Modelling Market Dynamics and Consumer Behaviour From The Bottom Up

1. Modelling Market Dynamics and Consumer Behaviour From The Bottom Up Iqbal Adjali Mathematics & Informatics Unilever R&D, Colworth Science Park Bedford, UK First ESSA Summer School Brescia 15 September 2010

2. Unilever R & D Consumer Modelling Research

3. Unilever – the Vitality Company!

4. Some Unilever Brands

5. Leading Categories & Brands Spreads Weight Management Tea Ice Cream Foods Home and Personal Care Savoury & Dressings Skin Deodorants Laundry #1 in D&E Daily Hair Care # 1 in D&E World Number 1 Household Care World Number 2 Oral Care Local Strength Our 12 €1 billion brands

6. Feel Good, Look Good, Get more out of life…

7. Mathematics & Informatics Group Mathematics Psychology Behaviour Change Theory Interactive Systems Persuasive Technology On-line Therapy Personalisation Algorithms Agent Based Methods Network Science Graphical Models Economics/Game Theory Consumer Complexity Natural Language Processing Ubiquitous Computing Connected Sensors Computer Science

8. Agenda Background and Motivation • Marketing science – the legacy • The social simulation approach to Marketing • Challenges in applying social simulation to market and consumer behaviour Application Examples • Modelling the diffusion dynamics of the trial patterns of an online grocery store • Break after 1h30mn? • Modelling the volatility in market shares – A Micro-simulation approach • Social network interventions – Experimental study • Social network interventions – ABM of the take-up of physical exercise General Discussion

9. Why is Understanding Consumer Behaviour Important? As an applied field, it’s relevant to: • Business and market strategists • Marketing managers/practitioners • Market regulatory bodies • Consumer groups As a research discipline, it’s relevant to: • Economics, sociology, psychology, anthropology, biology • Key to understanding human behaviour

10. Consumer BehaviourComplexity and Emergence • Unpredictability of market behaviour (e.g. path dependence): Stochastic behaviour of consumers • Consumer-consumer interactions: e.g. Word-of-Mouth dynamics • Consumer lock-in phenomena: what makes a new product successful • Understanding and measuring the effect of advertising / marketing interventions

11. Consumer Behaviour Literature:Two Schools of Thought • Economic theory • Consumer behaviour is based on Demand Theory and utility maximisation • Purchase behaviour is a direct function of external economic factors (price, income,...) • Behavioural Sciences • Internal and non-economic factors are considered • Based on statistical models • Fragmented field • Data driven

12. Consumer Behaviour:Economic Approach • Traditional Theory of Demand culminates in Lancaster’s Consumption Technology Matrix Rule for choice selection Logit Model Ui utility of product i has for the consumer Kelvin Lancaster, “Consumer Demand: A New Approach”, Columbia University Press - NY, 1971

13. Consumer BehaviourEconomic Approach • Advantages • Mature theory - existence of consensus • Amenable to quantitative modelling & analysis • Disadvantages • Focuses on external economic factors • Unrealistic assumptions (e.g. perfect information, rationality) • views consumer behaviour as the outcome of the consumer decision process

14. Behavioural Sciences:Some Approaches Search and evaluation, innovative behaviour, change behaviour, e.g. new product launch Habitual behaviour, e.g. influence of advertising Post-purchase behaviour, e.g. consumer satisfaction/loyalty Influence of society on decision process, e.g. Word-of-mouth Theory of Planned Behaviour Stimulus-Response Models Cognitive Dissonance Theory Social Network Theory

15. Theory of Planned Behaviour Predispositions Motivations Behaviour Attitudes F.M. Nicosia, Consumer Decision Processes, Prentice-Hall, 1966 Outcome beliefs Attitudes Intention Behaviour Reference beliefs Norms Control beliefs Perceived control

16. Behavioural Sciences Approach • Advantages • takes into account internal (psychological) and social and cultural factors • more accurate in describing observed behaviour • Disadvantages • No unified approach; disparate and often conflicting models • No formal models, statistical inference only technique that can often be used • Data difficult to get

