meant as a framework, simpler models can (and should) be used, and if necessary extended using the framework Product Product characteristics relate to individual preferences Vector model of preferences: the more, the better (quality, service) Uinj = Ajn With: Uinj = Utility of consumer i on attribute n for product j Ajn = Score of product j for attribute n Product Ideal point model of preferences: relative position on a scale (design, colour, taste) Uinj = 1 - |Ajn - Pin| With: Uinj = Utility of consumer i on attribute n for product j Ajn = Score of product j for attribute n Pin = Preference of consumer i for attribute n Product Besides individual preferences, consumers also have social preferences for products Networks play a critical role in social effects Uinj = Nj/N With: Uinj = Utility of consumer i on attribute n (here the social attribute) for product j Nj = Number of neighbours consuming product j N = Number of neighbours Product The utilities (vector, ideal point and social) are summed to constuct a total utility. Beta indicates the relative weight of each utility in the total utility ( n U ij 1 n *U ijn ) n With: Uij = Utility of consumer i for product j, ranging from 0 to 1 ßn = Weighting of attribute n, ranging from 0 to 1 Uijn = Utility of consumer i for product j for attribute n Product The weighting of utilities can be different for different agents, thus including heterogeneity in the consumer population ( n U ij 1 in *U ijn ) n With: Uij = Utility of consumer i for product j, ranging from 0 to 1 ßn = Weighting of attribute n, ranging from 0 to 1 Uijn = Utility of consumer i for product j for attribute n Product The decision process of consumers can be represented by the values of the betas Cognitive effort: the number of product aspects taken into account (involvement) Social v.s individual orientation: weighting of social utility Product Note! The formulation of utility has the lay-out of a regression formula But: for each simulated consumer this formula may be different. Moreover, the utilities and their weigting are subject to change Price The concept of value-for-money is being used to link price to utility the value for money will be closer to the utility of the product the lower its price and the higher the consumers budget Vij U ij * Bi * (1 Pj ) With: Vij = Value for money of product j for consumer i Uij = Utility of consumer i for product j Pj = Price of product j, ranging from 0 to 1 Bi = Budget of consumer I, ranging from 0 to 1 Placement Focus on distance: - Simple formalization: distance as additional attribute in the model - Heterogeneity in distance score expresses the distance to a buying location. - Weighting the distance attribute (with a ß) distinguishes between markets where distance is important (e.g., groceries) versus unimportant (e.g., e-commerce) Promotion – by producer - Convince consumers to attach more weight to a product attribute on which the product scores well (increasing the ß) - Convincing the consumers that their utility for attribute n would be higher than they currently believe (increase Uinj). - Inform consumers about other consumers (famous role models) that already use a product, thus affecting the social attribute. Promotion – by producer - Who to address? - Mass media - Random consumers - Consumers with particular characteristics (segments) - Clusters of connected consumers - A mix of strategies? Promotion – consumers: word-of-mouth - Specific information: exchange information concerning the product utilities (Uinj), e.g. fuel consumption of a car - Generic information: discuss the importance of certain attributes (weighting of attributes), e.g. the importance of safety of a car. - Norms: consider the number of neighbours consuming a particular product without considering further information (social attribute as defined in product) Promotion – consumers: word-of-mouth -Network effects are critical Regular lattice Small World Scale Free Empirical data Macro level sales data (& timing of marketing strategies) – development of market shares over time and indication of marketing effects Micro level sales data (loyalty card data) – how do consumers behave in a market Micro level data on decision-making – getting grip on the decision-making process of consumers (attributes & weights) Conclusions Formalising the four P’s provides a perspective on: modelling complexities in markets implementing and testing marketing strategies in complex markets The linkage of simulation models to emperical data on both the micro and macro level Research organisation Micro level data (networks) hypothesising Macro level data (markets) formalisation Agent & product characteristics experimentation validation Simulation of market dynamics