Data Analytics in Marketing, Entrepreneurship, and Innovation

Business Analytics: Through SIoT and SIoVIntroductionBackgroundBenefits and AdvantagesSafety ManagementTraffic Control and ConvenienceProductivityCommercializationIssues, Controversies, ProblemsInformation Management in SIoVsSolutions and RecommendationsFuture Research DirectionsConclusionReferencesInnovation AnalyticsIntroductionScope of Innovation AnalyticsManagers and Analytics ApplicationsDiffusion of Innovation Analysis—Creating the EnvironmentThe Cases of Transformation for the Digital FutureNetflix Innovation AnalyticsEmirates Airlines Innovation Analytics (Marketing Techniques)Amazon and Innovation Analytics (Customers Database)Airbnb Innovation Analytics (New Product Development)Alibaba Strategy (Investing in People Knowledge)ReferencesBusiness Predictive Analytics: Tools and TechnologiesIntroductionLearning OutcomesBusiness Intelligence (BI) SoftwareFunctionalityWeaknessesIntended AudienceOpen-Source Analytics ToolsFunctionalityWeaknessesIntended AudienceProprietary Analytics ToolsFunctionalityWeaknessesIntended AudienceThe Right Tool for the JobCase Study: Microsoft Power BI and Football AttendanceGetting Started with Football Attendance DataIntroducing Microsoft Power BIImporting DataCreating a VisualCreating a FilterClub Performance and AttendanceLinking DataLooking for Relationships.DebriefingProblem SetBibliographyHospitality Analytics: Use of Discrete Choice Analysis for Decision SupportIntroductionLiterature ReviewFoundations of Consumer Research: Cognitive ApproachBehavioural Decision TheoryTheories of ChoiceThe AlternativesDecision RulesPast Research on Restaurant AttributesAscertaining Attribute ImportanceChallenges for Ascertaining ImportanceConjoint Analysis or Discrete Choice AnalysisConjoint Analysis in Restaurant Attributes ResearchResearch DesignPreliminary ConsiderationsDiscrete Choice ExperimentsSampling StrategyRecruitment of Participants, Pilot Study and Final SampleThe Research InstrumentScreening SectionChoice TournamentCounting Analysis for ACBCHB Analysis: Calculation of Utilities and ImportancesHB with CovariatesResults and DiscussionAn Outline of the Different Tasks (Sections)Fixed AttributesOptional AttributesAverage ImportancesHB Analysis with CovariatesDifference in Levels of Attributes for Every OccasionConclusionsImplications for the Restaurant IndustryReflections on Limitations of This ResearchReferencesData Analytics in Marketing and Customer AnalyticsIntroductionObjectives and Learning OutcomesDefinitions of Data Analytics, Business Analytics, Marketing and Marketing Management, Marketing Analytics, Customers and Consumers, Customer AnalyticsData AnalyticsBusiness AnalyticsMarketing and Marketing ManagementCustomer and ConsumerMarketing Analytics and Its SignificanceCustomer Analytics and Its RoleThe Marketing Management Tasks and ProcessTasks of Marketing ManagementProcess of Marketing ManagementMarketing ResearchSegmentation, Targeting, Differentiation, and Positioning (STD&P)Marketing Mix (4Ps and 7Ps) Including the UpdatedMarketing ImplementationMarketing Evaluation and ControlData Analytics in MarketingData PreprocessingData ModelingCustomer AnalyticsConclusions and ImplicationsAcknowledgmentsReferencesMarketing AnalyticsIntroductionInsights from a Survey of Small and Medium Enterprises (SMEs)from the UK's East Midlands RegionThe Paradox of the Perceived Impact of Marketing Analytics vs. FundingNeed for an Overarching Strategy for Marketing AnalyticsThe Historical Use of Metrics and Analytics in MarketingAvailability of Marketing Analytic Skills Specifically and Analytics Skills in GeneralPrevalence of Marketing Analytics Curricula in Business and Management EducationData Privacy vs. AnalyticsData Availability vs. Data QualityAccessibility of Paid Professional AnalyticsThe Future of Marketing AnalyticsThe Typology of Marketing Analytics – Laying the FoundationOverarching Strategy/Vision/LeadershipResources/Competency/Capacity/ToolsData Availability vs. Data QualityContextMeaning and Marketing IntelligenceConclusionReferencesBig Data AnalyticsCharacteristics of Big DataBig data in Fighting COVID-19Big Data in Artificial IntelligenceBig Data in Social Media and Internet of ThingsBig Data in Customer InteractionsBig Data in Data ScienceReferencesNew Product Development and Entrepreneurship AnalyticsIntroductionThe Concepts of New Product and New Product DevelopmentClassification of New ProductsNew-to-the-World ProductsNew Product LinesAdditions to Existing LinesImprovements and Revisions to Existing ProductsCost ReductionsRepositioningNew Product Development ProcessIdea Generation StageIdea Screening StageConcept Development and Testing StageConcept DevelopmentConcept TestingMarketing Strategy Development Stage.Business Analysis StageProduct Development StageTest Marketing StageCommercialization StageProduct Development Analytics.Predictive Analytics in Product DevelopmentEntrepreneurship AnalyticsAnalytics for Start-up EntrepreneursChoose the Right Analytics TeamCollect the Right DataMake Key Technology Decisions EarlyMeasure Your ResultsFind the Supportive InvestorsGrowth Hacking for Start-upsConclusionReferencesPredictive Learning Analytics in Higher EducationSection 1: IntroductionSection 2: Prospects and Challenges of Predictive AnalyticsProspectsChallengesSection 3: Ethical Framework and Considerations for Predictive Analytics in Higher EducationSection 4: The Application of Predictive Analytics in Higher EducationStudent Academic AdvisingAdaptive LearningMini-Case Study: Intellipath at Colorado Technical UniversityManagement of Student EnrolmentStudent Academic PerformancePredictive Analytics for Curriculum InternationalizationSection 5: Case Studies of Predictive Learning Analytics in Higher Education ManagementIntroductionPredictive Analytics in Georgia State and Kennesaw State UniversitiesPredictive Analytics at Mount St Mary's UniversityReferences
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