market intelligence session 1 course introduction, backward market research, secondary data, measure...

Market IntelligenceSession 1

Course Introduction, Backward Market Research, Secondary Data, Measure Types, Crosstabs

Today’s Agenda

• Course Overview: The Big Picture• Introduction to Backward Market Research• Secondary data• Measure types• Crosstabs • Set up Luna Beer case• Some course details

Today’s Agenda

• Course Overview: The Big Picture• Introduction to Backward Market Research• Secondary data• Measure types• Crosstabs• Set up Luna Beer case• Some course details

John Doerr

“There are basically four risks we have to confront in each deal. There is technical risk: Can we split the atom? There is people risk: Will the key players on the team stay together? There is financial risk: Can we keep the company well financed? And there is market risk: Can we get the dogs to eat the dog food? The most dangerous of these risks is market risk. Removing market risk is expensive … we’re risk takers but we will take a technology risk over a market risk any day of the week.”

(Perkins & Perkins, 1999, The Internet Bubble, p. 74)

Segway Scooters

• Hyped in 2001 by Jeff Bezos, Steve Jobs, John Doerr

• Jobs: cities would be re-architected to leverage

• Doerr: Segway would make its first billion dollars faster than any company in history

• Segway released in 2002• By the summer of 2004, less than

10,000 units had. 30,000 units through 2007.

Market Risk

• Market risk = demand uncertainty• Marketers and managers can make better

decisions when uncertainty is reduced • It is reduced through market intelligence

• Mature product– US Sales of Wine 2008 – 2013

• Innovative Product– iPad

Wine – Mintel report

Prediction for 2009 ?

Prediction for 2015 ?

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Wine – Mintel report

2009 2010 2011 2012 2013 2014 2015 2016

Apple iPad

• Pre-launch demand uncertainty• Jan 21, 2010 WSJ– “Apple’s tablet foray faces several obstacles. Analysts say

demand will depend on its price, which some believe to be about $1,000. Apple must also convince consumers the product is worth buying in addition to an iPhone and a laptop computer. And Apple faces competition from cheaper netbooks and other devices such as Amazon.com Inc.’s Kindle e-book reader.”

Jan 22, 2010, FastCompany.com

“About 20% of US Consumers Would Buy Apple’s Tablet”

Initial reaction?

Jan 22, 2010, FastCompany.com

“About 20% of US Consumers Would Buy Apple’s Tablet”

– “A survey of over 3,300 U.S. consumers has shown nearly 20% of them would buy one. Its success looks assured.”

– Purchase intent question: “4% of the responders said they were “very likely” to buy such a device, with 14% more labeling themselves as “somewhat likely”. That means close to 20% of the US consumers surveyed would be interested in buying Apple’s machine”

June 2010, Forrester Research

• Forrester Research, June 2010– Low estimate for demand: 1.2 million iPads in 2010.– “Consumers didn’t ask for tablets. In fact, Forrester’s data

shows that the top features consumers say they want in a PC are a complete mismatch with the features of the iPad.”

When/What Do You Ask Customers?

• Dr. Land (inventor of polaroid) said, "I didn't invent the polaroid camera, it's always existed, just waiting to be discovered."

• Steve replied, "That's right. I knew long before we built it exactly what the Mac was. It always existed. I never had to ask customers what they wanted. If it's something truly revolutionary, they won't be able to help you.”

– Steve Jobs by Walter Isaacson

“If I had asked people what they wanted, they would have said faster horses.”

-Henry Ford

Being Customer Led• Contrast with Chrysler / Ford minivan

– “Ford lacked confidence that a market existed, because the product didn’t exist. The auto industry places great value on historical studies of market segments. Well, we couldn’t prove there was a market for the minivan because there was no historical segment to cite…never got a letter from a housewife asking for a minivan.”

– Being customer led - listen to customers, but understand the limitations of what you are getting in some environments

Market Intelligence: 3 Skills

• Backward market research• Getting data & judging its quality• Tools for classic marketing problems

Market Intelligence: 3 Skills

• Backward market research: – Start at the end of the process: what decision will this

intelligence inform? move backwards from there

• Getting data & judging its quality• Tools for classic marketing problems

3 Key Skills

• Backward market research• Getting data and judging its quality– Secondary data– Exploratory research– Descriptive research– Causal research

• Tools for classic marketing problems

3 Key Skills

• Backward market research• Getting data and judging its quality• Tools (analysis frameworks) for classic marketing

problems– Perceptual maps– Conjoint analysis for new product forecasting– Segmentation– Promotion Analysis– Database Marketing– Simulated Test Markets

