Marketing with AI for Dummies:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Newark
John Wiley & Sons, Incorporated
2024
|
| Ausgabe: | 1st ed |
| Schlagworte: | |
| Online-Zugang: | DE-2070s |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (403 Seiten) |
| ISBN: | 9781394237203 |
Internformat
MARC
| LEADER | 00000nam a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV050101231 | ||
| 003 | DE-604 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 241218s2024 xx o|||| 00||| eng d | ||
| 020 | |a 9781394237203 |9 978-1-394-23720-3 | ||
| 035 | |a (ZDB-30-PQE)EBC31622257 | ||
| 035 | |a (ZDB-30-PAD)EBC31622257 | ||
| 035 | |a (ZDB-89-EBL)EBL31622257 | ||
| 035 | |a (OCoLC)1453270679 | ||
| 035 | |a (DE-599)BVBBV050101231 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-2070s | ||
| 082 | 0 | |a 658.80028563 | |
| 100 | 1 | |a Singh, Shiv |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Marketing with AI for Dummies |
| 250 | |a 1st ed | ||
| 264 | 1 | |a Newark |b John Wiley & Sons, Incorporated |c 2024 | |
| 264 | 4 | |c ©2024 | |
| 300 | |a 1 Online-Ressource (403 Seiten) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 500 | |a Description based on publisher supplied metadata and other sources | ||
| 505 | 8 | |a Intro -- Title Page -- Copyright Page -- Table of Contents -- Introduction -- About This Book -- Foolish Assumptions -- Icons Used in This Book -- Beyond the Book -- Where to Go from Here -- Part 1 Getting Started with Marketing with AI -- Chapter 1 A Brief History of AI -- Early Technological Advances -- Alan Turing and Machine Intelligence -- The Turing Test in 1950 -- The Turing test: 1960s and beyond -- The Dartmouth Conference of 1956 -- Machine Learning and Expert Systems Emerge -- Meeting machine learning -- Examining expert systems -- An AI Winter Sets In -- The Stanford Cart: From the '60s to the '80s -- More AI Developments in the 1980s -- Rapid Advancements of AI in the 1990s and Beyond -- Watching machine learning grow up -- Playing a pivotal chess match -- Tracking the deep learning revolution -- Demonstrating intuition in the age of AI -- Creating content with generative AI -- Chapter 2 Exploring AI Business Use Cases -- Automating Customer Service -- Serving customers by using chatbots -- Resolving customer issues with virtual assistants -- Seeking out trends and solutions with sentiment analysis -- Enhancing Product and Technology with AI -- Streamlining product validation -- Simulating user experience testing -- Writing code -- Detecting and resolving software bugs -- Testing software and creating documentation -- Accelerating Research and Development -- Generating and exploring ideas -- Extracting insights from data -- Optimizing product designs and production processes -- Giving Marketing an AI Boost -- Creating coherent, consistent content -- Personalizing marketing messages -- Managing digital advertising -- Streamlining search engine optimization (SEO) -- Optimizing Sales with AI -- Driving profitability -- Nurturing leads -- Forecasting sales -- Adding AI to Legal Activities -- Analyzing documentation for legal research | |
| 505 | 8 | |a Evaluating and drafting contracts -- Performing due diligence -- Managing intellectual property -- Chapter 3 Launching into the AI Marketing Era -- Ready or Not: AI Is Your New Marketing Copilot -- Putting performance marketers at risk -- Competing with creative directors -- Watching AI Upend the Corporate World -- Taking Foundational Steps Toward AI Marketing -- Addressing the marketing dichotomy -- Assessing progress with the AI checklist -- Adopting a Strategic Framework for Entering the AI Era -- Going for liftoff -- Initiating atmospheric ascent -- Reaching escape velocity -- Dominating deep space -- Part 2 Exploring Fundamental AI Structures and Concepts -- Chapter 4 Collecting, Organizing, and Transforming Data -- Defining Data in the Context of AI -- Considering the quality of data -- Getting an appropriate quantity of data -- Choosing Data Collection Methods for Marketing with AI -- Identifying data sources and methods -- Minding data privacy and ethics -- Putting Your Marketing Data in Its Place -- Understanding Data via Manual and Automated Systems -- Preparing the Data for Use by AI Algorithms and Models -- Perfecting data by cleaning -- Transforming data -- Splitting data into subsets -- Trimming down data -- Handling imbalanced and irrelevant data -- Chapter 5 Making Connections: Machine Learning and Neural Networks -- Examining the Process of Machine Learning -- Understanding Neural Networks -- Layers of a neural network -- Challenges with neural networks -- Supervised and Unsupervised Learning -- Following the path of supervised learning -- Embracing the freedom of unsupervised learning -- Exploring Reinforcement Learning -- Reinforcement learning in e-mail marketing -- Weighing explorations against exploits -- Mastering Sequences and Time Series -- Seeing how neural networks excel at time series analysis | |
