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

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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

Overview

In "Machine Learning and Generative AI for Marketing," you will explore how to fuse Python programming with the latest AI advancements to create effective data-driven marketing strategies. With practical examples and hands-on projects, this book empowers you to analyze and predict customer behaviors, personalize campaigns, and elevate your marketing efforts.

What this Book will help me do

Master advanced AI and machine learning techniques for marketing.
Implement predictive models to enhance customer engagement and decision-making.
Perceive customer behaviors using advanced segmentation and analytics.
Craft personalized marketing strategies through generative AI.
Learn ethical and compliant practices for leveraging AI in marketing.

Author(s)

Yoon Hyup Hwang and Nicholas C. Burtch have combined their expertise as seasoned professionals in the fields of machine learning and marketing analytics to bring you this comprehensive resource. Both authors have extensive experience working with marketing data and applying advanced AI techniques to real-world challenges. Their collaborative approach to writing ensures that readers are guided through both the technical and practical aspects of applying AI in marketing.

Who is it for?

This book is ideal for data analysts and scientists wanting to apply ML to marketing contexts, ML engineers looking to leverage generative AI for content marketing, and business professionals interested in integrating AI into marketing strategies. Readers should have intermediate Python proficiency and a fundamental understanding of ML concepts.

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