1. ME Basics–1 Marketing Engineering Basics G Introduction G Course Overview G Software Review

2. ME Basics–2 Daily Marketing Decisions Segmentation Targeting Positioning Budgets Marketing Mix Market size Market share Campaign effectiveness Pricing structure Portfolio Management Advertising design Sales channels

3. ME Basics–3 How Do Managers Make Marketing Decisions? G Intuition/judgment? G Strategic rationale? G Best practice benchmarks? G Internet search? G Consultant/Market Research results? G Sales force guesses? G Use decision models? G All of the above?

4. ME Basics–4 Introducing... Marketing Engineering G Course description and structure G What is marketing engineering? G Why learn marketing engineering? G Introduction to software G Introduce Conglom Promotions case

5. ME Basics–5 What’s Different About This Course? G Integrates marketing concepts with practice. G Emphasizes “learning by doing.” G It is a capstone course. G Provides you software tools to help you apply marketing concepts to real decision situations (even after you graduate!).

6. ME Basics–6 Takeaways G Gain an appreciation for the value of systematic marketing decision making. G Learn the language of high-powered marketing consultants -- i.e., how to put together analyses that tell a coherent story. G Understand how to critically evaluate analytical results presented to you by others -- i.e., become a good customer of analytical models. G Learn how successful companies have integrated marketing engineering within their organizations. G Develop skills to become a marketing engineer (i.e., to structure marketing problems and issues analytically using decision models).

7. ME Basics–7 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.

8. ME Basics–8 Marketing Engineering Marketing Environment Marketing Engineering Data Information Insights Decisions Implementation Automatic scanning, data entry, subjective interpretation Financial, human, and other organizational resources Judgment under uncertainty, eg., modeling, communication, introspection Decision model; mental model Database management, e.g.., selection, sorting, summarization, report generation

9. ME Basics–9 Trends Favoring Marketing Engineering G High-powered personal computers connected to networks are becoming ubiquitous. G The volume of marketing data is exploding. G Firms are re-engineering marketing for the information age.

10. ME Basics–10 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: G Verbal G Box and Arrow G Mathematical G Graphical

11. ME Basics–11 An Example of a Verbal 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.

12. ME Basics–12 Boxes and Arrows Model Fixed Population Size Imitators Timing of Purchases by Innovators Timing of Purchases by Imitators Pattern of Sales Growth of New Product Innovators Influence Imitators Innovators

13. ME Basics–13 Graphical Model Cumulative Sales of a Product Time Fixed Population Size

14. ME Basics–14 New York City’s Weather

15. ME Basics–15 Mathematical Model where: x t =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 dx t dt = (a + bx t )(N – x t )

16. ME Basics–16 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.

17. ME Basics–17 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?

18. ME Basics–18 Introduce if P(S) > 50%? S=Success (True) F=Failure (True) G=Good market research result P=Poor market research result. P(G|S)=0.80 (80/100) P(P|F)=0.80 (20/25) P(S)=0.15 P(F)=0.85 P(S|G)= P(G|S) P(S) P(G|S) P(S) + P(G|F) P(F) = 0.80  0.15 =41.3% 0.80  0.15 + 0.20  0.85

19. ME Basics–19 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

20. ME Basics–20 Models vs Intuition/Judgments Types ofSubjectiveObjective Judgments ExpertsMental Decision Decision Had to Make Model Model Model Academic performance of graduate students0.190.250.54 Life expectancy of cancer patients–0.010.130.35 Changes in stock prices0.230.290.80 Mental illness using personality tests0.280.310.46 Grades and attitudes in psychology course0.480.560.62 Business failures using financial ratios0.500.530.67 Students’ rating of teaching effectiveness0.350.560.91 Performance of life insurance salesman0.130.140.43 IQ scores using Roschach tests0.470.510.54 Mean (across many studies) 0.330.390.64

