marketing operations 01 course overview & introduction to ... · marketing operations....

© IfM 2017 1Marketing Operations, Prof. Dr. Manfred Krafft

Marketing Operations

01 Course Overview &

Introduction to Marketing Operations13.06.2017 – F1, Domplatz 20-22

Prof. Dr. Manfred KrafftSoSe2017 | Term 2

Teaching Assistant: M.Sc. Julian Allendorf, [email protected]

Office Hours: by appointment via E-Mail

© IfM 2017 Marketing Operations, Prof. Dr. Manfred Krafft 2

Marketing in the 4th semester

1st term 2nd term

Module Quantitative Marketing

Market Research Marketing Operations

© IfM 2017 3Marketing Operations, Prof. Dr. Manfred Krafft

Source: McKinsey&Company (2015), http://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/how-digital-marketing-operations-can-transform-business

© IfM 2017 Marketing Operations, Prof. Dr. Manfred Krafft 4

Research Priorities 2016-2018

Quantitative models to understand causality, levers, and influence in a complex world

Delivering integrated, real-time, relevant experiences in context

Making sense of changing decision process(es)

New data, new methods, and new skills – how to bring it all together?

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Innovation, design, and strategy in an age of disruption5

MSI Research Priorities

Source: Marketing Science Institute, http://www.msi.org/uploads/articles/MSI_RP16-18.pdf.

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Password for lecture material

Password for signing in on learnweb:

You can find the slides and additional materialin the download area for this lecture on learnweb.

5Marketing Operations, Prof. Dr. Manfred Krafft

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Course Schedule


Date Time Room Main TopicsTue, 13.06.2017 14-16 F1 01 Introduction

Tue, 20.06.2017 14-16 F1 02 Market Response Functions I

Wed, 21.06.2017 12-14 Aula am Aasee 03 Market Response Functions II

Tue, 27.06.2017 14-16 F1 04 Parameter Estimation

Wed, 28.06.2017 12-14 Aula am Aasee 05 Practice Case I

Fr, 30.06.2017 10-12 H1 Tutorial 1

Tue, 04.07.2017 14-16 F1 06 Solution Practice Case I; Practice Case II

Wed, 05.07.2017 12-14 Aula am Aasee 07 Methods of Optimization

Fr, 07.07.2017 10-12 H1 Guest Lecture: McKinsey & Company, Inc.

Tue, 11.07.2017 14-16 F1 Guest Lecture: Henkel AG & Co. KGaA

Wed, 12.07.2017 12-14 Aula am Aasee Tutorial 2

Fr, 14.07.2017 10-12 H1 Tutorial 3

Tue, 18.07.2017 14-16 F1 Tutorial 4

Wed, 19.07.2017 12-14 Aula am Aasee Guest Lecture: Interbrand GmbH

Fr, 21.07.2017 10-12 H1 08 Evaluation & Wrap-up

Fr, 28.07.2017 10-12 H1 Tutorial 5: Optional Q & A

Sa, 05.08.2017 09-10 tba Exam

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Title of exam: Marketing Operations

Duration: 60 minutes

Credit points: 3 ECTS

Relevant contents: All contents covered in the course material and sessions (also practice sessions and guest lectures)

Exam

8

The binding date of the exam will be announced by PrüfungsamtWirtschaftswissenschaften (PAM) exclusively.

Marketing Operations, Prof. Dr. Manfred Krafft

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How the weekly workload of this module may look like

Marketing Operations, Prof. Dr. Manfred Krafft 9

Thus, if you want to do well… organize your time and prioritize your activities.… prevent stress and disappointment by planning ahead NOW.… find a smart balance between work and extracurricular activities

such as part-time work and hobbies.

Reading before and after class

2 hours

Attendance of class and seminar

4,5 hours

Preparation for seminar and group work,

administration, etc.

2 hours

Preparation for examination

3 hours

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Chap. 3: Methods of optimizationAlbers, S. (1998): Regeln für die Allokation eines Marketing-Budgets auf Produkte oder Marktsegmente, Zeitschrift für betriebswirtschaftl. Forschung, Vol. 50, pp. 211- 235.

Kotler, P.; Keller, K. L.; Opresnik, M. O. (2015): Marketing-Management, 14th Ed., Pearson Studium, München, pp. 118-139.

