Marketing Science 1

1. Marketing Science 1 University of Tsukuba, Grad. Sch. of Sys. and Info. Eng. Instructor: Fumiyo Kondo Room: 3F1131 kondo@sk.tsukuba.ac.jp

2. Introduction to Marketing Science • Course description and structure • What is marketing engineering? • Why learn marketing engineering? • Introduction to software • Introduce Conglom Promotions case

3. Marketing Engineering Basics • Introduction • Course Overview • Software Review

4. How Does This Course Differ from Other Marketing Courses? • Integrates marketing concepts and practice. • Emphasizes “learning by doing”. • Provides software tools to apply marketing concepts to real decision situations.

5. Transition of Marketing Definition • Age of No Need for Marketing • Mass Marketing that target all consumers • （Traditional）Segmentation Marketing • Concept of Exchange (Kotler) • One-to-One Marketing • Concept of Relationship

6. Definition of Segmentation Marketing • Concept of Exchange by Kotler（1976） Societal and managerial process.. Exchange .. Needs and wants of individuals and organizations • Marketing Management Facilitates proactively the exchange process viewed as a management philosophy for desirable exchanges Ability to understand customers and Markets

7. Recent Definition of Marketing by AMA (American Marketing Association) Marketing is • an organizational function and • a set of processes for creating, communicating, and delivering value to customers and for managingcustomer relationships in ways that benefit the organization and its stakeholders.

8. Marketing Engineering Marketing engineering is the art and science of developing and using interactive, customizable, computer-decision models for analyzing, planning, and implementing marketing tactics and strategies.

9. Trends FavoringMarketing Engineering • High-powered personal computers connected to networks are becoming ubiquitous. • The volume of marketing data is exploding. • Firms are re-engineering marketing for the information age.

10. Managers’ Typical Approachin Marketing Decision Making • Rely on experience and wisdom … based on mental models • Use practice standards • Alternative approach … based on decision models This course uses decision models

11. Strength and Weakness of Mental models • Psychologically comfortable with the decisions • Prone to systematic errors • Experience can be confounded with responsibility biases, for example, Sales managers ... lower advertising budgets & higher expenditures on personal selling Advertising managers ... larger advertising budget

12. Strength and Weakness of Practice of Standards • Good on average • Ignore idiosyncratic elements in decision context e.g., a new competitor enters the market with an aggressive advertising program, resulting in a decrease in the firm’s sales. • A fixed advertising-to-sales-ratiobased on practice of ｓｔａｂｄａｒｄｓ would prescribe a decrease in advertising. • Other reasonable mental model would suggest some form of retaliation based on increased advertising.

13. Conceptual Marketing vs. Marketing Engineering • Third approach … build a spreadsheet decision model called marketing engineering (ME) • First approach (mental model) referred to as conceptual marketing ME complements conceptual marketing.

14. Marketing Engineering Marketing Environment Automatic scanning, data entry, subjective interpretation Marketing Engineering Data Database management, e.g.., selection, sorting, summarization, report generation Information Decision model; mental model Insights Judgment under uncertainty, eg., modeling, communication, introspection Decisions Financial, human, and other organizational resources Implementation

15. Data are facts, beliefs, or observations used in making decisions. A common misconception is that decision models require objective data. • Information refers to summarized or categorized data. • Insights provide meaning to the data or information, and they help manager gain a better understanding of the decision situation. • A decision is a judgement favoring a particular insight as offering the most plausible explanation or favoring a particular course of action. (Decision provides purpose to information.) • Implimentation is the set of actions the manager or the organization takes to commit resources toward physically realizing a decision.

16. What is a Model? A model is a stylized representation of reality that is easier to deal with and explore for a specific purpose than reality itself. We will use the following types of models: • Verbal • Box and Arrow • Mathematical • Graphical

17. Stylized Models do not capture reality fully, but focus only on some aspects.

18. Representation A model is only a convenient analogy that may bear little resemblance to the physical characteristics of the reality it is trying to capture.

19. Specific purpose People develop models with a specific purpose in mind. The purpose of a marketing model could be to understand or influence certain types of behavior in the market place(e.g. repeat purchase of the firm’s product)

20. An Example of a Verbal Model- Example of Diffusion Model - Sales of a new product often start slowly as “innovators” in the population adopt the product. The innovators influence “imitators,” leading to accelerated sales growth. As more people in the population purchase the product, sales continue to increase but sales growth slows down.

21. Boxes and Arrows Model Fixed Population Size Imitators Innovators Innovators Influence Imitators Timing of Purchases by Imitators Timing of Purchases by Innovators Pattern of Sales Growth of New Product

22. Graphical Model Fixed Population Size Cumulative Sales of a Product Time

23. New York City’s Weather

24. dxt = (a + bxt)(N –xt) dt Mathematical Model where: xt = Total number of people who have adopted product by time t N = Population size a,b = Constants to be determined. The actual path of the curve will depend on these constants

25. Are Models Valuable? Belief: ‘No mechanical prediction method can possibly capture the complicated cues and patterns humans use for prediction.’ Hard Fact: A host of studies in medical diagnosis, loan granting, auditing and production scheduling have shown that even simple models out-perform expert judgement. Example: Bowman and Kunreuther showed that simple models based on managers’ past behaviour, (in terms of production scheduling and inventory decisions) out-perform the managers themselves in the future.

