Uploaded byMichael Lieberman
22 Visual Displays Of Marketing Research Insights
This document provides 22 visual displays that can be used to summarize marketing research insights. It begins with an introduction explaining that visuals are an effective way to convey research findings and insights. It then presents visual examples organized into the following sections: research process, customer satisfaction/unmet needs, competitive analysis, win/loss research, brand awareness & equity, brand position, and segmentation. Each visual is accompanied by a brief description of its purpose and use. The document concludes with information about the authors.
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22 Visual Displays Of Marketing Research Insights
- 1. Marketing ResearchInsights22 VisualDisplaysby Kathryn Korostoffand Michael Lieberman
- 2. © 2009 byKathryn Korostoff & Michael LiebermanCopyright holder is licensing this under the Creative Commons License, Attribution 3.0.http://creativecommons.org/licenses/by/3.0/us/Please feel free to post this on your blog or email it to whomeveryou believe would benefit from reading it. Thank you.2
- 3. ContentsPageIntroduction………………………………4Research Process…………………………..5Customer Satisfaction/UnmetNeeds………..8Competitive Analysis……………………...15Win/Loss Research………………………..19Brand Awareness & Equity……………..….22Brand Position……………………...….…27Segmentation…………………………....33About the Authors…………………….…373
- 4. IntroductionA picture isworth a thousand words.A graphic is worth more.Understanding topical relationships between cause and effects is a vital marketing research reporting requisite. A good researcher presents clear, actionable results, the best approaches to the marketing challenge the client must solve—the answer. Often the most effective method for conveying these results are visual.Marketing research graphics can deliver insights into almost all marketing strategies and activities. In some cases, standard pie charts and bar graphs are sufficient. But in our experience, such standard visual displays are overused and simply don’t tell enough of a story to have impact. To really convey the “so what” results from a primary 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 a mix of direct analytic results and synthesis-oriented displays; an array of options for use by students, product managers, C-suite executives, marketing and marketing research professionals.4
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- 6. Explaining Research Optionsto AudienceUse: Raises awareness of research options among groups new to market research. Often used during project planning meetings to help set expectations and explain trade-offs.6HighSocial Network MonitoringCustomer Advisory CouncilsImmediacyFocus GroupsIn-depth InterviewsQuantitative SurveysLowLowHighSuitable for Extrapolation
- 7. Agency Selection WeightedScorecard7Use: Aids in agency selection process by getting agreement on selection criteria and weights. Agencies are then judged on criteria, and weights applied, to reach a total score. In this example, Agency A’s superior presentation skills are a significant factor in making it a better choice for the client.
- 8. Customer Satisfaction/ Unmet Needs8
- 9. MATRIX Analysis9Use: Thisvisual displays a customer value management (CVM) quadrant. By constructing a visual critical path the CVM can serve as an organization’s strategic navigation. Attributes migrate from CVM categories, beginning as ‘Unmet Needs’ and migrating clockwise. The CVM method becomes a precise technique for assessing the role of new product features, predicting how they will migrate, and provides a map of the strategic directions of the product or corporate communication.StrengthsUnmet NeedsThese are the "primary strengths" of the Brand. These are "target issues" to improve brand equity. The Brand is performing below average and these attributes are important. Stronger ImpactImpact on Satisfaction/Purchase IntentConsumer concerns are being met, though these attributes are not important for brand equity. Potential for resource misallocation. These attributes are not crucial. Immediate focus should be on brand attributes.Weaker ImpactLow PriorityPotential AdvantagesWeaker PerformanceStronger PerformanceAverage Performance Scores
- 10. Identify Unmet NeedsBased on Current Product Perceptions: Laptop ExampleUse: Easily focuses audience on areas of opportunity (where importance is high and satisfaction is low). Most often used with quantitative data, but can be used with qualitative if clearly stated.10Takes less desk spaceHighTo use on sofaSatisfaction with Current ProductLooks coolTo loan to kidsTo take on airplanesLowLowHighImportance
- 11. IdentifyEmergingNeeds and CurrentBrand Perceptions: Digital Camera ExampleUse: Identifies areas of emerging opportunity based on potential for brand differentiation and perceived importance. Helps audience quickly see key results. Most often used with quantitative data, but can be used with qualitative if clearly stated.Battery lifeFalse20x zoomAll Brands do this Equally Well9+ Megapixels2 inch+ LCDStorage capacityTrueFalseTrueItem is becoming MORE important to me11
- 12. Key Drivers MapExample for Brand LoyaltyUse: Identifies areas of needed improvement by displaying results for sources of hypothesized customer disloyalty. For example, in this case, lack of product attribute Z is observed by many clients, but has little impact on loyalty. In contrast, competitors’ “cool” packaging is widely observed by clients and has a strong impact. This display helps audience quickly see areas for action. Works well with non-technical audiences.12Brand perception AHighCompetitorshave cooler packagingImpact on Customer LoyaltyLack of product attribute Z
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- 14. Pricing too highLowLowHighFrequencyof Perception
- 15. Customer Satisfaction ResultsUse:Summarizes items driving and deterring customer loyalty. Useful for non-technical audiences seeking key take-aways without a lot of details. Blue arrows at bottom indicate the dependent variables used in the data analysis. Supporting data would detail relative strength of each item.13Drives Disloyal BehaviorsDrives Loyal BehaviorsCall center wait time less than 3 minutes