| 505 | 8 | |a Embracing time series features, challenges, and tools -- Developing Vision and Image Processing in AI -- Exploiting the power of convolutional neural networks (CNNs) -- Looking deeper: Advanced vision techniques -- Tools for Machine Learning and Neural Networks -- Participating with Python -- Diving into deep learning platforms -- Chapter 6 Adding Natural Language Processing and Sentiment Analysis -- Demystifying the Backbone of NLP -- Exploring linguistics for NLP -- Seeing the big picture with statistical NLP -- Why linguistics and NLP both matter -- Elevating NLP with Machine Learning -- Integrating NLP and machine learning -- Adapting to the emotional spectrum -- Examining Transformers and Attention Mechanisms -- Unpacking Sentiment Analysis -- Catching the feeling -- Understanding language nuances -- Integrating social media analytics -- Challenges for NLP and Sentiment Analysis -- Engaging Best Practices for Using NLP and Sentiment Analysis -- Chapter 7 Collaborating via Predictions, Procedures, Systems, and Filtering -- Understanding Predictive Analytics -- Using predictive analytics in various industries -- Building predictive models -- Best practices for predictive analytics -- Putting AI Procedures into Practice -- The AI System Development Lifecycle -- Understanding Filtering in AI -- Knowing where you encounter filtering -- AI filtering in recommendation systems -- Chapter 8 Getting Comfortable with Generative AI -- Changing the Game with Generative AI -- Knowing core generative AI concepts and techniques -- Reviewing the training process for generative AI models -- Getting to Know GPT Models -- Training the models is intensive -- Exploring the models' operation -- Creating New Text, Images, and Video -- Generating text -- Creating images -- Producing video -- Introducing Major Consumer-Facing Generative AI Models | |
| 505 | 8 | |a Addressing the Challenges of Using Generative AI Models -- Seeing the technical challenges and limitations -- Exposing ethical and societal consequences -- Part 3 Using AI to Know Customers Better -- Chapter 9 Segmentation and Persona Development -- Exploring Behavioral Segmentation Elements -- Sourcing the Right Customer Data -- Seeing How AI Performs Segmentation -- Refining, Validating, and Enhancing Segmentation Models -- Two aspects of AI model refinement -- Validation techniques -- Aligning Persona Development -- Verifying the authentic core of AI-created personas -- Ethical considerations in persona development -- Leveraging AI Personas for All Business Efforts -- Driving the customer experience -- Directing marketing with personas -- Aligning product offerings with personas -- Employing Synthetic Customer Panels -- Creating synthetic panels -- Embracing the opportunities -- Managing the risks -- Chapter 10 Lead Scoring, LTV, and Dynamic Pricing -- Working Together: Three Core Concepts -- Identifying potential leads -- Maximizing customer potential -- Adapting to market conditions on the fly -- Scoring Leads with the Help of AI -- Instilling precision with AI solutions -- Leveraging machine learning algorithms -- Achieving precision through predictive analytics -- Enhancing customer interfaces (and experiences) with AI -- Validating AI-powered lead scoring via empirical evidence -- Enhancing data analysis with AI tools -- Finding companies that offer AI-infused lead-scoring capabilities -- Calculating Lifetime Value to Affect Lead Scoring -- Allowing for predictive customer analysis -- Finding companies that offer AI-infused LTV calculations -- Turning Lead Scoring and LTV Insights into Dynamic Pricing -- Chapter 11 Churn Modeling and Measurement with AI -- Getting the Scoop on Churn Modeling -- Building your churn model | |
| 505 | 8 | |a Validating, calibrating, and integrating your churn model -- Improving churn insights with generative AI -- Combating churn with customer retention strategies -- Personalizing customer interactions -- Enhancing customer support -- Implementing loyalty programs -- Conducting regular feedback and follow-up initiatives -- Using exit surveys and win-back campaigns -- Ramping Up Your Measurement Operations -- Letting AI drive data collection and monitoring -- Optimizing measurement operations with AI techniques -- Incorporating visualization and reporting solutions -- Checking Out Tools for Churn Modeling and Measurement Operations -- Part 4 Transforming Brand Content and Campaign Development -- Chapter 12 Using AI for Ideation and Planning -- Engaging AI to Ideate on Behalf of Human Beings -- Deciding whether AI Hallucinations Are a Feature or a Bug -- Bringing in unexpected ideas and concepts -- Branching out with non-traditional storytelling -- Facilitating testing and experimentation -- Staying the course with generative AI -- Following Practical Steps for Idea Generation with AI -- Starting with the right prompts -- Stepping through an AI-for- ideation exercise -- Deciding on AI Ideation Tools to Use -- Chapter 13 Perfecting Prompts for Conversational Interfaces -- Reviewing Use Cases for Conversational Interfaces -- Writing Strong Prompts to Guide AI Responses -- Setting the voice and tone -- Defining a role -- Identifying the AI's task -- Specifying the format -- Good and Bad Marketing Prompt Design Examples -- Refining and Iterating Strong Prompts -- Fighting AI Bias in Prompt Writing -- Using Prompt Design Apps -- Chapter 14 Developing Creative Assets -- Trying Out an AI-Generated Where's Waldo? Illustration -- Exploring an Approach for Creating Visual Assets with AI -- Minding the integrity of your customers, data, and teams | |
| 505 | 8 | |a Examining an example scenario | |
