book

Machine Learning and Generative AI for Marketing

Name: Machine Learning and Generative AI for Marketing
ISBN: 9781835889404

by Yoon Hyup Hwang, Nicholas C. Burtch

August 2024

Beginner to intermediate

482 pages

11h 28m

English

Packt Publishing

Read now

Unlock full access

Preface
Who this book is forWhat this book coversTo get the most out of this bookGet in touch
The Evolution of Marketing in the AI Era and Preparing Your Toolkit
The evolution of marketing with AI/MLThe pre-AI era of marketingThe advent of digital marketingThe integration of AI/ML in marketingCore data science techniques in marketingPredictive analyticsUnderstanding customer dataA/B testing and experimentationSegmentation and targetingCustomer lifetime value modelingIntegrating AI for enhanced insightsSetting up the Python environment for AI/ML projectsInstalling the Anaconda distributionInstalling essential Python libraries for AI/MLIntegrating your environment with JupyterLabVerifying your setupNavigating the Jupyter notebookTraining your first ML modelStep 1: Importing the necessary librariesStep 2: Loading the dataStep 3: Exploratory data analysisStep 4: Preparing the data for MLStep 5: Training a modelStep 6: Evaluating the modelSummary
Decoding Marketing Performance with KPIs
Understanding marketing KPIsAwareness stageEngagement stageConversion stageRetention stageLoyalty stageComputing and visualizing KPIs from data with PythonConversion rateOverall conversion rateDemographics and conversion rateSales channel and conversion rateCLVOverall CLVGeolocation and CLVProduct and CLVCPAOverall CPASales channel and CPAPromotions and CPAROIOverall ROIROI per sales channelROI per promotionCorrelations across KPIsTracking marketing performance with KPIsChoosing the right visualization for KPIsOngoing KPI trackingSummary
Unveiling the Dynamics of Marketing Success
Why people engage in regression analysisTarget variableContinuous variablesCategorical variablesFactorizeCategoricalDummy variablesRegression analysisInteraction variablesWhy people convert with decision tree interpretationTarget variableContinuous versus categorical variableDecision tree analysisWhy people churn with causal inferenceCausal effect estimationCausal modelEstimationRefutationGraphical causal modelCausal influenceAnomaly attributionSummary
Harnessing Seasonality and Trends for Strategic Planning
Time series analysis basicsBasic time series trendsMoving averagesAutocorrelationProduct trendsTrend and seasonality decompositionAdditive time series decompositionMultiplicative time series decompositionTime series forecasting modelsARIMATraining the ARIMA modelARIMA model diagnosticsForecasting with the ARIMA modelProphet time-series modelingTraining a Prophet modelForecasting with a Prophet modelOther time-series modelsSummary
Enhancing Customer Insight with Sentiment Analysis
Introduction to sentiment analysis in marketingThe significance of sentiment analysisAdvancements in AI and sentiment analysisPractical example: Twitter Airline Sentiment datasetPreparing data for sentiment analysisTraditional NLP techniques for data preparationCleaning text dataTokenization and stop word removalLemmatizationClass imbalanceEvaluating class balanceAddressing class imbalanceGenAI for data augmentationPerforming sentiment analysisBuilding your own ML modelFeature engineeringModel trainingModel evaluationClassification reportUsing pre-trained LLMsImplementing pre-trained modelsEvaluating model performanceTranslating sentiment into actionable insightsCreating your own datasetCollecting twitter dataCollecting data from other platformsPerforming NER on a dataset for a fictional retailerUnderstanding topics and themesUsing word cloudsDiscovering latent topics with LDATemporal trends: tracking the brand narrativeMapping sentiments using geospatial analysisSummary
Leveraging Predictive Analytics and A/B Testing for Customer Engagement
Predicting customer conversion with tree-based algorithmsTree-based machine learning algorithmsBuilding random forest modelsTarget and feature variablesTraining a random forest modelPredicting and evaluating random forest modelGradient boosted decision tree (GBDT) modelingTraining GBDT modelPredicting and Evaluating GBDT ModelPredicting customer conversion with deep learning algorithmsTrain and test sets for deep learning modelsWide neural network modelingTraining the wide neural network modelPredicting and evaluating wide neural network modelDeep neural network modelingTraining deep neural network modelPredicting and evaluating the deep neural network modelConducting A/B testing for optimal model choiceSimulating A/B TestingTwo-tailed T-testSummary
Personalized Product Recommendations
Product analytics with market basket analysisApriori algorithm – finding frequent itemsetsAssociation rulesCollaborative filteringUser-based collaborative filteringRecommending by the most similar customerRecommending by the top products bought by similar customersItem-based collaborative filteringRecommending by the most similar itemsRecommending by the purchase historyOther frequently used recommendation methodsSummary
Segmenting Customers with Machine Learning
One-time versus repeat customersThe need to retain customersAnalyzing the impact of retaining customersCustomer segmentation with purchase behaviorsK-means clusteringWithout log transformationWith log transformationSilhouette scoreCustomer segmentation with product interestsSummary
Creating Compelling Content with Zero-Shot Learning
Fundamentals of generative AIA probabilistic approachFoundational modelsGenerative adversarial networksVariational autoencodersLong short-term memory networksTransformersWhen GenAI is the right fitIntroduction to pre-trained models and ZSLContextual embeddingsSemantic proximityPre-trained modelsModel weightsModel architecturePreprocessing stepsZero-shot learningMechanics of learning and predictionOutput parametersZSL for marketing copyPreparing for ZSL in PythonCreating an effective promptExample 1: Product descriptionsExample 2: Blog post titlesExample 3: Social media captionsImpact of parameter tuningSummary