26. How Good are You at Interpreting Market Research Information? Your firm has had the following record over the last 5 years: 85 of 100 new product developments failed. Lilien Modelling Associates (LMA) did a $50,000 study on your new product, Sheila Aftershave, and reports ‘Success’! LMA’s record is pretty good: of the 125 field studies it has done, it had 80/100 accurate ‘success’ calls (80%) 20/25 accurate ‘failure’ calls (‘I told you so’) also 80%. If you should introduce Sheila if P(S) > 50% and LMA says “success”, should you introduce?

27. Are ‘Models’ the Whole Answer? No! The widespread availability of statistical packages has put mathematical bazookas in the hands of those who would be dangerous with an abacus. —Barnett To evaluate any decision aid, you need a proper baseline. 1. Intuitive judgement does not have an impressive track record. 2. When driving at night with your headlights on you do not necessarily see too well. But turning them off will not improve the situation. 3. ‘Decision aids do not guarantee perfect decisions but when appropriately used they will yield better decisions on average than intuition.’ —Hogarth, p.199

28. Models vs Intuition/Judgments Types of Subjective Objective Judgments Experts Mental Decision Decision Had to Make Model Model Model Academic performance of graduate students 0.19 0.25 0.54 Life expectancy of cancer patients –0.01 0.13 0.35 Changes in stock prices 0.23 0.29 0.80 Mental illness using personality tests 0.28 0.31 0.46 Grades and attitudes in psychology course 0.48 0.56 0.62 Business failures using financial ratios 0.50 0.53 0.67 Students’ rating of teaching effectiveness 0.35 0.56 0.91 Performance of life insurance salesman 0.13 0.14 0.43 IQ scores using Roschach tests 0.47 0.51 0.54 Mean (across many studies)0.33 0.39 0.64

29. Applicant Profile(Academic performance of graduate students) Under- Appli- Personal Selectivity graduate College Work GMAT GMAT cant Essay of Under- Major Grade Exper- Verbal Quanti- graduate Institution Avg. ience tative 1 poor highest science 2.50 10 98% 60% 2 excellent above avg. business 3.82 0 70% 80% 3 average below avg. other 2.96 15 90% 80% • • • • • • • • • • • • • • • • 117 weak least business 3.10 100 98% 99% 118 strong above avg other 3.44 60 68% 67% 119 excellent highest science 2.16 5 85% 25% 120 strong not very business 3.98 12 30% 58%

30. Small Models Example:Trial/Repeat Model Share = % Aware × % Available | Aware × % Try | Aware, Available × % Repeat | Try, Aware, Available × Usage Rate

31. 50% 80% 40% 50% Trial/Repeat Model Target Population Aware? Available? Try? Repeat? Market Share = ?

32. Trial hi low J hi Repeat low Model Diagnostics L

33. Trial Dynamics You never get everyone to try 100% % Population Trying (Trial) Time

34. ×Repeat Dynamics 100% Note—late triers often do not become regular users % Repeaters Among Triers (Repeat) Time

35. = Share Dynamics! Fiona ‘the brand manager’ gets promoted 100% Steve, her replacement, gets fired Share = (Trial ×Repeat) John, ‘the caretaker’, takes over Time

36. What People Observed Sales/Outlet What People Thought # Company Outlets in Market New Phenomenon:Retail Outlet Management

37. Why? 100 80 Market Share= Outlet Share 60 Market Share 40 20 20 40 60 80 100 Outlet Share Typical outlet-share/market-share relationship

38. Retail Building Implications • Market Share = Outlet Share Use incremental analysis and spread resources evenly. But 2. Market Share/Outlet Share is S-shaped • Concentrate in few areas • Invest or divest

39. Model Benefits • Small models can offer insight • Models can identify phenomena • Operational models can provide long-term benefits

40. More on Benefits ofDecision Models • Improves consistency of decisions. • Allows you to explore more decision options. • Allows you to assess the relative impact of variables. • Facilitates group decision making. • (Most important) It updates your subjective mental model.

41. Value of Models

42. Why Don’t More ManagersUse Decision Models? • Mental models are often good enough. • Models are incomplete. • Managers cannot typically observe the opportunity costs of their decisions. • Models require precision. • Models emphasize analysis; Managers prefer actions. • They haven’t been exposed to Marketing Engineering. All models are wrong. Some are useful!

43. Some Course Objectives • Gain an appreciation for the value of systematic marketing decision making. • Learn the language and tools of marketing consultants. • Learn how successful companies have integrated marketing engineering within their organizations. • Understand how to critically evaluate analytical results presented to you. • Develop skills to become a marketing engineer (ie, to structure marketing problems and issues analytically using decision models).

44. We Focus on End-User Models End-User Models High-End Models Scale of problem Small/Medium Small/Large Time Availability Short Long (for setting up model) Costs/Benefits Low/Medium High User Training Moderate/High Low/Moderate Technical Skills Low/Moderate High Recurrence of problem Low Low or High* * Low for one-time studies High for models in continuous use

45. Marketing Engineering Software Non-Excel Models by Commercial Vendors Excel Models Non-Excel Models

46. Excel Models AdbudgAdvisorAssessorCallplanChoice-based segmentationCompetitive advertisingCompetitive biddingConglomerate, Inc. promotional analysisGE: Portfolio analysis Generalized Bass ModelLearning curve pricingPIMS:Strategy modelPromotional spending AnalysisSales resource allocation modelValue-in-use pricingVisual response modelingYield management for hotels Marketing Engineering Software

47. Non-Excel Models ADCAD: Ad copy designCluster AnalysisConjoint AnalysisMultinomial logit analysisPositioning Analysis Non-Excel Models by Commercial Vendors Analytic hierarchy processDecision tree analysisGeodemographic site planningNeural net for forecasting Marketing Engineering Software