22 visual displays

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Contents

Introduction.. 4

Research Process...5

Customer Satisfaction/Unmet Needs...8

Competitive Analysis.. 15

Win/Loss Research.19

Brand Awareness & Equity 22

Brand Position........ 27

Segmentation...... 33

About the Authors... 37

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Introduction

A picture is worth a thousand words.

A graphic is worth more.

Understanding topical relationships between cause and effects is a vital marketingresearch reporting requisite. A good researcher presents clear, actionable results, thebest approaches to the marketing challenge the client must solvethe answer. Oftenthe most effective method for conveying these results are visual.

Marketing research graphics can deliver insights into almost all marketing strategiesand activities. In some cases, standard pie charts and bar graphs are sufficient. But inour experience, such standard visual displays are overused and simply dont tellenough of a story to have impact. To really convey the so what results from aprimary research study, something more is needed.

This volume is a concise overview intended to showcase compelling marketing research

visuals that assist in the design and delivery of impactful results. It strives to share amix of direct analytic results and synthesis-oriented displays; an array of options foruse by students, product managers, C-suite executives, marketing and marketingresearch professionals.

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RESEARCH PROCESS

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All results are hypothetical and for illustration purposes only.

Explaining Research Options to Audience

Use: Raises awareness ofresearch options among groups

new to market research. Oftenused during project planning

meetings to help setexpectations and explain trade-

offs.

6

Suitable for Extrapolation

Immedia

cy

Low

Low High

High

Social Network Monitoring

Customer Advisory Councils

Focus Groups

In-depth Interviews

Quantitative Surveys


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Agency Selection Weighted Scorecard

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Criteria Weight AgencyA AgencyBFee 10 3 30 5 50

Creative

approach

10 5 50 3 30

Presenta-

tion skills

30 4 120 2 60

References 10 4 40 3 30

Cultural fit 20 5 100 4 80

Timelinecommit-

ment

20 2 40 5 100

Total 100 380 350

Use: Aids in agency selectionprocess by getting agreement

on selection criteria and

weights. Agencies are then

judged on criteria, and weightsapplied, to reach a total score.In this example, Agency As

superior presentation skills area significant factor in making it

a better choice for the client.


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MATRIX Analysis

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Average Performance Scores

ImpactonSatisfaction/PurchaseIntent

WeakerImpact

Weaker Performance Stronger Performance

StrongerImpact Strengths

Low Priority Potential Advantages

Unmet NeedsThese are "target issues" to

improve brand equity. The Brand is

performing below average and

these attributes are im portant.

These are the "primary strengths" ofthe Brand.

These attributes are not crucial.

Immediate focus should be on brand

attributes.

Consumer concerns are being met,

though these attributes are not

important for brand equity. Potential

for resource misallocation.

Use: This visual displays a

customer value management(CVM) quadrant. By constructinga visual critical path the CVM canserve as an organizationsstrategic navigation. Attributesmigrate from CVM categories,

beginning as Unmet Needs andmigrating clockwise. The CVMmethod becomes a precisetechnique for assessing the roleof new product features,predicting how they will migrate,and provides a map of thestrategic directions of the productor corporate communication.


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Identify Unmet Needs Based on CurrentProduct Perceptions: Laptop Example

Use: Easily focuses audienceon areas of opportunity (where

importance is high andsatisfaction is low). Most often

used with quantitative data, butcan be used with qualitative if

clearly stated.

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Importance

Satisfa

ctionwithCurre

ntProduct

Low

Low High

High

Takes less desk space

To use on sofa

To take on planes

Looks cool

To loan to kids


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Battery life

Item is becoming MORE important tome

AllBrandsdothisEquallyWell

True

False True

False

2 inch+ LCD

Storage capacity

20x zoom

9+ Megapixels

Identify Emerging Needs and Current Brand

Perceptions: Digital Camera Example

Use: Identifies areas ofemerging opportunity based on

potential for brand

differentiation and perceivedimportance. Helps audience

quickly see key results. Most

often used with quantitative

data, but can be used withqualitative if clearly stated.