Enhancing Brand Presence with Few-Shot Learning and Transfer Learning
Navigating FSLUnderstanding FSL through meta-learningImplementing model-agnostic meta-learning in marketingOvercoming challenges in FSLNavigating transfer learningThe mechanics of transfer learningTransfer learning using KerasTransfer learning using API servicesOvercoming challenges in transfer learningApplying FSL to improve brand consistencyBenchmarking with ZSL and FSLDeveloping an email marketing campaignStep 1: Initial email creationStep 2: Collect and analyze initial metrics and responsesStep 3: Iterative refinementStep 4: Continued feedback integrationSummary
Micro-Targeting with Retrieval-Augmented Generation
Introduction to RAG for precision marketingHow RAG worksMathematical model of RAG retrievalThe importance of data in RAGData freshnessData specificityUnderstanding the retrieval indexIndexing strategiesData curation and updatingRAG implementation challengesApplications of RAG in marketingBuilding a knowledge retrieval system for marketing with LangChainIntroduction to LangChainUnderstanding the external datasetDesigning the retrieval model with LangChainInstall and connect to ElasticsearchIndexing data in ElasticsearchData ingestion into ElasticsearchIntegrating with LangChain using GPTImplementing RAG for micro-targeting based on customer dataDetermining the campaign strategyMessage timingChoosing the brandUsing LangChain for micro-targetingCase study 1: Targeted product discountsCase study 2: Product upsellingCase study 3: Real-time content customization for bpw.styleSummary
The Future Landscape of AI and ML in Marketing
Consolidating key AI and ML conceptsNext-generation AI technologies in marketingFrom RAG to ReActHow ReAct worksApplications in marketingModel architecture advancesDiffusion modelsNeural radiance fieldsCapsule networksMulti-modal GenAITechnical foundationsMulti-modal applicationsAR and VR as emerging digital platformsThe role of ML in AR for marketingPersonalized AR experiencesGeolocation-based AR campaignsSeamless product integrationThe role of ML in VR for marketingImmersive storytelling and brand experiencesBehavioral data insightsVirtual storefronts and eventsSummary
Ethics and Governance in AI-Enabled Marketing
Ethical considerations in AI/ML for marketingModel transparency and explainabilityModel explainability toolsBias mitigationAddressing training data biasAlgorithmic fairness techniquesDiversity in model evaluation dataGrounding for LLMsChain-of-thought reasoningBalancing privacy with personalizationFederated learningDifferential privacy and anonymizationGovernance and regulatory complianceIntellectual property protectionData and model licensing and attributionInternal data ownership and securityData management and collection practicesEthical governance frameworksInternal AI ethics committeesAI policy development and reportingContinuous training and awarenessRegulatory complianceGeneral Data Protection Regulation (GDPR)California Consumer Privacy Act (CCPA)Global regulations and standardsIndustry-specific guidelinesSummary
Other Books You May Enjoy
Index

Content preview from Machine Learning and Generative AI for Marketing

Index

A/B testing 11, 213

conducting, for optimal model choice 213, 214

simulating 214-218

two-tailed t-test 219, 220

Additive time-series decomposition method 120-124

AI advancements 145

AI marketing models

bias, mitigating strategies 417

AI/ML, for marketing 396

bias, mitigating 416-419

ethical considerations 410

ethics and governance 168

explainability 410, 411

model architecture advances 398

model explainability tools 411-416

multi-modal GenAI 401

privacy with personalization, balancing 420-426

Python environment, setting up for 13

Python libraries, installing for 14

ReAct 396

transparency 410

Akaike Information Criterion (AIC) 131

Anaconda distribution

installing 13

anomaly attribution 102-105

anonymization 421

API services

using, for ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

Applied Machine Learning and AI for Engineers

Publisher Resources

ISBN: 9781835889404

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills

Machine Learning and Generative AI for Marketing

by Yoon Hyup Hwang, Nicholas C. Burtch

Index

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.