book

AI for Marketing and Product Innovation

by A. K. Pradeep, Andrew Appel, Stan Sthanunathan

December 2018

Beginner to intermediate

272 pages

5h 11m

English

Wiley

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Living in the Age of the Algorithm
Concept 1: Rule-based SystemsConcept 2: Inference EnginesConcept 3: HeuristicsConcept 4: Hierarchical LearningConcept 5: Expert SystemsConcept 6: Big DataConcept 7: Data CleansingConcept 8: Filling Gaps in DataConcept 9: A Fast Snapshot of Machine LearningAreas of Opportunity for Machine Learning
Intuition Behind Neural Networks and Deep Learning AlgorithmsLet It Go: How Google Showed Us That Knowing How to Do It Is Easier Than Knowing How You Know It
Intuition Behind Clustering and Classification AlgorithmsIntuition Behind Forecasting and Prediction AlgorithmsIntuition Behind Natural Language Processing Algorithms and Word2VecIntuition Behind Data and Normalization Methods
Principal Component Analysis and Its ApplicationsIntuition Behind Rule-based and Fuzzy Inference EnginesIntuition Behind Genetic Algorithms and OptimizationIntuition Behind Programming Tools
Supervised LearningUnsupervised LearningReinforcement Learning
Data SourcesWorkarounds to Get the Job DoneCleaning Up Missing or Dummy Data

Inputs and Data for Product InnovationAnalytical Tools for Product InnovationStep 1: Identify Metaphors – The Language of the Non-conscious MindStep 2: Separate Dominant, Emergent, Fading, and Past Codes from MetaphorsStep 3: Identify Product Contexts in the Non-conscious MindStep 4: Algorithmically Parse Non-conscious Contexts to Extract ConceptsStep 5: Generate Millions of Product Concept Ideas Based on CombinationsStep 6: Validate and Prioritize Product Concepts Based on Conscious Consumer DataStep 7: Create Algorithmic Feature and Bundling OptionsStep 8: Category Extensions and Adjacency ExpansionStep 9: Premiumize and Luxury Extension Identification
Key Inputs and Data for Machine-based Pricing AnalysisA Control Theoretic Approach to Dynamic PricingRule-based Heuristics Engine for Price Modifications
Timing of a PromotionTemplates of Promotion and Real Time OptimizationConvert Free to Paying, Upgrade, UpsellLanguage and Neurological CodesPromotions Driven by Loyalty Card DataPersonality Extraction from Loyalty Data – Expanded UseCharity and the Inverse Hierarchy of Needs from Loyalty DataPlanogram and Store Brand, and Store-Within-a-Store Launch from Loyalty DataSwitching Algorithms
Inputs and Data for SegmentationAnalytical Tools for Segmentation
Brand PersonalityMachine-based Brand Tracking and Correlation to PerformanceMachine-based Brand Leadership AssessmentMachine-based Brand Celebrity Spokesperson SelectionMachine-based Mergers and Acquisitions Portfolio CreationMachine-based Product Name Creation
Compression of Time – The Real Budget SavingsWeighing the Worth of Programmatic BuyingNeuroscience Rule-based Expert Systems for Copy TestingCapitalizing on Fading Fads and Micro Trends That Appear and Then DisappearCapitalizing on Past Trends and Blasts from the PastRFP Response and B2B Blending News and Trends with StoriesSales and Relationship ManagementProgrammatic Creative Storytelling
What Does This Mean for Strategy?What to Do In-house and What to OutsourceWhat Kind of Partnerships and the Shifting LandscapesWhat Are Implications for Hiring and Talent Retention, and HR?What Does Human Supervision Mean in the Age of the Algorithm and Machine Learning?How to Question the Algorithm and Know When to Pull the PlugNext Generation of Marketers – Who Are They, and How to Spot ThemHow Budgets and Planning Will Change
What Does an ML- and AI-enabled Market Research or Marketing Services Agency Look Like?What an ML- and AI-enabled Research Agency or Marketing Services Company Can Do That Traditional Agencies Cannot DoThe New Nature of PartnershipIs There a Role for a CES or Cannes-like Event for AI and ML Algorithms and Artificial Intelligence Programs?Challenges and SolutionsBig DataAI- and ML-powered Strategic DevelopmentCreative ExecutionBeam Me UpWill Retail Be a Remnant?Getting RealIt Begins – and Ends – with an “A” Word

Content preview from AI for Marketing and Product Innovation

INDEX

A

Absolute value function, 48, 196
Accenture, 209
Acura, 175
Adjacency expansion, 129
Advertising costs:
- rationalizing of, 24–25
- and time compression, 178–181
Advocacy, consumer, 142–143
Agreeableness (personality trait), 167–168
Ahrendts, Angela, on consumer data, 26
AI, see Artificial Intelligence
“AI-complete” problem, 61
Algebra, 78
Algorithms, xxiii
- forecasting/prediction, 54–61
- for future creative/research agencies, 203–204, 221–223
- genetic, 87–92
- for identifying product contexts, 125–126
- NLP, 61–70
- for promotions, 139–140
- tasks executed by, 3–5
Algorithmic Pricing, 131
Alpha-beta search (game AI), 42
AlphaGo, 39
AlphaZero, 2, 39–40, 49
Altman, Sam, on AI, xxv
Amazon, 22, 111, 196, 211, 219–220
Ambiguity score, 186
Anomalies, detecting, 116–117
Apple, 220
Ariely, Dan, on Big Data, 181
Armstrong, Stuart, on difficulty of programming, 153
Artificial Intelligence (AI):
- defined, xxii
- latest capabilities of, xxv
- partnerships with, 195–196
Artificial selection, 88
Association, 103
ATM fees, 136
Attitude, 21
Auto-completion, 62, 65
Automatic rule inference, 10
Automatic translation, 69
Average values, 19
Awareness, consumer, 141