- 16. Support requests resolvedwithin 30 minutes
- 17. Product installed andoperational in
- 18. Call center waittime exceeds3 minutes
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- 20. Product interface perceivedas complexPropensity to spread positive word of mouth$ Value of average purchase
- 21. Customer Loyalty Driversby StageUse: For Customer Satisfaction or Loyalty studies that identify those variables driving positive scores by stage. Often useful in organizations that want to assign performance goals for specific functional areas. In this case, the areas of focus are pre-sales, sales and product development (deployment of the product has implications for product design). A useful Management Summary display, typically supported by details in the body of a full report. This example is B2B, but can be used for consumer data as well.14Engineering services
- 22. Customer perception “Standsbehind its products”Pre-salesAccount manager expertise
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- 24. Financing optionsSalesEase ofintegration
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- 27. Competitive Issue Targeting16Use: Compares client brand strength versus a competitor on attributes that drive brand loyalty. Often used during Customer Satisfaction and Product Positioning studies. This graphic explains the six Loyalty quadrants attributes can be placed into.Critical WeaknessesParity IssuesLeverageable StrengthsStrongerBrand performs better than competitionBrand parityBrand underperformanceImpact on LoyaltyLow Priority IssuesSecondary WeaknessesWeakerAttribute performanceBetterWorse
- 28. BioTech Difference withCompetitors:Company Characteristics17Use: An at-a-glance summary of brand strengths vis-à-vis key competitors, this quadrant map is a guide to where the client company, BioTech, needs to allocate its communication resources.Communication Resource PrioritiesKeyVulnerabilitiesStrategicAdvantagesAmple availabilityof inhalers at pharmacyOffers asthma productswhich are a good value for the moneyBioTech Higher Than Both CompetitorsArnold Higher, InnoPharma Lower than BioTechInnoPharmaHigher, Arnold Lower than BioTechBioTechLower than Both CompetitorsOffers easy-to-use asthma administration devicesRegularly conducts clinicaloutcomes studies regardingasthmaProvides samples of bronchodilatorsIs dedicated tothe treatment of asthmaProvides samples of bronchodilatorsdelivery devicesPotentialVulnerabilitiesPotentialAdvantages
- 29. Classic Venn Diagram18Use:Classic Venn-style diagram. Shows how groups overlap, in this case to show that over time, an increasing percent of customers are favorable to both competing brands (suggesting that the brands may be losing perceived competitive differentiators).Favorableto Brand AFavorableto Brand B200612%Favorableto Brand AFavorableto Brand B34%2009
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- 31. Win/Loss Research DisplayUse:Clearly profiles win causes versus loss causes. For example, in this case the client’s 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 thus are not on the chart). In contrast, some loss factors overlap (they often occur in the same accounts,) so those percents add up to more than 100.20LossWinFactor AFactor E60%50%Factor BFactor F25%30%Factor G30%
- 32. Loss Factors OutweighingWin FactorsUse: Summarizes key factors identified in a Win/Loss study. Illustrates a case where three factors driving losses are outweighing two other factors that drive wins. Of course, other variations may exist. For example, in some cases, two strong Win factors could outweigh two Loss factors. Helps non-technical clients focus on key results.21LossWin
- 33. Brand Awareness &Equity22
- 34. Summary for BrandAwareness Research23Use: Shows how project objectives were clearly addressed in a Brand Awareness study. A simple, precise display used in presentations to focus audience on actionability of research results.Brand Equity: Regression Analysis24Likelihoodto RecommendUse: Identifies key drivers of brand equity—the ‘why’ behind the key measurement. This visual regression chart is common in Branding studies. Works well with product managers and executives in the C-suite.Providing welldesigned reports(.10)Overall Quality of Payment Process (.16)Overall Quality(.47)Efficient Data QueryHandling Process(.27)Providing reasonablepayment schedule(.32)Managing turnover(.30)Providing prompt payments(.27)Ability to train site staffin study protocol (.22)Providing fair payment amounts(.26)Having low turnover(.18)
- 35. Structural Equation ModelSkincare Brand EquitySkincare ComponentsLatent MeasurementSkincare AttributesPerformanceSkincareBenefitsDoes not dry out skinCleans wellLeaves skin soft and smoothDoes not leave skin itchyIs for everyday use SkincarePurchase Intent(.80)(.62)Product BouquetHas products that are fun to useHas a long-lasting fragranceHas products that make you smell greatThe color of the product is natural(.08)SkincareBrandEquitySkincare/Brand Rating(.65)Is dermatologist recommendedMade with the "latest" ingredientsContains familiar ingredientsIts products are made from natural ingredients that are good for youGenuine &Natural(.12)SkincareIndulgenceIs relaxingTurns my everyday shower into a few special minutesHas a calming effectHelps keep my skin looking young(.48)Recommend Skincare(.77)Quality & ValueMakes a great giftIs a product I would be proud to display in my bathroomCosts a little more, but worth it(.52)
- 36. Structural Equations Model:Skincare26Use: Structural Equations Modeling is a very powerful multivariate technique that incorporates a number of statistical analyses. The true strength of Structural Equations Modeling (SEM) is that it can be expressed in path diagrams, thus allowing clients and marketing managers to understand the output of SEM with a minimum of statistical background. SEM’s other main advantage is that it includes latent variables (Skincare brand equity) that are a compilation of discrete brand intent measurements (to the left). SEM is widely used in Brand Equity and Loyalty studies.