Shankar, V. (2008). Strategic Allocation of Marketing Resources: Methods and Insights. In Kerin, R. A.; O’Regan, R. (Eds.): Marketing Mix Decisions: New Perspectives and Practices (p. 171), American Marketing Association, Chicago

Bibliography


A. Basic literature

Lilien, G. L.; Rangaswamy, A. (2004): Marketing Engineering: Computer-Assisted Marketing Analysis and Planning, Rev. 2nd Edition, Trafford, Victoria.

B. Basic literature concerning particular chapters

Chap. 1: Introduction to Marketing Operations

Ambler, T. (2003): Marketing and the Bottom Line, 2nd Ed., Pearson Education,London et al., p. 27.

Kotler, P.; Keller, K. L.; Opresnik, M. O. (2015): Marketing-Management, 14th Ed., Pearson Studium, München, p. 118.

Lilien, G. L.; Kotler, P.; Moorthy, K. S. (1992): Marketing Models, 2nd Ed., Prentice Hall, Englewood Cliffs, pp. 1-18.

Lilien, G. L.; Rangaswamy, A. (2004): Marketing Engineering: Computer-Assisted Marketing Analysis and Planning, Rev. 2nd Ed., Trafford, Victoria, chapter I, pp. 1-19.

Russo, J. E.; Shoemaker, P. J. H. (1989): Decision Traps, Doubleday and Company, N.Y., p. 137.

Kerin, R. A.; O’Regan, R. (2008): Marketing Mix Decisions: New Perspectives and Practices, American Marketing Association, Chicago

Chap. 2.1 and 2.2: Response functions

Albers, S. (2000): 30 Jahre deutschsprachige Marketing-Forschung im deutschen Raumzum quantitativ orientierten Marketing, in: Backhaus, K. (ed.): DeutschsprachigeMarketing-Forschung: Bestandsaufnahme und Perspektiven, Schäffer-Poeschel, Stuttgart, pp. 209-238.

Lilien, G. L.; Kotler, P.; Moorthy, K. S. (1992): Marketing Models, 2nd Ed., Prentice Hall, Englewood Cliffs, pp. 290, 661-675.

Lilien, G. L.; Rangaswamy, A. (2004): Marketing Engineering: Computer-Assisted Marketing Analysis and Planning, Rev. 2nd Ed., Trafford, Victoria, pp. 29-57.

Chap. 2.3: Parameter estimationAssmus, G.; Farley, J. U.; Lehmann, D. R. (1984): How Advertising Affects Sales: A Meta-Analysis of Econometric Results, Journal of Marketing Research, Vol. 21, pp. 65-74.

Farley, J. U.; Lehmann, D. R.; Sawyer, A. (1995): Empirical Marketing Generalization Using Meta-Analysis, Marketing Science, Vol. 14 (3), Part 2 of 2, pp. G36-G46.

Lilien, G. L.; Kotler, P.; Moorthy, K. S. (1992): Marketing Models, 2nd Ed., Prentice Hall, Englewood Cliffs, pp. 676-697.

Little, J. D. C. (1970): Models and Managers: The Concept of a Decision Calculus, Management Science, Vol. 16, pp. B466-B485.

Tellis, G. J. (1988): The Price Elasticity of Selective Demand: A Meta-Analysis of Econometric Models of Sales, Journal of Marketing Research, Vol. 25, pp. 331-341.

Basic literature for preparation and rework

L2 Additional literature

L3 Imparted in excerpts relevant contents

L1 Available in the literature folder(Heribert Meffert Library)

L4 General literature concerning the subject

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L4

L4

L2

L1

L4

L2

L4

L4

L4

Available on learnweb

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L1

L4

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Course objectives


• Gain an appreciation for the value of systematic marketing decision making.

• Learn the language and tools of marketing management.

• Learn how successful companies have integrated marketing engineering within their organization.

• Understand how to critically evaluate analytical results presented to you.

• Develop skills to become a marketing engineer (i.e., to structure marketing problems and issues analytically using decision models).

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Learning objectives – Lecture 1

• To get an overview of the challenges of today‘s operative marketers.

• To understand the importance of modeling in the discipline of marketing.

• To understand that models are used by all of us.

• To appreciate the usefulness of models and understand their potential as a source of competitive advantage.


