Slide 1The Importance of InformationThe Marketing Information SystemMarketing Information SystemAssessing Information NeedsWhere Will This Information Come From?The Marketing Research ProcessDefining the Problem and ObjectivesDeveloping the Research PlanGathering Secondary DataPrimary Data CollectionPrimary Research Comes in Two Flavors:Observation TechniquesPros and Cons of ObservationSurvey ResearchExperimental ResearchSampling Issues for Quantitative ResearchImplementing the Research PlanSpecial topic in market research: data miningWhat’s a data warehouse?Strategic Objectives of Data MiningData Mining Statistical AnalysesChapter 4Managing Marketing Information to Gain Customer InsightsThe Importance of InformationCompanies need information about their: customer needs marketing environment competitionMarketing managers do not need more information, they need better information.The Marketing Information SystemMarketing Information SystemAn MIS consists of people, equipment, and procedures to gather, sort, analyze, evaluate, and distribute needed, timely, and accurate information to marketing decision makers.The MIS helps managers to: 1. Assess information needs 2. Develop needed information3. Distribute informationAssessing Information NeedsA good MIS balances the information users would like against what they really need and what is feasible to offer.Sometimes the company cannot provide the needed information because it is not available or due to MIS limitations. Have to decide whether the benefits of more information are worth the costs.Where Will This Information Come From?Internal Databases: Electronic collections of information obtained from data sources within the company.Marketing Intelligence: Systematic collection and analysis of publicly available information about competitors and developments in the marketing environment.Marketing Research: Systematic design, collection, analysis, and reporting of data relevant to a specific marketing situation facing an organization.The Marketing Research ProcessDefining the Problem and Objectives Gather preliminary information that will help define the problem and suggest hypotheses. Exploratory Research Descriptive Research Causal Research Describes things (e.g., market potential for a product, Demographics, and attitudes). Tests hypotheses about cause-and-effect relationships.Developing the Research PlanIncludes:Determining the exact information needed Developing a plan for gathering it efficiently Presenting the written plan to managementOutlines:Sources of existing dataSpecific research approachesContact methodsSampling plansInstruments for data collectionGathering Secondary DataInformation that already exists somewhereInternal databasesCommercial data servicesGovernment servicesAvailable more quickly and at a lower cost than primary dataMust be relevant, accurate, current, and impartialPrimary Data CollectionConsists of information collected for the specific purpose at hand.Must be relevant, accurate, current, and unbiased.Must determine:Research approach Contact methodsSampling planResearch instruments12Primary Research Comes in Two Flavors:Qualitative ResearchObservations In-Depth InterviewingFocus groupsQuantitative ResearchSurveysExperiments“Big Data”Observation TechniquesTypesObtrusiveUnobtrusiveRelevant SituationsDecision Making/PurchaseConsumptionImportant Information to GatherUser behaviors and sequence characteristics of the situation (e.g., who was involved, roles played, physical environment, etc.particular aspects of the product that played a key roleUser problems or frustrationsemotionOutcomes of product usetime expended14Pros and Cons of ObservationProsRecord what “actually happened” without any bias or memory filtersAvoids problems of measuring attitudes or motivationsTechnology can facilitate detailed data gatheringConsAttitudes and motivations cannot be determined Some behavior cannot be observed because it is unethical or illegal15Survey ResearchMethodsTelephone, mail, face-to-face, electronicRelies heavily on scaled/categorized responses (closed-end)Some open ended questions may be includedExamples include demographic data, satisfaction judgments, product use and intentions, attitudesProsVersatile Quick data gathering and analysis Accuracy of dataConsSurvey construction and quality highly variable – requires expertiseImplementation time can be lengthy  Sampling must be scientificMay yield a great deal of data, but little knowledge16Experimental ResearchUsed to understand causal relationships between marketing mix variables (e.g., products, ads, packaging, promotions) and consumer responses (e.g., purchase, intentions, preference) Experimental groups are compared to each other and/or to control groupsField experiments: tested in natural environments, e.g., in homes, grocery stores, town-to-townLaboratory experiments: in a controlled environment, e.g., R&D facilities, “mock” stores, mall interceptsProsIsolation of the test (independent) variable supports cause-effect judgmentsConsTime consuming and expensive Not always able to isolate all possible causal variables17Sampling Issues for Quantitative ResearchProbability SamplingSimple random sample – every member of the population has an equal and known probability of selectionStratified random sample – Population is divided into mutually exclusive groups and each group is randomly sampledNon-Probability SamplingConvenience Sample – Researcher selects population based upon ease and availability.Judgement sample – Researcher strategically selects members who are good representatives of the populationCollecting the DataProcessing theDataAnalyzing theData Research PlanImplementing the Research Plan Most Expensive andSubject To Error19Special topic in market research: data miningWhat is data mining?A process that accesses the company’s internal data, (usually stored in a data warehouse) and which then transforms data into knowledge, and hopefully, wisdom.What happens?Discovers patterns and relationships hidden in data by using advanced statistical analysis and modeling techniques20What’s a data warehouse?An assembly of disparate data from