| 650 | 4 | |a Artificial intelligence | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Singh, Shiv |t Marketing with AI for Dummies |d Newark : John Wiley & Sons, Incorporated,c2024 |z 9781394237197 |
| 912 | |a ZDB-30-PQE | ||
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-035438393 | |
| 966 | e | |u https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=31622257 |l DE-2070s |p ZDB-30-PQE |q HWR_PDA_PQE |x Aggregator |3 Volltext | |
Datensatz im Suchindex
| _version_ | 1818760561772462080 |
|---|---|
| adam_text | |
| any_adam_object | |
| author | Singh, Shiv |
| author_facet | Singh, Shiv |
| author_role | aut |
| author_sort | Singh, Shiv |
| author_variant | s s ss |
| building | Verbundindex |
| bvnumber | BV050101231 |
| collection | ZDB-30-PQE |
| contents | Intro -- Title Page -- Copyright Page -- Table of Contents -- Introduction -- About This Book -- Foolish Assumptions -- Icons Used in This Book -- Beyond the Book -- Where to Go from Here -- Part 1 Getting Started with Marketing with AI -- Chapter 1 A Brief History of AI -- Early Technological Advances -- Alan Turing and Machine Intelligence -- The Turing Test in 1950 -- The Turing test: 1960s and beyond -- The Dartmouth Conference of 1956 -- Machine Learning and Expert Systems Emerge -- Meeting machine learning -- Examining expert systems -- An AI Winter Sets In -- The Stanford Cart: From the '60s to the '80s -- More AI Developments in the 1980s -- Rapid Advancements of AI in the 1990s and Beyond -- Watching machine learning grow up -- Playing a pivotal chess match -- Tracking the deep learning revolution -- Demonstrating intuition in the age of AI -- Creating content with generative AI -- Chapter 2 Exploring AI Business Use Cases -- Automating Customer Service -- Serving customers by using chatbots -- Resolving customer issues with virtual assistants -- Seeking out trends and solutions with sentiment analysis -- Enhancing Product and Technology with AI -- Streamlining product validation -- Simulating user experience testing -- Writing code -- Detecting and resolving software bugs -- Testing software and creating documentation -- Accelerating Research and Development -- Generating and exploring ideas -- Extracting insights from data -- Optimizing product designs and production processes -- Giving Marketing an AI Boost -- Creating coherent, consistent content -- Personalizing marketing messages -- Managing digital advertising -- Streamlining search engine optimization (SEO) -- Optimizing Sales with AI -- Driving profitability -- Nurturing leads -- Forecasting sales -- Adding AI to Legal Activities -- Analyzing documentation for legal research Evaluating and drafting contracts -- Performing due diligence -- Managing intellectual property -- Chapter 3 Launching into the AI Marketing Era -- Ready or Not: AI Is Your New Marketing Copilot -- Putting performance marketers at risk -- Competing with creative directors -- Watching AI Upend the Corporate World -- Taking Foundational Steps Toward AI Marketing -- Addressing the marketing dichotomy -- Assessing progress with the AI checklist -- Adopting a Strategic Framework for Entering the AI Era -- Going for liftoff -- Initiating atmospheric ascent -- Reaching escape velocity -- Dominating deep space -- Part 2 Exploring Fundamental AI Structures and Concepts -- Chapter 4 Collecting, Organizing, and Transforming Data -- Defining Data in the Context of AI -- Considering the quality of data -- Getting an appropriate quantity of data -- Choosing Data Collection Methods for Marketing with AI -- Identifying data sources and methods -- Minding data privacy and ethics -- Putting Your Marketing Data in Its Place -- Understanding Data via Manual and Automated Systems -- Preparing the Data for Use by AI Algorithms and Models -- Perfecting data by cleaning -- Transforming data -- Splitting data into subsets -- Trimming down data -- Handling imbalanced and irrelevant data -- Chapter 5 Making Connections: Machine Learning and Neural Networks -- Examining the Process of Machine Learning -- Understanding Neural Networks -- Layers of a neural network -- Challenges with neural networks -- Supervised and Unsupervised Learning -- Following the path of supervised learning -- Embracing the freedom of unsupervised learning -- Exploring Reinforcement Learning -- Reinforcement learning in e-mail marketing -- Weighing explorations against exploits -- Mastering Sequences and Time Series -- Seeing how neural networks excel at time series analysis Embracing time series features, challenges, and tools -- Developing Vision and Image Processing in AI -- Exploiting the power of convolutional neural networks (CNNs) -- Looking deeper: Advanced vision techniques -- Tools for Machine Learning and Neural Networks -- Participating with Python -- Diving into deep learning platforms -- Chapter 6 Adding Natural Language Processing and Sentiment Analysis -- Demystifying the Backbone of NLP -- Exploring linguistics for NLP -- Seeing the big picture with statistical NLP -- Why linguistics and NLP both matter -- Elevating NLP with Machine Learning -- Integrating NLP and machine learning -- Adapting to the emotional spectrum -- Examining Transformers and Attention Mechanisms -- Unpacking Sentiment Analysis -- Catching the feeling -- Understanding language nuances -- Integrating social media analytics -- Challenges for NLP and Sentiment Analysis -- Engaging Best