Simulating consumer behavioura perspective on how to model consumer

behaviour for environmental policy

Wander JagerUniversity of Groningen (NL)

Dept. of Marketing

Environmental relevant behaviour

Environmental problem awareness plays at best a Environmental problem awareness plays at best a limited rolelimited role

Finances and daily hassles play a dominant role in Finances and daily hassles play a dominant role in deciding to behave environmentally friendlydeciding to behave environmentally friendly

Environmental (relevant) behaviour WWe have to realise that environmental outcomes often hardly e have to realise that environmental outcomes often hardly

affect the decision-making of consumers.affect the decision-making of consumers.

Consumers may behave environmental friendly without Consumers may behave environmental friendly without environmental concern playing a (relevant) role as driver of that environmental concern playing a (relevant) role as driver of that behaviour.behaviour.

Labelling behaviour as ‘environmentally relevant’ does not Labelling behaviour as ‘environmentally relevant’ does not provide additional understanding of the drivers of that behaviour, provide additional understanding of the drivers of that behaviour, nor does it provide extra cues for effective policy measures.nor does it provide extra cues for effective policy measures.

Hence, in analysing and changing environmental relevant Hence, in analysing and changing environmental relevant behaviour, standard behavioural analysis applies.behaviour, standard behavioural analysis applies.

Behavioural analysis

Needs as motives of consumer behaviourNeeds as motives of consumer behaviour Consumer decision making: cognitive effort & Consumer decision making: cognitive effort &

individual and social dimensionsindividual and social dimensions

Human needs: Maslow (1954)

Human needs: Max-Neef (1992)

SubsistenceSubsistence ProtectionProtection AffectionAffection UnderstandingUnderstanding ParticipationParticipation LeisureLeisure Creation Creation IdentityIdentity Freedom Freedom

Human needs Lower needs (related to daily hassles) are Lower needs (related to daily hassles) are

experienced on a more frequent basis and experienced on a more frequent basis and more directly than higher needsmore directly than higher needs

Higher needs, especially related to the Higher needs, especially related to the environment, are more cognitive (not directly environment, are more cognitive (not directly and personally experienced), and are more and personally experienced), and are more likely to dominate cognitive responses likely to dominate cognitive responses (questionnaire)(questionnaire)

Lower needs are more emotionally driven, Lower needs are more emotionally driven, and may have a strong effect on behaviour and may have a strong effect on behaviour without cognitively being experienced.without cognitively being experienced.

Human needs

- Behaviour may have effect on different needs Behaviour may have effect on different needs simultaneously:simultaneously:

- destroyers or violatorsdestroyers or violators

- pseudo-satisfierspseudo-satisfiers

- inhibiting satisfiersinhibiting satisfiers

- singular satisfierssingular satisfiers

- synergistic satisfierssynergistic satisfiers

Human decision-making

- Cognitive effort

- Social versus individual orientation

Decision-making

People have limited cognitive resources. The allocationPeople have limited cognitive resources. The allocation

of our limited cognitive resources depends on a.o:of our limited cognitive resources depends on a.o:- Consumer involvement (relevance of needs)Consumer involvement (relevance of needs)- Complexity of the decision Complexity of the decision - Newness of the decisionNewness of the decision- Constraints (e.g. time, cognitive capacity)Constraints (e.g. time, cognitive capacity)

Low involvement will result in the use of simple Low involvement will result in the use of simple strategies using limited amounts of information.strategies using limited amounts of information.

Decision-making

Using social information is often a smart Using social information is often a smart strategy to save on cognitive effort. Social strategy to save on cognitive effort. Social decision-making is more likely when:decision-making is more likely when:

- Lack of clear information (complex, social Lack of clear information (complex, social relevant)relevant)

- Visibility of behaviour is high or Visibility of behaviour is high or communication is easycommunication is easy

- Higher social susceptibility (personality)Higher social susceptibility (personality)

Decision-making- Exchange of information is more likely if people are Exchange of information is more likely if people are

similar on the relevant dimension of comparisonsimilar on the relevant dimension of comparison

- Network effects play a pivotal role- 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

GLM: where do we arrive given the current situation?

GLM: How do changes in the instrumentarium affect the expected arrival?

SocSim: what are more and less likely routes?

SocSim: How to respond to different (unanticipated) developments? GLM: Where do we arrive?SocSim: How to sail?