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Help yourself: Make longhand notes

• Research shows that making longhand notes …

helps to recall complex relationships,

provides benefits in transferring knowledge to other contexts and

benefits a conscious analysis and thus, the cognitive processing of the subject matters …

… are significantly better than making notes by laptop.


Source: http://www.psychologicalscience.org/index.php/news/were-only-human/ink-on-paper-some-notes-on-note-taking.html

Source: istockphoto.com

Longhand notes lead to higher quality learning and are a superior strategy for storing new learning for

later study.

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Why marketing?


Source: Interbrand, http://de.slideshare.net/InterbrandLondon/applying-research-to-grow-your-brand.

A portfolio consisting of the 100 Best Global Brands has nearly always outperformed the MSCI World, as well as the S&P 500.

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• Increasing amounts of marketing data are available.

• Marketing Operations tools help to transform data into information (analysis).

• Information allows insights: causal structures, extent of influences, modeling under uncertainty.

• Insights allow better decision making and better implementation:

Increasing return on marketing (ROM)

Why Marketing Operations?


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Practice example 1: Walmart and 9/11

• Within a few hours of the 9/11 attacks, sales of flags and other patriotic items started skyrocketing.

• On Sept 11, the 2,700 Walmart stores sold over 100,000 flags (compared to 6,400 the previous year on that day), and over 200,000 on September 12th, 2001.

• Detecting these increases, Walmart locked up all the supplies it could find before its competitors (like Kmart) could react.

• Real-time tracking and analysis helped to cope with a demand surge.


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Practice example 2: Heinz

• Issue: Is the company getting the best efficiency and effectiveness on its promotional spending across US markets?

• Approach: Used market response analysis and ReAllocator (a marketing decision support system) to conclude that Heinz:

• had significantly different share positions/per capita spending across markets

• was misallocating spending and that the firm could substantially reduce overall spending without sacrificing national market share

• Results: Reduced promotional spending 40% and increased market share from 34% to 37%.


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Source: Based on an Accenture study with a sample of 400 companies worldwide (2005)/Tom Davenport.

High Lowperformers performers

65% Have significant decision-support/analytical capabilities 23%

36% Value analytical insights to a very large extent 8%

77% Have above average analytical capability within industry 33%

77% Have BI/Data Warehouse modules installed 62%

73% Make decisions based on electronically stored data and analysis 51%

40% Use analytics across their entire organization 23%


Example

Allegro

Top performing companies use analytics

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Overview: Chapter 1


1 Introduction to Marketing Operations

1.1 Introduction to modeling

1.2 Benefits, components and difficulties of marketing planning

1.3 Concepts of modeling – characteristics, objectives & typology

1.4 Management‘s resistance to models

1.5 Comparison of mental, subjective & objective models

1.6 Concluding remarks

1.7 Overview of the course contents

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Overview: Chapter 1










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You may have already used models without

realizing that you used one!

Consider the Boston Consulting Group’s

portfolio management matrix …

Fundamental Concepts

Introduction to models


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Mar

ket g

row

th ra

te

10x High 1x Low 0.1x

Relative market share(share relative to largest competitor)

?

Cashcow

Star

Dog

Questionmark

20%

High

10%

Low

0%

Boston Consulting Group growth-share matrix


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1.3 Concepts of modeling – characteristics, objectives and typology





Overview: Chapter 1


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Benefits of marketing planning:Marketing has to be made quantifiable


1. Support of objective and future-oriented thinking and acting

2. Coordination of decisions and actions in the marketing sector

3. Necessary for controlling and performance assessment of organizational units

4. Beneficial for communication and information of organization members concerning:

• objectives

• planned activities

• and essential resource management

• therefore, it represents the base for constructive criticism

5. Identification of opportunities and risks

6. Motivation of organizational members since income, career opportunities and reputation of the employees depend on the realization of the marketing objectives

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Difficulties of marketing planning


Cause: multiple stages, complexity, exogenous disturbances

You Me

Identification and prediction ofconsumer behavior

Motivation of distribution chainmembers to act in a specific way

Identification and predictionof competitor behavior

Forecast of the results of purchase negotiations

Problems: linking response to marketing actions(with regard to time and entity)

Supplier

Manufacturer

Retailer

Consumer

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Overview: Chapter 1


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• Reality is complex.

• Models are abstractions of reality.

• Abstraction means focusing on some central properties of the phenomenon and ignoring everything else.