Practices for Using NLP and Sentiment Analysis -- Chapter 7 Collaborating via Predictions, Procedures, Systems, and Filtering -- Understanding Predictive Analytics -- Using predictive analytics in various industries -- Building predictive models -- Best practices for predictive analytics -- Putting AI Procedures into Practice -- The AI System Development Lifecycle -- Understanding Filtering in AI -- Knowing where you encounter filtering -- AI filtering in recommendation systems -- Chapter 8 Getting Comfortable with Generative AI -- Changing the Game with Generative AI -- Knowing core generative AI concepts and techniques -- Reviewing the training process for generative AI models -- Getting to Know GPT Models -- Training the models is intensive -- Exploring the models' operation -- Creating New Text, Images, and Video -- Generating text -- Creating images -- Producing video -- Introducing Major Consumer-Facing Generative AI Models Addressing the Challenges of Using Generative AI Models -- Seeing the technical challenges and limitations -- Exposing ethical and societal consequences -- Part 3 Using AI to Know Customers Better -- Chapter 9 Segmentation and Persona Development -- Exploring Behavioral Segmentation Elements -- Sourcing the Right Customer Data -- Seeing How AI Performs Segmentation -- Refining, Validating, and Enhancing Segmentation Models -- Two aspects of AI model refinement -- Validation techniques -- Aligning Persona Development -- Verifying the authentic core of AI-created personas -- Ethical considerations in persona development -- Leveraging AI Personas for All Business Efforts -- Driving the customer experience -- Directing marketing with personas -- Aligning product offerings with personas -- Employing Synthetic Customer Panels -- Creating synthetic panels -- Embracing the opportunities -- Managing the risks -- Chapter 10 Lead Scoring, LTV, and Dynamic Pricing -- Working Together: Three Core Concepts -- Identifying potential leads -- Maximizing customer potential -- Adapting to market conditions on the fly -- Scoring Leads with the Help of AI -- Instilling precision with AI solutions -- Leveraging machine learning algorithms -- Achieving precision through predictive analytics -- Enhancing customer interfaces (and experiences) with AI -- Validating AI-powered lead scoring via empirical evidence -- Enhancing data analysis with AI tools -- Finding companies that offer AI-infused lead-scoring capabilities -- Calculating Lifetime Value to Affect Lead Scoring -- Allowing for predictive customer analysis -- Finding companies that offer AI-infused LTV calculations -- Turning Lead Scoring and LTV Insights into Dynamic Pricing -- Chapter 11 Churn Modeling and Measurement with AI -- Getting the Scoop on Churn Modeling -- Building your churn model Validating, calibrating, and integrating your churn model -- Improving churn insights with generative AI -- Combating churn with customer retention strategies -- Personalizing customer interactions -- Enhancing customer support -- Implementing loyalty programs -- Conducting regular feedback and follow-up initiatives -- Using exit surveys and win-back campaigns -- Ramping Up Your Measurement Operations -- Letting AI drive data collection and monitoring -- Optimizing measurement operations with AI techniques -- Incorporating visualization and reporting solutions -- Checking Out Tools for Churn Modeling and Measurement Operations -- Part 4 Transforming Brand Content and Campaign Development -- Chapter 12 Using AI for Ideation and Planning -- Engaging AI to Ideate on Behalf of Human Beings -- Deciding whether AI Hallucinations Are a Feature or a Bug -- Bringing in unexpected ideas and concepts -- Branching out with non-traditional storytelling -- Facilitating testing and experimentation -- Staying the course with generative AI -- Following Practical Steps for Idea Generation with AI -- Starting with the right prompts -- Stepping through an AI-for- ideation exercise -- Deciding on AI Ideation Tools to Use -- Chapter 13 Perfecting Prompts for Conversational Interfaces -- Reviewing Use Cases for Conversational Interfaces -- Writing Strong Prompts to Guide AI Responses -- Setting the voice and tone -- Defining a role -- Identifying the AI's task -- Specifying the format -- Good and Bad Marketing Prompt Design Examples -- Refining and Iterating Strong Prompts -- Fighting AI Bias in Prompt Writing -- Using Prompt Design Apps -- Chapter 14 Developing Creative Assets -- Trying Out an AI-Generated Where's Waldo? Illustration -- Exploring an Approach for Creating Visual Assets with AI -- Minding the integrity of your customers, data, and teams Examining an example scenario |
| ctrlnum | (ZDB-30-PQE)EBC31622257 (ZDB-30-PAD)EBC31622257 (ZDB-89-EBL)EBL31622257 (OCoLC)1453270679 (DE-599)BVBBV050101231 |
| dewey-full | 658.80028563 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.80028563 |
| dewey-search | 658.80028563 |
| dewey-sort | 3658.80028563 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