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 key-drivers 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 Formalising all the factors just mentioned would – if possible – result in a model not would – if possible – result in a model not accessible for researchaccessible for research

A simplification is required for developing a A simplification is required for developing a transparent simulation modeltransparent simulation model

The formalisations as presented are meant The formalisations as presented are meant as a framework, simpler models can (and as a framework, simpler models can (and should) be used, and if necessary extended should) be used, and if necessary extended using the frameworkusing 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 jAjn = 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 jAjn = Score of product j for attribute nPin = 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 jNj = Number of neighbours consuming product jN = 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

With:Uij = Utility of consumer i for product j, ranging from 0 to 1ßn = Weighting of attribute n, ranging from 0 to 1Uijn = Utility of consumer i for product j for attribute n

n

UU ijn

n

nij

)*(1

Product

The weighting of utilities can be different for different agents, thus including heterogeneity in the consumer population

With:Uij = Utility of consumer i for product j, ranging from 0 to 1ßn = Weighting of attribute n, ranging from 0 to 1Uijn = Utility of consumer i for product j for attribute n

n

UU

ijn

n

in

ij

)*(1

Product

The decision process of consumers can be The decision process of consumers can be represented by the values of the betasrepresented by the values of the betas Cognitive effort: the number of product Cognitive effort: the number of product

aspects taken into account (involvement)aspects taken into account (involvement) Social v.s individual orientation: weighting Social v.s individual orientation: weighting

of social utilityof social utility

Product

Note! The formulation of utility has the lay-out Note! The formulation of utility has the lay-out of a regression formulaof a regression formula

But: for each simulated consumer this formula But: for each simulated consumer this formula may be different. Moreover, the utilities and may be different. Moreover, the utilities and their weigting are subject to changetheir weigting are subject to change

Price The concept of value-for-money is being used The concept of value-for-money is being used

to link price to utilityto link price to utility the value for money will be closer to the utility the value for money will be closer to the utility

of the product the lower its price and the of the product the lower its price and the higher the consumers budgethigher the consumers budget

With:Vij = Value for money of product j for consumer iUij = Utility of consumer i for product jPj = Price of product j, ranging from 0 to 1Bi = Budget of consumer I, ranging from 0 to 1

)1(** jiijij PBUV

Placement

Focus on distance:Focus on distance:

- Simple formalization: distance as additional - Simple formalization: distance as additional attribute in the model attribute in the model

- Heterogeneity in distance score expresses the - Heterogeneity in distance score expresses the distance to a buying location. distance to a buying location.

- Weighting the distance attribute (with a ß) - Weighting the distance attribute (with a ß) distinguishes between markets where distance is distinguishes between markets where distance is important (e.g., groceries) versus unimportant important (e.g., groceries) versus unimportant (e.g., e-commerce)(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 - Specific information: exchange information concerning the product utilities (Uinj), e.g. fuel concerning the product utilities (Uinj), e.g. fuel consumption of a car consumption of a car

- Generic information: discuss the importance of - Generic information: discuss the importance of certain attributes (weighting of attributes), e.g. certain attributes (weighting of attributes), e.g. the importance of safety of a car. the importance of safety of a car.

- Norms: consider the number of neighbours - Norms: consider the number of neighbours consuming a particular product without consuming a particular product without considering further information (social attribute considering further information (social attribute as defined in product)as defined in product)

Promotion – consumers: word-of-mouth

-Network effects are critical-Network effects are critical

Regular lattice Small World Scale Free

Empirical data

Macro level sales data (& timing of marketing Macro level sales data (& timing of marketing strategies) – development of market shares strategies) – development of market shares over time and indication of marketing effectsover time and indication of marketing effects

Micro level sales data (loyalty card data) – Micro level sales data (loyalty card data) – how do consumers behave in a markethow do consumers behave in a market

Micro level data on decision-making – getting Micro level data on decision-making – getting grip on the decision-making process of grip on the decision-making process of consumers (attributes & weights)consumers (attributes & weights)

Conclusions

Formalising the four P’s provides a Formalising the four P’s provides a perspective on: perspective on: modelling complexities in marketsmodelling complexities in markets implementing and testing marketing implementing and testing marketing

strategies in complex marketsstrategies in complex markets The linkage of simulation models to The linkage of simulation models to

emperical data on both the micro and emperical data on both the micro and macro levelmacro level

Research organisation

Micro level data (networks)

Agent & product characteristics

Simulation of market dynamics

Macro level data(markets)

hypothesising

formalisation

experimentation

validation