Market Intelligence – Focus on Decisions Relating to …

Customer Company Competition

Market Segmentation

Target Market Selection

Product and Service

Positioning

Marketing Mix

Product & Service

Place and Channel

Promotion

Creating Value

Capturing Value

Customer Acquisition

Customer Retention

Profits

Sustaining Value

Course Positioning

• For users of market intelligence in marketing management, entrepreneurship, consulting– help you become a more sophisticated user by assuming

role of research provider and by providing practice as evaluator of research

Course Positioning

• There is a quantitative component to this class, especially second half of course

• But… we will not go deeply into math behind the tools – goal is to give you basic working understanding of the tools, not to make you an expert in them

• For some tools (conjoint, simulated test markets, perceptual maps), I will show you simplified version to give you basic intuition. You will not learn how to do a complex or sophisticated analysis on your own

Today’s Agenda

BMR Reading

• Take aways?

Backward Market Research • Imagine the end of the process: – What decision alternatives might be implemented?– What analyses will help you make decision?– What info/data do you need to run this analysis?

• Where to get the data for analysis?– Do they already exist? – If not, may need to commission a study.

• Design the study (“need-” vs. “nice-to-know”)• Analyze data & make recommendation

Today’s Agenda

Primary vs. Secondary Data

• Primary –

• Secondary –

collected anew for current purposes

exists already, was collected for some other purpose

Secondary Data: Pros & Cons

• Advantages

• Disadvantages

• Advantages– cheap– quick– often sufficient– usually reputable firms– access to larger samples or hard-to-reach populations

• Disadvantages– there is a lot of data out there– numbers sometimes conflict – categories may not fit your needs– everyone has access to it (including competitors)

Types of Secondary Data

*SymphonyIRI = (http://symphonyiri.com/)

Secondary Data Quality: “What’s Behind the Numbers?” by Exter

• Are Data consistent with other independent sources?• What are the classifications? Do they fit needs?• When were numbers collected? Obsolete?• Who collected the numbers? – Expertise and resources?– Bias?

• Why were the data collected? Self-interest?• How were the numbers generated? – Sample size– Sampling method– Measure type

• Correlation does not mean causation!

Correlation and CausalityCorrelation between X and Y, X Causes Y , or …

…or a third variable could cause both X and Y

“Turn off the tube”, says the American Academy of Pediatricians. “Watching TV causes obesity.” A study conducted for the society found that 4th graders who watched more than 25 hours of television a week weighed, on average, eleven pounds more than fourth graders who watched less than 25 hours. The academy advised parents to severely restrict their children’s television diets.

“TV watching causes obesity”

Don’t live together if you want to stay married. So says a nationwide study of over 2000 couples. The study found that couples who had lived together before getting married were 2.3 times as likely to get divorced as couples who had not lived together.

“Living together leads to divorce”

Today’s Agenda

Measure Types• Nominal: Unordered Categories

• Ordinal: Ordered Categories, intervals can’t be assumed to be equal.

• Interval: Equally spaced categories, 0 is arbitrary and units arbitrary.

• Ratio: Equally spaced categories, 0 on scale means 0 of underlying quantity.

Measure typesExamples Meaningful stats

nominal -Items placed in buckets frequencies-Lots of demographics mode(e.g., race, gender)

ordinal -rank orderings frequencies

-Uneven ranges (e.g., 1-18 yrs, mode19-30 yrs, 31-50 yrs, 51-100 yrs) median

interval -Likert scales (agree-disagree) frequencies

-Rating scales (e.g., -3 to +3, mode1-5, 1-7, 1-20 scale) median

mean ratio -# items sold mode

-$ sales medianmean% change

From National Insurance

1. What Measure Type? Meaningful statistics?

From National Insurance3. What Measure Type? Meaningful statistics?

1. What Measure Type?

1. What Measure Type?Ordinal• Frequencies• Mode• Median

Nominal• Frequencies• Mode

From National Insurance3. What Measure Type?

From National Insurance3. What Measure Type? Ratio

• Frequencies• Mode• Median• Mean• Relative %

From National Insurance4. What Measure Type? Interval

• Frequencies• Mode• Median• Mean

Measurement Example

• For a student project, three students prepared a survey comparing two dishwashing detergents (Sheen and Glitter) on how rough or mild the detergent was on their hands.*

• The students developed the following question and scale and administered the survey to 10 respondents for both detergents. The scale used to rate each detergent is below:

* This example is adapted from Churchill and Iacobucci (2009)

Measurement Example

• The survey responses are listed below:

Measurement Example• Students decided to work individually to code and analyze the

responses and then get back together. When they got back together, each of three suggested a different coding scheme.

• What is your initial reaction to these scales?

Measurement Example• When the data are coded using each of the three scales, the

following mean ratings result (in red below under S and G) . • Based on this, which student is right?