| edition | 1st ed |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nam a2200000zc 4500</leader><controlfield tag="001">BV050101231</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">241218s2024 xx o|||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781394237203</subfield><subfield code="9">978-1-394-23720-3</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-30-PQE)EBC31622257</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-30-PAD)EBC31622257</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-89-EBL)EBL31622257</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1453270679</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV050101231</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-2070s</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">658.80028563</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Singh, Shiv</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Marketing with AI for Dummies</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">1st ed</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Newark</subfield><subfield code="b">John Wiley & Sons, Incorporated</subfield><subfield code="c">2024</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2024</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (403 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Intro -- Title Page -- Copyright Page -- Table of Contents -- Introduction -- About This Book -- Foolish Assumptions -- Icons Used in This Book -- Beyond the Book -- Where to Go from Here -- Part 1 Getting Started with Marketing with AI -- Chapter 1 A Brief History of AI -- Early Technological Advances -- Alan Turing and Machine Intelligence -- The Turing Test in 1950 -- The Turing test: 1960s and beyond -- The Dartmouth Conference of 1956 -- Machine Learning and Expert Systems Emerge -- Meeting machine learning -- Examining expert systems -- An AI Winter Sets In -- The Stanford Cart: From the '60s to the '80s -- More AI Developments in the 1980s -- Rapid Advancements of AI in the 1990s and Beyond -- Watching machine learning grow up -- Playing a pivotal chess match -- Tracking the deep learning revolution -- Demonstrating intuition in the age of AI -- Creating content with generative AI -- Chapter 2 Exploring AI Business Use Cases -- Automating Customer Service -- Serving customers by using chatbots -- Resolving customer issues with virtual assistants -- Seeking out trends and solutions with sentiment analysis -- Enhancing Product and Technology with AI -- Streamlining product validation -- Simulating user experience testing -- Writing code -- Detecting and resolving software bugs -- Testing software and creating documentation -- Accelerating Research and Development -- Generating and exploring ideas -- Extracting insights from data -- Optimizing product designs and production processes -- Giving Marketing an AI Boost -- Creating coherent, consistent content -- Personalizing marketing messages -- Managing digital advertising -- Streamlining search engine optimization (SEO) -- Optimizing Sales with AI -- Driving profitability -- Nurturing leads -- Forecasting sales -- Adding AI to Legal Activities -- Analyzing documentation for legal research</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Evaluating and drafting contracts -- Performing due diligence -- Managing intellectual property -- Chapter 3 Launching into the AI Marketing Era -- Ready or Not: AI Is Your New Marketing Copilot -- Putting performance marketers at risk -- Competing with creative directors -- Watching AI Upend the Corporate World -- Taking Foundational Steps Toward AI Marketing -- Addressing the marketing dichotomy -- Assessing progress with the AI checklist -- Adopting a Strategic Framework for Entering the AI Era -- Going for liftoff -- Initiating atmospheric ascent -- Reaching escape velocity -- Dominating deep space -- Part 2 Exploring Fundamental AI Structures and Concepts -- Chapter 4 Collecting, Organizing, and Transforming Data -- Defining Data in the Context of AI -- Considering the quality of data -- Getting an appropriate quantity of data -- Choosing Data Collection Methods for Marketing with AI -- Identifying data sources and methods -- Minding data privacy and ethics -- Putting Your Marketing Data in Its Place -- Understanding Data via Manual and Automated Systems -- Preparing the Data for Use by AI Algorithms and Models -- Perfecting data by cleaning -- Transforming data -- Splitting data into subsets -- Trimming down data -- Handling imbalanced and irrelevant data -- Chapter 5 Making Connections: Machine Learning and Neural Networks -- Examining the Process of Machine Learning -- Understanding Neural Networks -- Layers of a neural network -- Challenges with neural networks -- Supervised and Unsupervised Learning -- Following the path of supervised learning -- Embracing the freedom of unsupervised learning -- Exploring Reinforcement Learning -- Reinforcement learning in e-mail marketing -- Weighing explorations against exploits -- Mastering Sequences and Time Series -- Seeing how neural networks excel at time series analysis</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Embracing time series features, challenges, and tools -- Developing Vision and Image Processing in AI -- Exploiting the power of convolutional neural networks (CNNs) -- Looking deeper: Advanced vision techniques -- Tools for Machine Learning and Neural Networks -- Participating with Python -- Diving into deep learning platforms -- Chapter 6 Adding Natural Language Processing and Sentiment Analysis -- Demystifying the Backbone of NLP -- Exploring linguistics for NLP -- Seeing the big picture with statistical NLP -- Why linguistics and NLP