• A model should be a more compact description of reality.

• Excessive detail confuses rather than illuminates.

How detailed should the model be?

What is a model?


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Modeling concepts

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What are models?

relationship to reality

Why do we use models?

explanation, prediction, optimization

How do we construct models?

modeling issues


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• Verbal• Boxes & arrows• Mathematical• Graphical• Individual vs. aggregate

A model is a stylized representation (i.e., abstraction) 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:

What is a model?


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Models

Explanation

Prediction

Optimization (normative/prescriptive)

Intended uses of a model


Bass Diffusion Model,Time-Series Models

ADBUDG,ADVISOR

Maslow’s Hierarchy of Needs

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unit sales

advertising ($)

Linear models give good fit over a relevant range but bad normative implications.

linear

Concave functions are used to find optimal advertising budgets.

concave(diminishing returns)

S-shape

Example: Budget planning


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Environmental forces

Example: Broad model of factors influencing consumers


Consumer

Marketing program

Technological forces

The above model would be OK for a general understanding of the consumer but would be insufficient for an in-depth analysis of consumer brand meanings, perceptions, etc.

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Consumer decision process

• Problem recognition• Information search• Alternative evaluation• Purchase decision• Post-purchase behavior

Marketing mix influences• Product• Price• Promotion• Place

Promotion influences• Purchase task• Social surroundings• Physical surroundings• Temporal effects• Antecedent states

Sociocultural influences• Personal influence• Reference groups• Family• Social class• Culture/Subculture

Psychological influences• Motivation• Personality/Lifestyle• Perception• Learning• Beliefs/Values/Attitudes

Source: Berkowitz, E.; Kerin, R. A.; Hartley, S.; Rudelius, W. (2011): Marketing, 10th edition, McGraw-Hill Companies, Inc., p.110.

Example: Detailed model of factors influencing consumers


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Example: Bass Diffusion Model (verbal)


Source: Lilien, G. L.; Rangaswamy, A. (2004): Marketing Engineering, 2nd ed., Trafford, Victoria, p. 11 f.

Description of a 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 salesgrowth. As more people in the population purchase the product, sales continue to increase but sales growth slows down.

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Timing of purchases byimitators

ImitatorsInnovators

Timing of purchases byinnovators

(Fixed) population size (N; Nt)

Innovatorsinfluenceimitators

Pattern of sales growthof new product

Source: Lilien, G. L.; Rangaswamy, A. (2004): Marketing Engineering, 2nd ed., Trafford, Victoria, p. 12.

Example: Bass Diffusion Model (boxes & arrows)


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cumulative number of adopters until t (Nt)

time t

absolute market potential (N)

Source: Adopted from Lilien, G. L.; Rangaswamy, A. (2004): Marketing Engineering, 2nd ed., Trafford, Victoria, p. 12.

Description: Bass Diffusion Model (graphical)


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Example: Bass Diffusion Model (mathematical)


𝑛𝑛𝑡𝑡 = �𝑁𝑁 − 𝑁𝑁𝑡𝑡 ∗ 𝑝𝑝 + 𝑞𝑞𝑁𝑁𝑡𝑡�𝑁𝑁

= 𝑝𝑝 ∗ �𝑁𝑁 − 𝑁𝑁𝑡𝑡 + 𝑞𝑞 ∗ �𝑁𝑁 − 𝑁𝑁𝑡𝑡 ∗𝑁𝑁𝑡𝑡�𝑁𝑁

nt = number of adopters at time t (sales at time t)

𝑝𝑝 = coefficient of innovation (external influence)

𝑞𝑞 = coefficient of imitation(“internal” to the society in which the diffusion spreads)

�𝑁𝑁 = customers who will eventually adopt the product(i.e., penetrable market potential)

𝑁𝑁𝑡𝑡 = cumulative number of adopters until time t (n0 + n1 + … + nt-1)

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Managers derive a spectrum of benefits from using decision models, leading ultimately to better decisions.

Source: Lilien, G. L.; Rangaswamy, A. (2004): Marketing Engineering, 2nd Ed., Trafford, Victoria, p. 13.

Benefits of marketing models


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Management vs. models (1)


• Managers sometimes fear and distrust models but everybody (including managers) uses models.

• Managers do not recognize their implicit or mental models.

• Market response modeling is useful because it can improve the effectiveness of decisions and make hidden assumptions explicit.