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Key Drivers Map Example for BrandLoyalty

Use: Identifies areas of needed

improvement by displaying resultsfor sources of hypothesizedcustomer disloyalty. For example,in this case, lack of productattribute Z is observed by manyclients, but has little impact on

loyalty. In contrast, competitorscool packaging is widely

observed by clients and has astrong impact. This display helpsaudience quickly see areas foraction. Works well with non-technical audiences.

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Frequency of Perception

ImpactonCustom

erLoyalty

Lo

w

Low High

High


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Customer Satisfaction Results

Use: Summarizes items drivingand deterring customer loyalty.

Useful for non-technicalaudiences seeking key take-

aways without a lot of details.

Blue arrows at bottom indicatethe dependent variables used in

the data analysis. Supportingdata would detail relative

strength of each item.

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Propensity to spread positive word of mouth

$ Value of average purchase

Call center wait timeexceeds3 minutes

Support requests unresolved

Product interface perceivedas complex

Drives Disloyal Behaviors

Call center wait time lessthan 3 minutes

Support requests resolvedwithin 30 minutes

Product installed andoperational in


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Customer Loyalty Drivers by Stage

Use: For Customer Satisfaction orLoyalty studies that identify thosevariables driving positive scores bystage. Often useful in organizationsthat want to assign performancegoals for specific functional areas.

In this case, the areas of focus arepre-sales, sales and productdevelopment (deployment of theproduct has implications forproduct design). A usefulManagement Summary display,

typically supported by details in thebody of a full report. This exampleis B2B, but can be used forconsumer data as well.

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Engineering services

Customer perception Stands behind its products

Pre-sales

Account manager expertise

Proposal content

Financing options

Sales

Ease of integration

TCO

Deployment


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COMPETITIVE ANALYSIS

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Competitive Issue Targeting

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SecondaryWeaknesses

ImpactonLoyalty

Stronger

Weaker

Brand parityBrand underperformance

Attribute performance BetterWorse

Low PriorityIssues

Brand performs better thancompetition

Critical Weaknesses Parity IssuesLeverageable

Strengths Use: Compares client brandstrength versus a competitor on

attributes that drive brand

loyalty. Often used duringCustomer Satisfaction andProduct Positioning studies.

This graphic explains the sixLoyalty quadrants attributes can

be placed into.


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BioTech Difference with Competitors:Company Characteristics

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BioTech Higher Than BothCompetitors

Arnold Higher, InnoPharma

Lower than BioTech

InnoPharma Higher, ArnoldLower than BioTech

BioTechLower than BothCompetitors

Ample availabilityof inhalers at pharmacy

KeyVulnerabilities

StrategicAdvantages

PotentialVulnerabilities

PotentialAdvantages

Offers asthma productswhich are a good value

for the money

Provides samples of bronchodilatorsdelivery devices

Offers easy-to-use asthma

administration devicesProvides samples ofbronchodilators

Is dedicated tothe treatment of asthma

Regularly conducts clinicaloutcomes studies regardingasthma

CommunicationResource Priorities

Use: An at-a-glancesummary of brand

strengths vis--vis key

competitors, this quadrant

map is a guide to wherethe client company,BioTech, needs to allocate

its communicationresources.


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Classic Venn Diagram

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2009Favorableto Brand A Favorableto Brand B34%

2006Favorableto Brand A Favorableto Brand B12% Use: Classic Venn-style

diagram. Shows how groups

overlap, in this case to showthat over time, an increasing

percent of customers are

favorable to both competing

brands (suggesting that the

brands may be losing perceivedcompetitive differentiators).


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WIN/LOSS RESEARCH

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Win/Loss Research Display Use: Clearly profiles win

causes versus loss causes.

For example, in this case theclients wins are most often

driven by Factor A (evident in

60% of Wins) and Factor B(evident in 25%). Other

miscellaneous win factors exist,but were fragmented (and thusare not on the chart). Incontrast, some loss factors

overlap (they often occur in thesame accounts,) so those

percents add up to more than

100.