both matter -- Elevating NLP with Machine Learning -- Integrating NLP and machine learning -- Adapting to the emotional spectrum -- Examining Transformers and Attention Mechanisms -- Unpacking Sentiment Analysis -- Catching the feeling -- Understanding language nuances -- Integrating social media analytics -- Challenges for NLP and Sentiment Analysis -- Engaging Best Practices for Using NLP and Sentiment Analysis -- Chapter 7 Collaborating via Predictions, Procedures, Systems, and Filtering -- Understanding Predictive Analytics -- Using predictive analytics in various industries -- Building predictive models -- Best practices for predictive analytics -- Putting AI Procedures into Practice -- The AI System Development Lifecycle -- Understanding Filtering in AI -- Knowing where you encounter filtering -- AI filtering in recommendation systems -- Chapter 8 Getting Comfortable with Generative AI -- Changing the Game with Generative AI -- Knowing core generative AI concepts and techniques -- Reviewing the training process for generative AI models -- Getting to Know GPT Models -- Training the models is intensive -- Exploring the models' operation -- Creating New Text, Images, and Video -- Generating text -- Creating images -- Producing video -- Introducing Major Consumer-Facing Generative AI Models</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Addressing the Challenges of Using Generative AI Models -- Seeing the technical challenges and limitations -- Exposing ethical and societal consequences -- Part 3 Using AI to Know Customers Better -- Chapter 9 Segmentation and Persona Development -- Exploring Behavioral Segmentation Elements -- Sourcing the Right Customer Data -- Seeing How AI Performs Segmentation -- Refining, Validating, and Enhancing Segmentation Models -- Two aspects of AI model refinement -- Validation techniques -- Aligning Persona Development -- Verifying the authentic core of AI-created personas -- Ethical considerations in persona development -- Leveraging AI Personas for All Business Efforts -- Driving the customer experience -- Directing marketing with personas -- Aligning product offerings with personas -- Employing Synthetic Customer Panels -- Creating synthetic panels -- Embracing the opportunities -- Managing the risks -- Chapter 10 Lead Scoring, LTV, and Dynamic Pricing -- Working Together: Three Core Concepts -- Identifying potential leads -- Maximizing customer potential -- Adapting to market conditions on the fly -- Scoring Leads with the Help of AI -- Instilling precision with AI solutions -- Leveraging machine learning algorithms -- Achieving precision through predictive analytics -- Enhancing customer interfaces (and experiences) with AI -- Validating AI-powered lead scoring via empirical evidence -- Enhancing data analysis with AI tools -- Finding companies that offer AI-infused lead-scoring capabilities -- Calculating Lifetime Value to Affect Lead Scoring -- Allowing for predictive customer analysis -- Finding companies that offer AI-infused LTV calculations -- Turning Lead Scoring and LTV Insights into Dynamic Pricing -- Chapter 11 Churn Modeling and Measurement with AI -- Getting the Scoop on Churn Modeling -- Building your churn model</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Validating, calibrating, and integrating your churn model -- Improving churn insights with generative AI -- Combating churn with customer retention strategies -- Personalizing customer interactions -- Enhancing customer support -- Implementing loyalty programs -- Conducting regular feedback and follow-up initiatives -- Using exit surveys and win-back campaigns -- Ramping Up Your Measurement Operations -- Letting AI drive data collection and monitoring -- Optimizing measurement operations with AI techniques -- Incorporating visualization and reporting solutions -- Checking Out Tools for Churn Modeling and Measurement Operations -- Part 4 Transforming Brand Content and Campaign Development -- Chapter 12 Using AI for Ideation and Planning -- Engaging AI to Ideate on Behalf of Human Beings -- Deciding whether AI Hallucinations Are a Feature or a Bug -- Bringing in unexpected ideas and concepts -- Branching out with non-traditional storytelling -- Facilitating testing and experimentation -- Staying the course with generative AI -- Following Practical Steps for Idea Generation with AI -- Starting with the right prompts -- Stepping through an AI-for- ideation exercise -- Deciding on AI Ideation Tools to Use -- Chapter 13 Perfecting Prompts for Conversational Interfaces -- Reviewing Use Cases for Conversational Interfaces -- Writing Strong Prompts to Guide AI Responses -- Setting the voice and tone -- Defining a role -- Identifying the AI's task -- Specifying the format -- Good and Bad Marketing Prompt Design Examples -- Refining and Iterating Strong Prompts -- Fighting AI Bias in Prompt Writing -- Using Prompt Design Apps -- Chapter 14 Developing Creative Assets -- Trying Out an AI-Generated Where's Waldo? Illustration -- Exploring an Approach for Creating Visual Assets with AI -- Minding the integrity of your customers, data, and teams</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Examining an example scenario</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial intelligence</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="a">Singh, Shiv</subfield><subfield code="t">Marketing with AI for Dummies</subfield><subfield code="d">Newark : John Wiley & Sons, Incorporated,c2024</subfield><subfield code="z">9781394237197</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-PQE</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-035438393</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=31622257</subfield><subfield code="l">DE-2070s</subfield><subfield code="p">ZDB-30-PQE</subfield><subfield code="q">HWR_PDA_PQE</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV050101231 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-18T07:00:35Z |