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- 38. Correspondence Analysis -Total28Biolin MixVariety of delivery formsBiolinPreferred for conservative therapyInsa-lin MixInsa-linReduces PPGlevelsGood valueEasy for patientto administerTrusted brandEffective when mixedEffective with oralantidiabetes agentsLowers HbA1cEffective without weight gainConsistent per doseresponseDuration of actionBiologPredictable day-to-dayConsistent throughout dayMimics physiologicpatternsInsalogLandis PharmaReduces risk of nocturnalhypoglycemiaPreferred for intensivetherapy
- 39. Correspondence Analysis -Total29Use: This multidimensional correspondence map represents a snapshot of the current market position of biotech products that includes measures of attribute 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.
- 40. Multidimensional Scaling30Main Causes/MainEffects of Weather ChangesCutting down of rain forest in South America and AsiaLack of recycling ofhousehold wasteDeforestation in the UKPollution by industryToo much building/ constructionon flood plainsHole in the ozone layerGreenhouse gas emissions/greenhouse effectFloodsPollution causedWarmer wintersby the motor carChanges in planning lawsWetter wintersHotter summersDroughtsWetter summersChanges in agricultureNew diseases, e.g. malariaOverpopulationMain Effects of Climate ChangesMain Causes of Climate Change
- 41. Multidimensional Scaling31Main Causes/MainEffects of Weather ChangesUse: Multidimensional scaling (MDS) is a set of related statistical techniques often used in information visualization for exploring similarities or dissimilarities in data. Our graphic is an environmental cause and effect visual to show how population 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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- 45. Superior product reliabilitySWOTDisplayStrengthsWeaknessesUse: Presents the top findings from a SWOT study. Usually supported by drill-downs for each quadrant. A clean display used in presentations to focus audience on most important results.ThreatsOpportunitiesLow-cost substitutes
- 46. Decline in discretionaryspending
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- 49. A Priori Examplefor Tech Segmentation34(An Option for Cases Where Needs-Based Segmentation is Impractical)Use: In some market segmentation studies, pure needs-based models can be ineffective (for various reasons). In such cases (and especially for technology markets), it is sometimes useful to segment based on category-level needs and awareness. To ensure usefulness, segments would be mapped to demographic and tactical factors. The upper right quadrant is the most attractive. Upper left quadrant is also attractive, but will have a longer sales cycle (such that specific sales and marketing implications exist).I need it & I know it!I need it BUT I don’t know it!Need ExistsI don’t need it & I know it!I don’t need it and am unawareNeed is ArticulatedLowHighLowHigh
- 50. 35VOTE OverviewUse: The Vote overview allows a political strategist to categorize what motives people to vote with five simple additions to the poll. These questions lead to the segments in the graphic.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't understand what the candidate stood for.I vote for the candidate who is most in line with my core issues.Image always determines who I vote for.I don't have a problem changing my opinion about who to vote for. High-Stakes GamblersCandidate AnalystsRationalTypicalInformationSeekersDecision DominanceConsistency SeekersImpulse Voters EmotionalLowHighUncertainty Tolerance
- 51. 36Multidimensional Preference MapsWhatdo You Want Your Look to Say About You?Use: Multidimensional preference analysis is a visual factor analysis.Proximity of personality attributes indicates group association relative to others on the map. The length of the attribute vector indicates the relative power of its impact on the graph. For example, ‘The Latest Woman’ thinks of herself more ‘feminine’ than ‘sexy’.Answers the question, ‘What attributes define a brand, or segmentation group.’ Deployed often in Fashion and Pharmaceutical segmentations.I am fun and whimsicalI take risks and push the limits I am sophisticatedCheeky ChicksFashion MondaineI have an eye for fashionI love being a womanI am conservativeI am successfulBourgeoisI am sexyThe Latest WomanI am feminineI am professional
- 52. About the AuthorsKathrynKorostoff 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 Lieberman37Michael 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 City University 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.
- 53. 38Thank you.Any commentsor [email protected]@mvsolution.com