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Factor A 60%

Factor B 25%

WinFactor E 50%

Factor F 30%

Factor G 30%

Loss


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Loss Factors Outweighing Win Factors

Use: Summarizes key factorsidentified in a Win/Loss study.

Illustrates a case where threefactors driving losses are

outweighing two other factors

that drive wins. Of course,other variations may exist. Forexample, in some cases, two

strong Win factors couldoutweigh two Loss factors.

Helps non-technical clientsfocus on key results.

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Win Loss


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BRAND AWARENESS &EQUITY

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Summary for Brand AwarenessResearch

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Business Challenge:How to Allocate Brand Awareness Budgetby Geographic Market & Approach?

ResearchObjectives

ID in which of 10 geographicmarkets our brand awareness is


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Brand Equity: Regression Analysis

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Providing reasonablepayment schedule

(.32)

Providing promptpayments

(.27)

Providing fairpayment amounts

(.26)

Managing turnover(.30)

Likelihoodto RecommendProviding well

designed reports(.10)

Overall Quality

(.47)

Overall Quality ofPayment Process

(.16)

Efficient Data Query

Handling Process(.27)

Ability to train site staffin study protocol

(.22)

Having low turnover(.18)

Use: Identifies key drivers ofbrand equitythe why behind

the key measurement. Thisvisual regression chart is

common in Branding studies.

Works well with productmanagers and executives in the

C-suite.


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Is dermatologist recommendedMade with the "latest" ingredientsContains familiar ingredientsIts products are made from naturalingredients that are good f or you

Does not dry out skinCleans wellLeaves skin soft and smoothDoes not leave skin itchyIs for everyday use

SkincareBenefits

Product Bouquet

Genuine &Natural

SkincareIndulgence

Quality &Value

SkincarePurchase Intent

Skincare/BrandRating

RecommendSkincare

Skincare Components Latent MeasurementSkincare Attributes Performance

Structural Equation ModelSkincare Brand Equity

Skincare

Brand

Equity

Has products that are fun to useHas a long-lasting fragranceHas products that make you smell greatThe color of the product is natural

Is relaxingTurns my everyday shower into a fewspecial minutesHas a calming effectHelps keep my skin looking youngMakes a great giftIs a product I would be proud to display in mybathroomCosts a little more, but worth it

(.62)

(.08)

(.12)

(.48)

(.52)

(.80)

(.65)

(.77)


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BRAND POSITION

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Correspondence Analysis - Total

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Preferred for conservative therapyBiolin Mix Biolin

Insa-linInsa-lin Mix

Easy for patientto administer

Effective with oral

antidiabetes agentsEffective without weight gain

Duration of action

Consistent throughout day

Landis PharmaReduces risk of nocturnal

hypoglycemia

Mimics physiologicpatterns

Predictable day-to-day

Lowers HbA1c

Consistent per doseresponse

Reduces PPGlevels

Variety of delivery forms

Effective when mixed

Preferred for intensivetherapy

Biolog

Insalog

Trusted brand

Good value


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Correspondence Analysis - Total

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Use: This multidimensional correspondence map represents a snapshot of

the current market position of biotech products that includes measures ofattribute importance and brand market share.

Bubble location indicates relative association of brands to product attributes.Useful to think as the companies as planets and attributes as moons.

It is commonly used in Pharmaceutical and Consumer Package Good

studies.

Figures on the map are interpreted below.

Size of Attribute Bubble indicates combined stated and derived importance.

Size of Product Bubble indicates percentage of patients treated.

BioTech Pharmaceuticals products are highlighted in bright red.

Key Selling Point = High Stated/High Derived Importance.

Value-Added Benefit = Low Stated/High Derived Importance.

Essential Support Point = High Stated/Low Derived Importance.