| institution | BVB |
| isbn | 9781394237203 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035438393 |
| oclc_num | 1453270679 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (403 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | John Wiley & Sons, Incorporated |
| record_format | marc |
| spelling | Singh, Shiv Verfasser aut Marketing with AI for Dummies 1st ed Newark John Wiley & Sons, Incorporated 2024 ©2024 1 Online-Ressource (403 Seiten) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Intro -- Title Page -- Copyright Page -- Table of Contents -- Introduction -- About This Book -- Foolish Assumptions -- Icons Used in This Book -- Beyond the Book -- Where to Go from Here -- Part 1 Getting Started with Marketing with AI -- Chapter 1 A Brief History of AI -- Early Technological Advances -- Alan Turing and Machine Intelligence -- The Turing Test in 1950 -- The Turing test: 1960s and beyond -- The Dartmouth Conference of 1956 -- Machine Learning and Expert Systems Emerge -- Meeting machine learning -- Examining expert systems -- An AI Winter Sets In -- The Stanford Cart: From the '60s to the '80s -- More AI Developments in the 1980s -- Rapid Advancements of AI in the 1990s and Beyond -- Watching machine learning grow up -- Playing a pivotal chess match -- Tracking the deep learning revolution -- Demonstrating intuition in the age of AI -- Creating content with generative AI -- Chapter 2 Exploring AI Business Use Cases -- Automating Customer Service -- Serving customers by using chatbots -- Resolving customer issues with virtual assistants -- Seeking out trends and solutions with sentiment analysis -- Enhancing Product and Technology with AI -- Streamlining product validation -- Simulating user experience testing -- Writing code -- Detecting and resolving software bugs -- Testing software and creating documentation -- Accelerating Research and Development -- Generating and exploring ideas -- Extracting insights from data -- Optimizing product designs and production processes -- Giving Marketing an AI Boost -- Creating coherent, consistent content -- Personalizing marketing messages -- Managing digital advertising -- Streamlining search engine optimization (SEO) -- Optimizing Sales with AI -- Driving profitability -- Nurturing leads -- Forecasting sales -- Adding AI to Legal Activities -- Analyzing documentation for legal research Evaluating and drafting contracts -- Performing due diligence -- Managing intellectual property -- Chapter 3 Launching into the AI Marketing Era -- Ready or Not: AI Is Your New Marketing Copilot -- Putting performance marketers at risk -- Competing with creative directors -- Watching AI Upend the Corporate World -- Taking Foundational Steps Toward AI Marketing -- Addressing the marketing dichotomy -- Assessing progress with the AI checklist -- Adopting a Strategic Framework for Entering the AI Era -- Going for liftoff -- Initiating atmospheric ascent -- Reaching escape velocity -- Dominating deep space -- Part 2 Exploring Fundamental AI Structures and Concepts -- Chapter 4 Collecting, Organizing, and Transforming Data -- Defining Data in the Context of AI -- Considering the quality of data -- Getting an appropriate quantity of data -- Choosing Data Collection Methods for Marketing with AI -- Identifying data sources and methods -- Minding data privacy and ethics -- Putting Your Marketing Data in Its Place -- Understanding Data via Manual and Automated Systems -- Preparing the Data for Use by AI Algorithms and Models -- Perfecting data by cleaning -- Transforming data -- Splitting data into subsets -- Trimming down data -- Handling imbalanced and irrelevant data -- Chapter 5 Making Connections: Machine Learning and Neural Networks -- Examining the Process of Machine Learning -- Understanding Neural Networks -- Layers of a neural network -- Challenges with neural networks -- Supervised and Unsupervised Learning -- Following the path of supervised learning -- Embracing the freedom of unsupervised learning -- Exploring Reinforcement Learning -- Reinforcement learning in e-mail marketing -- Weighing explorations against exploits -- Mastering Sequences and Time Series -- Seeing how neural networks excel at time series analysis Embracing time series features, challenges, and tools -- Developing Vision and Image Processing in AI -- Exploiting the power of convolutional neural networks (CNNs) -- Looking deeper: Advanced vision techniques -- Tools for Machine Learning and Neural Networks -- Participating with Python -- Diving into deep learning platforms -- Chapter 6 Adding Natural Language Processing and Sentiment Analysis -- Demystifying the Backbone of NLP -- Exploring linguistics for NLP -- Seeing the big picture with statistical NLP -- Why