17. Social Simulationand Consumer Behaviour Bottom-up modelling approach that allows one to • deal with consumers as individuals • rules and mechanisms of arbitrary complexity • take into account consumer-consumer interactions • consider time evolution/dynamics Useful for both theory building and practical applications Forces researcher to develop formal frameworks

18. Social Simulation vs. Traditional Methods Relative Merits Separate Models Integrated Framework

19. Key Challenges forConsumer Behaviour Modelling • Data availability/gathering • Standardised model building approaches – for comparison/duplication • Model testing/validation methodologies

20. Model Validation • Model validity is crucial for our purposes, as a robust agent based simulation will be one based on a validated MODEL, giving GOOD PREDICTIONS, and providing USEFUL INSIGHTS Model Validation refers to the process of selecting the right model to match observations with predictions, given model complexity. Validation increases both insight and prediction. P R E D I C T I O N Model complexity acts as the constraint on the whole exercise. I N S I G H T

21. Validation Approaches • K-I-S-S (Keep It Simple Stupid) • Quantitative (Fagiolo, 2005; Werker, 2004; Windrum, 2007) • Numerical techniques to search parameter space efficiently • Model calibration with macro AND micro level observations • Use of optimization techniques like GA/GP/ANT/SWARM • Qualitative (Garcia, 2007) • History friendly approach • Based more on expert opinions and stylized facts • Draws heavily from established theory • Experimental (Richetin et. al. 2009 and our ongoing collaboration) • Uses in-situ human players to run experiments • Simulation designed to mimic experiment OR experiments designed to test simulation • Hybrid approach using quantitative, qualitative and experimental styles • Cutting edge interdisciplinary

22. Advancing Consumer Behaviour Research • Investigate impact of advanced psychological theories on consumer behaviour • Investigate role of social networks and context dependency in determining consumer behaviour • Design controlled experiments (e.g. to test word-of-mouth dynamics, effect of advertising,…) • Closer collaboration across disciplines and between academia and industry

23. A Spatio-temporal Agent Based Model of Consumer Trials of a Swiss Online Grocery Store

25. Modelling the diffusion of a new consumer service • Context & Motivation • Diffusion dynamics • Bass, ABM • Data • The LeShop database • Model Specification • Simulation results • Conclusion

26. Introduction Background • Diffusion dynamics is an important concept in Marketing and the modelling of markets • Different approaches have been used in the literature (aggregate, disaggregate, statistical,…) • Difficulty in getting the right data for the right approach Motivation • Data available: individual-based customer transactions from the online grocery supermarket (LeShop) and geo-statistical data • Develop an empirically motivated ABM • Opportunity to integrate an ABM in a spatial (geographical) context • Research question*: Do neighbourhood effects play an important role in the spatio-temporal dynamics of information diffusion? *Bell, D. and Song, S. (2004). Social contagion and trial on the internet: Evidence from online grocery retailing. Working paper, Wharton School Marketing Department.

27. Modelling Diffusion: Bass • Bass Diffusion Model* • Top-down (aggregate) model • Global variables/parameters • Has been successful in predicting market take-up of innovations in consumer durables • Not easily generalisable (to many products and many competitors) • Restrictive assumptions (e.g. homogeneous population, perfect mixing…) *Bass, F., “A new product growth model for consumer durables”, Management Science 15(5), 1969

28. Shopper and Market Data ABM Platform Transaction data GIS/demo data Advertising data Quality data Customer transaction data Office of Swiss Stats Marketing Agencies Marketing Agencies

29. DataDescription Customer data GIS/demo data Three Linked Datasets: • Customers, Transactions, Products (1998-2003) Customer Table (~4.5k): • UserID,ZIP_Code,Age_Range,Gender,Language Transaction Table (~2m) : • User_ID,Basket_ID,Purchase_Date_Time,SKU_Number,Price,Quantity,Discount Product Table (~10k): • SKU_Number$Category$Sub_Category$Brand_ID$Quality

30. Cumulative trials by language

31. Monthly trials by language

32. The Swiss District Postcode MapGIS census data

33. The Customer Agents

34. Social NetworksRecommendation diffusion • We looked at the diffusion of the recommended membership (Only components with at least 9 members are taken into account) • 2002-2004 remarkable increase in “horizontal” connectivity – people started to react earlier, recommendations are taken during the same year 2000 2001 2002 2003 2004 2005

35. Recommendation networkAge distribution • Again, we analyse only components with more than 9 members! • Notable absence of older generations early on in the recommendation patterns!! 15-17y 18-24y 25-34y 35-44y 55-64y

36. Recommendation network: Gender and geographical distribution AG ZH GE VS Families have a big role in recommendation scheme! VD NE FR OTHER Intercantonal connections which surpass language barriers are due to Lausanne – Zurich connection and to “small” cantons.