21. ME Basics–21 Applicant Profile (Academic performance of graduate students) Under- Appli- Personal Selectivity graduate College Work GMATGMAT cant Essay of Under- Major Grade Exper-VerbalQuanti- graduate Institution Avg. ience tative 1poorhighest science2.50 10 98% 60% 2excellentabove avg. business3.82 0 70% 80% 3averagebelow avg. other2.96 15 90% 80% 117weakleast business3.10100 98% 99% 118strongabove avg other3.44 60 68% 67% 119excellenthighest science2.16 5 85% 25% 120strongnot very business3.98 12 30% 58%

22. ME Basics–22 Small Models Example: Trial/Repeat Model Share=% Aware  % Available | Aware  % Try | Aware, Available  % Repeat | Try, Aware, Available  Usage Rate

23. ME Basics–23 Target Population Aware? Available? Try? Repeat? Market Share =? 50% 80% 40% 50% Trial/Repeat Model

24. ME Basics–24 Repeat Trial low hi lowhi  Model Diagnostics

25. ME Basics–25 Trial Dynamics % Population Trying (Trial) 100% Time You never get everyone to try

26. ME Basics–26 % Repeaters Among Triers (Repeat) 100% Time Note—late triers often do not become regular users  Repeat Dynamics

27. ME Basics–27 Fiona ‘the brand manager’ gets promoted Steve, her replacement, gets fired John, ‘the caretaker’, takes over Share = (Trial  Repeat) 100% = Share Dynamics! Time

28. ME Basics–28 New Phenomenon: Retail Outlet Management Sales/Outlet # Company Outlets in Market What People Observed What People Thought

29. ME Basics–29 Why? Typical outlet-share/market-share relationship Market Share Outlet Share 20406080100 20 40 60 80 100 Market Share = Outlet Share

30. ME Basics–30 Retail Building Implications 1.Market Share = Outlet Share ú Use incremental analysis and spread resources evenly. But 2.Market Share/Outlet Share is S-shaped ú u Concentrate in few areas u Invest or divest

31. ME Basics–31 Model Benefits G Small models can offer insight G Models can identify phenomena G Operational models can provide long-term benefits

32. ME Basics–32 More on Benefits of Decision Models G Improves consistency of decisions. G Allows you to explore more decision options. G Allows you to assess the relative impact of variables. G Facilitates group decision making. G (Most important) It updates your subjective mental model.

33. ME Basics–33 Why Don’t More Managers Use Decision Models? G Mental models are often good enough. G Models are incomplete. G Managers cannot typically observe the opportunity costs of their decisions. G Models require precision. G Models emphasize analysis; Managers prefer actions. G They haven’t been exposed to Marketing Engineering. All models are wrong. Some are useful!

34. ME Basics–34 Some Course Objectives G Gain an appreciation for the value of systematic marketing decision making. G Learn the language and tools of marketing consultants. G Learn how successful companies have integrated marketing engineering within their organizations. G Understand how to critically evaluate analytical results presented to you. G Develop skills to become a marketing engineer (ie, to structure marketing problems and issues analytically using decision models).

35. ME Basics–35 We Focus on End-User Models *Low for one-time studies High for models in continuous use End-User ModelsHigh-End Models Scale of problemSmall/MediumSmall/Large Time AvailabilityShortLong (for setting up model) Costs/BenefitsLow/MediumHigh User TrainingModerate/HighLow/Moderate Technical SkillsLow/ModerateHigh Recurrence of problemLowLow or High*

36. ME Basics–36 Marketing Engineering Software Excel ModelsNon-Excel Models Non-Excel Models by Commercial Vendors

37. ME Basics–37 Marketing Engineering Software G Excel Models Adbudg Advisor Assessor Callplan Choice-based segmentation Competitive advertising Competitive bidding Conglomerate, Inc. promotional analysis GE: Portfolio analysis Generalized Bass Model Learning curve pricing PIMS:Strategy model Promotional spending Analysis Sales resource allocation model Value-in-use pricing Visual response modeling Yield management for hotels

38. ME Basics–38 Marketing Engineering Software G Non-Excel Models ADCAD: Ad copy design Cluster Analysis Conjoint Analysis Multinomial logit analysis Positioning Analysis G Non-Excel Models by Commercial Vendors Analytic hierarchy process Decision tree analysis Geodemographic site planning Neural net for forecasting