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Multidimensional Scaling

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Main Causes/Main Effects of Weather Changes

Main Causes of Climate Change Main Effects of Climate Changes

Hole in the ozone layerToo much building/ constructionon flood plains

Deforestation in the UKPollution by industry

Cutting down of rain forest in South America and Asia

Pollution causedby the motor car

Greenhouse gas emissions/greenhouse effect

Lack of recycling ofhousehold waste

Overpopulation

Hotter summers Wetter winters

New diseases, e.g. malaria

Droughts

Floods

Changes in planning laws

Changes in agriculture Wetter summers

Warmer winters


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Multidimensional Scaling

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Main Causes/Main Effects of Weather Changes

Use: Multidimensional scaling (MDS) is a set of relatedstatistical techniques often used in information visualizationfor exploring similarities or dissimilarities in data. Our graphic

is an environmental cause and effect visual to show howpopulation and policies affect climate changes in the United

Kingdom. A multidimensional scaling map is based on

derived Euclidean distances; it does not have traditional x-y

axes as, say, a quadrant map. Rather, the graphic is

analyzed based on the proximity of causes to effects.


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SWOT Display

Use: Presents the top findingsfrom a SWOT study. Usuallysupported by drill-downs for

each quadrant. A clean display

used in presentations to focus

audience on most important

results.

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Low-costsubstitutes

Decline indiscretionaryspending

New saleschannels

Geographicexpansion

Time to Market

Productpackaging

High customersatisfaction

Superior product

reliability

Strengths Weaknesses

ThreatsOpportunities


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SEGMENTATION

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VOTE Overview

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TypicalInformation

Seekers

Impulse Voters

High-Stakes Gamblers

Consistency Seekers

Uncertainty Tolerance

DecisionDom

inance

Emotional

Low High

Rational

Candidate Analysts

Use: The Vote overview allows apolitical strategist to categorize whatmotives people to vote with fivesimple additions to the poll. Thesequestions lead to the segments in thegraphic.

I may not know a lot about a candidate

before I vote for him, but that is okay.

It would really bother me if I didn'tunderstand what the candidate stoodfor.

I vote for the candidate who is most inline with my core issues.

Image always determines who I votefor.

I don't have a problem changing myopinion about who to vote for.


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Multidimensional Preference Maps

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What do You Want Your Look to Say About You?

I take risks and push the limits

I love being a woman

I am feminine

I am sexy

I have an eye for fashion

I am sophisticated

I am professional

I am successfulI am conservative

I am fun and whimsical

Cheeky Chicks

Bourgeois

Fashion Mondaine

The Latest Woman

Use: Multidimensional preferenceanalysis is a visual factor analysis.

Proximity of personality attributesindicates group association relativeto others on the map. The length ofthe attribute vector indicates the

relative power of its impact on thegraph. For example, The Latest

Woman thinks of herself more

feminine than sexy.

Answers the question, What

attributes define a brand, orsegmentation group.

Deployed often in Fashion andPharmaceutical segmentations.


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About the Authors

Kathryn Korostoff

Over the past 20 years, Kathryn has personally

directed more than 600 primary market research

projects and published over 100 bylined articles

in trade magazines. Currently, Kathryn spends

her time assisting companies as they create

market research departments, develop market

research strategies, or otherwise optimize their

use of market research. Most recently, Kathryn

founded Research Rockstar, to provide clients

with easy access to market research training and

management tools. She can be reached at

[email protected]. Her Twitter

tag is @ResearchRocks.

Michael Lieberman

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Michael D. Lieberman has more than nineteen years

of experience as a researcher and statistician in the

marketing research field. He has worked extensively

with clients in financial services, information

technology, food service, telecommunications,financial services, political polling, public relations,

and advertising testing fields. H e founded

Multivariate Solutions in 1998 and now works with an

international clientele including advertising firms,

political strategy groups, and full service marketing

research companies. Michael has taught at CityUniversity of New York and is currently on the faculty

of the University of Georgia as an adjunct professor

of statistics and marketing research. He can be

reached at [email protected]. His Twitter tag

is @Statmaven.
mailto:[email protected]:[email protected]


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Thank you.

Any comments or

questions? [email protected]@mvsolution.com
mailto:[email protected]:[email protected]:[email protected]:[email protected]

22 visual displays

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