37. Agent Behaviour The only endogenous variable in agents is the trial state: Aij. It evolves according to the following rule: • If Aij=0 then invoke the external influence rule: : random number drawn from uniform distribution [0,1] • If Aij still =0 then invoke the internal influence rule: • Implement a small world network for social interactions

39. Simulation Results • We performed several hundred simulation runs, sweeping the parameter space (F,G,F,G). • We calibrate the model on the real aggregate trial data by maximising Goodness-of-fit according the LOESS algorithm. • This procedure led to the following calibrated parameter values:

40. Cumulative Customer Trials

41. Real vs. Simulated Trials French German

42. Goodness of fit – LOESS Statistics

43. Spatio-Temporal Diffusion ABM Actual Each customer agent is unique and reacts to: Global Communication and Word of Mouth interactions Monthly Customer Trials Merger with Migros (Janurary 2004) Promotional Campaigns I. Adjali et al, (2007) “An Agent Based Model of Customer Tirials in an Online Grocery Store", presented at: ABM Marketing Conference, Groningen, 22 August 2007 Cumulative Total Customers

44. Conclusion (Innovation Dynamics) • A social simulation approach to marketing does make sense: it is a very effective way to capture: • Market and consumer heterogeneity • Consumer interactions • Comparison with real data is a crucial step in validating the model (aggregate and individual levels) • Neighbourhood (spatial) effects do exist and seem to matter even in online markets • Useful to consider the spatial dimension explicitly when appropriate • Challenges: explore parameter space, macro vs. micro validation, look at different diffusion processes e.g. product launches, opinion dynamics…

45. What Social Simulation Means For The Marketing Discipline • Computational modelling of individual consumer behaviour, social networks and brand relationships leads to better insight in consumer behaviour and market dynamics • Enhance predictive power of marketing mix models • develop robust marketing strategies through detailed scenario analyses

46. Volatility in the Consumer Packaged Goods Industry – A Micro-Simulation Based Study In collaboration with Nigel Gilbert and Alan Roach, Surrey University Frank Smith and Stephen Glavin, UCL Partly published in: Sengupta et al., Advances in Complex Systems 2010

47. Introduction • Consumer goods markets • Characterised by noisy market share dynamics and instability (Jager: 2007) • Heterogeneous consumers with wide variety of tastes/preferences (Allenby, Rossi: 1998) • Intense competitive interventions from multiple firms using pricing, promotions, advertising etc. (Ailawadi et. al.: 2001, Blattenberg, Wisniewski: 1989). • Additionally, social networks, WoM may play important roles

48. Consumer Strategies • Each strategy filters the list of available products. • The final choice is selected randomly from any remaining. • Available strategies • Cheapest – with a noise parameter on perceived difference. • Brand Loyalty – choose the brand it has bought most in the past. • Threshold – selects products below its predefined maximum price. • Change of pace – will swap brand after a number of weeks, defined by a parameter.

49. Assessment • To evaluate runs, • The difference between the actual monthly sales for each brand and the simulated sales data is calculated. • Each brand’s overall fitness is the sum of all the monthly differences. • And the model’s fitness is the sum of all the brands’ overall fitnesses. • Quite crude, but we are looking for a rough fit. • This is then used to assess the fit of models under different parameters settings.

50. Parameter Exploration • Two aspects of tuning, parameters and strategies. • Even with this simple model there are a lot of possible variations – 61,440! • A subgroup of 17 strategy combinations was identified. • So two sweeps were done. • A broad sweep to identify best strategy combinations and parameters. • A fine tuning sweep to identify the best parameter settings for these strategies.