S G1 0.8

2 3.8 4.2

3 2.2 1.8

Measurement Example• The students each calculated the comparative mildness

between the detergents as shown below.• Which student is right?

* Reverse coded – milder rating is low.

Sheen GlitterHow much milder is glitter than sheen?

Means for Ratio Scale Data:Per Capita

Ratio Scales & Index Numbers

Note: this is weighted average, so calculate using bottom row

Today’s Agenda

TWO VARIABLES: TABLING CONTINGENCY DATA

A. Raw Frequencies

School X: Gender x Acceptance

Is there Gender Discrimination Going On Here?

TWO VARIABLES: TABLING CONTINGENCY DATA

A. Raw Frequencies B. Cell Percentages

School X: Gender x Acceptance

Is there Gender Discrimination Going On Here?

C. Row Percentages

D. Column Percentages

Rule of Thumb

• If a potential causal interpretation exists, make numbers add up to 100% at each level of the causal factor.

• In the example above: it is possible that gender (row) causes or influences acceptance (column), but not that acceptance influences gender. Hence, row percentages (format C) would be desirable.

Today’s Agenda

Luna Beer Objectives

• Feasibility decisions• Problem formulation, information needs• Role of secondary data• Role of research and time budgets

Luna Beer Questions: Using BMR

• What decision are we making and what is action standard– decision: go/no go– action standard: break even in year 1

• What information do we need (to plug into formula) to make decision?

• Which reports allow that information to be estimated?

• What decision do these reports suggest?

Prepping Luna Beer– Buy research from the tool on Platform (by tues, Jan 20,

8pm)– Only 1 member of each team can purchase reports– Submit 2-slides (via Platform)– Labeling your file: include section #, team #, assignment

name, and Last names in alphabetical order (e.g., 302.8.Luna.Alster,Brown,Casta,Jones,Smith)

– Hints:• Choose only “need to know” info at lowest cost• Each person bring a copy of your team’s slides to class in case you

are called on to present

Today’s Agenda

• Randomly assigned on team builder• Memorize your Team #!!

Evaluation

• 3 quizzes (20% each), dates in syllabus– Quiz 1 – Quiz 2 – Quiz 3 during final exam– All 1 hour long, non-cumulative

• National Insurance case, done individually (5% total)• Team PPT case submissions (20% total)

– Best 6 of 7 will count @ 3.33% each– 3 TAs will grade each submission on mastery of material and effort

put in, and will be averaged• Class participation (15%)

– Quality more important than quantity– Individual submissions will be graded on check/no check basis and

will be factored into participation grade

Assignments

Guest Speakers

Clint McClain, WalmartGeoff Tanner, Big Heart Pet BrandsKevin Clark (formerly IBM)Caroline Klompmaker, Burt’s Bees

Also Peggy Liu: Qualtrics tutorialDanielle Brick: SPSS tutorial

TAs for Market IntelligenceHead TA: Paul Escadajillo• Rafael Bitencourt• Alison Caldwell• Jose Ceballos• Nikhil Cheruku• Steven Chung• Nicholas Djokic• Paul Escajadillo• Thomas Goellner• Matthew Harris• Sherin Kurian• Sofia Martinez• Trevor McKinnon• Adrian Meyer• Kellie O'Connor

• Charlene Ondak• Raghunath Prabhu• Eric Vandenbrink• Adam Weintrob•

PhD Student TAs:• SPSS expert: Danielle Brick (

Danielle.Brick@duke.edu)• Qualtrics expert: Peggy Liu

(Peggy.liu@duke.edu)

For Next Time…

• Luna Beer Case• SPSS self-paced tutorial: National Insurance

Case– Prepare by reading National Insurance case and

SPSS tutorial handout.

• Advantages– cheap– quick– often sufficient– usually reputable firms– access to larger samples or hard-to-reach populations

• Disadvantages– there is a lot of data out there– numbers sometimes conflict – categories may not fit your needs– everyone has access to it (including competitors)

Secondary Data Quality: “What’s Behind the Numbers?” by Exter

• Are Data consistent with other independent sources?• What are the classifications? Do they fit needs?• When were numbers collected? Obsolete?• Who collected the numbers? – Expertise and resources?– Bias?

• Why were the data collected? Self-interest?• How were the numbers generated? – Sample size– Sampling method– Measure type

Ratio Scales & Index Numbers

Note: this is weighted average, so calculate using bottom row

• What Measure Type?Ordinal• Frequencies• Mode• Median

• What Measure Type?

Nominal• Frequencies• Mode

From National Insurance• What Measure Type? Ratio

• Frequencies• Mode• Median• Mean• Relative %

From National Insurance• What Measure Type? Interval

• Frequencies• Mode• Median• Mean

market intelligence session 1 course introduction, backward market research, secondary data, measure...

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