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Logical structures for the interpretation of experience

or

Beliefs about the network of causes and effects describing a system, along with the boundary of

the model (which variable to include and which to exclude) and the time horizon we consider relevant.

Decision makers always have (implicit) mental models

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Why don’t more managers use decision models?


• Mental models are often good enough.

• Models are incomplete.

• Usually managers cannot observe the opportunity costs of their decisions.

• Models require precision, mathematics and effort.

• Models emphasize analysis; managers prefer actions.

• Managers have not been exposed to Marketing Operations.

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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 outperform expert judgment.


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• University of Pennsylvania/Wharton • Massachusetts Institute of Technology • Penn State University • University of Texas at Dallas • UCLA• New York University • Northwestern • University of Michigan • University of California, Berkeley (CA) • University of Texas (Austin, TX) • Duke University (Durham) • University of Chicago

and many more …

• Australia (GSM, NSW, Sydney, …)• Austria (Vienna, …)• Canada (Laval, McGill, …)• China/Hong Kong• France (INSEAD, …)• Germany (Münster, WHU, …)• India/Indonesia/Pakistan• Netherlands (Erasmus, Groningen, …)• Spain (Barcelona, …)• Latin America

and many more …

USA Worldwide

Universities using „marketing engineering“ in teaching


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Big Data Experts – Closing the Gap

Master of Business Analytics (MBA)

Source: http://ibmexperts.computerwoche.de/a/data-scientist-werden-aber-wo,3207340.

“In today’s world, with its ever-increasing volume of data, businesses that can make

sense of the flow of information have a competitive edge, making graduates with the

computational, analytical and business skills to provide that edge highly sought after

by employers in Australia and globally.”

Industry demand for a skilled pool of graduates who can apply data science to solve business challenges

Programs have emerged offering degrees at undergraduate, graduate and doctoral levels

Source: University of Melbourne, https://mbs.edu/education-development/degreeprograms/mba/masterofbusinessanalytics.


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0% 20% 40% 60% 80% 100%

Academic performance of graduate students

Life expectancy of cancer patients

Changes in stock prices

Mental illness using personality tests

Grades and attitudes in psychology course

Business failures using financial ratios

Students' ratings of teaching effectiveness

Performance of life insurance salesman

IQ scores using Rorschach test

Mean (across many studies)

Mental ModelSubjective Decision ModelObjective Decision Model

Mental Model: expert predictionSubjective Model: linear model from past expert predictionsObjective Model: linear regression model from data

Objective Models outperform subjective and mental models by far!

Source: Russo, J. E.; Schoemaker, P. J. H. (1989): Decision Traps, Doubleday and Company, N.Y., p. 137.

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Comparison of different types of models


Degree of correlation with true outcomes

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Are „models“ the whole answer? No!


To evaluate any decision aid, you need a proper baseline!

1. Intuitive judgment does not have an impressive track record.

2. When driving at night with your headlights on, you do not necessarily see too well.

3. Decision aids do not guarantee perfect decisions.

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Focus

- Elasticities- Market response functions

- Extrapolating trends- Time series analysis

- Trade magazines- Reports- Market databases

- Supplier documentation- Prospects /potential customers- Industries- Technologies

- by products- by regions- by orders- by customer groups

- Address, contact person- Results of sales calls- Sales and profit statistics- Preferences

1. What does exist?CUSTOMER DATABASES

2. What will or can happen?

POTENTIAL

STATISTICAL PREDICTION

MARKET

CAUSAL PREDICTION

SALES STATISTICS

Types of information


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How do I find out if spending money for marketing is justified?

What specific models can I use to examine the effect of marketing expenditure on my business performance?

How do I find my specific response function?

What is the amount that I should spend on marketing?

How does this all help me in practice?

What questions does this course answer?


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Correlation or Causation?


Source: Messerli, F.H. (2012): Chocolate Consumption, Cognitive Function, and Nobel Laureates, The New England Journal of Medicine, pp. 1-3.

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Summary – Lecture 1

• Marketing modeling is an essential part of today’s marketing best practices.

• Everyone already uses (implicit) models.

• Reality is too complex to assess it intuitively.

• Using marketing models in general yields better results than managerial judgment.

• Choosing the right model depends on its purpose and data availability.

• Marketing models are nothing to be scared of.


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