linguistics and NLP both matter -- Elevating NLP with Machine Learning -- Integrating NLP and machine learning -- Adapting to the emotional spectrum -- Examining Transformers and Attention Mechanisms -- Unpacking Sentiment Analysis -- Catching the feeling -- Understanding language nuances -- Integrating social media analytics -- Challenges for NLP and Sentiment Analysis -- Engaging Best Practices for Using NLP and Sentiment Analysis -- Chapter 7 Collaborating via Predictions, Procedures, Systems, and Filtering -- Understanding Predictive Analytics -- Using predictive analytics in various industries -- Building predictive models -- Best practices for predictive analytics -- Putting AI Procedures into Practice -- The AI System Development Lifecycle -- Understanding Filtering in AI -- Knowing where you encounter filtering -- AI filtering in recommendation systems -- Chapter 8 Getting Comfortable with Generative AI -- Changing the Game with Generative AI -- Knowing core generative AI concepts and techniques -- Reviewing the training process for generative AI models -- Getting to Know GPT Models -- Training the models is intensive -- Exploring the models' operation -- Creating New Text, Images, and Video -- Generating text -- Creating images -- Producing video -- Introducing Major Consumer-Facing Generative AI Models Addressing the Challenges of Using Generative AI Models -- Seeing the technical challenges and limitations -- Exposing ethical and societal consequences -- Part 3 Using AI to Know Customers Better -- Chapter 9 Segmentation and Persona Development -- Exploring Behavioral Segmentation Elements -- Sourcing the Right Customer Data -- Seeing How AI Performs Segmentation -- Refining, Validating, and Enhancing Segmentation Models -- Two aspects of AI model refinement -- Validation techniques -- Aligning Persona Development -- Verifying the authentic core of AI-created personas -- Ethical considerations in persona development -- Leveraging AI Personas for All Business Efforts -- Driving the customer experience -- Directing marketing with personas -- Aligning product offerings with personas -- Employing Synthetic Customer Panels -- Creating synthetic panels -- Embracing the opportunities -- Managing the risks -- Chapter 10 Lead Scoring, LTV, and Dynamic Pricing -- Working Together: Three Core Concepts -- Identifying potential leads -- Maximizing customer potential -- Adapting to market conditions on the fly -- Scoring Leads with the Help of AI -- Instilling precision with AI solutions -- Leveraging machine learning algorithms -- Achieving precision through predictive analytics -- Enhancing customer interfaces (and experiences) with AI -- Validating AI-powered lead scoring via empirical evidence -- Enhancing data analysis with AI tools -- Finding companies that offer AI-infused lead-scoring capabilities -- Calculating Lifetime Value to Affect Lead Scoring -- Allowing for predictive customer analysis -- Finding companies that offer AI-infused LTV calculations -- Turning Lead Scoring and LTV Insights into Dynamic Pricing -- Chapter 11 Churn Modeling and Measurement with AI -- Getting the Scoop on Churn Modeling -- Building your churn model Validating, calibrating, and integrating your churn model -- Improving churn insights with generative AI -- Combating churn with customer retention strategies -- Personalizing customer interactions -- Enhancing customer support -- Implementing loyalty programs -- Conducting regular feedback and follow-up initiatives -- Using exit surveys and win-back campaigns -- Ramping Up Your Measurement Operations -- Letting AI drive data collection and monitoring -- Optimizing measurement operations with AI techniques -- Incorporating visualization and reporting solutions -- Checking Out Tools for Churn Modeling and Measurement Operations -- Part 4 Transforming Brand Content and Campaign Development -- Chapter 12 Using AI for Ideation and Planning -- Engaging AI to Ideate on Behalf of Human Beings -- Deciding whether AI Hallucinations Are a Feature or a Bug -- Bringing in unexpected ideas and concepts -- Branching out with non-traditional storytelling -- Facilitating testing and experimentation -- Staying the course with generative AI -- Following Practical Steps for Idea Generation with AI -- Starting with the right prompts -- Stepping through an AI-for- ideation exercise -- Deciding on AI Ideation Tools to Use -- Chapter 13 Perfecting Prompts for Conversational Interfaces -- Reviewing Use Cases for Conversational Interfaces -- Writing Strong Prompts to Guide AI Responses -- Setting the voice and tone -- Defining a role -- Identifying the AI's task -- Specifying the format -- Good and Bad Marketing Prompt Design Examples -- Refining and Iterating Strong Prompts -- Fighting AI Bias in Prompt Writing -- Using Prompt Design Apps -- Chapter 14 Developing Creative Assets -- Trying Out an AI-Generated Where's Waldo? Illustration -- Exploring an Approach for Creating Visual Assets with AI -- Minding the integrity of your customers, data, and teams Examining an example scenario Artificial intelligence Erscheint auch als Druck-Ausgabe Singh, Shiv Marketing with AI for Dummies Newark : John Wiley & Sons, Incorporated,c2024 9781394237197 |
| spellingShingle | Singh, Shiv Marketing with AI for Dummies Intro -- Title Page -- Copyright Page -- Table of Contents -- Introduction -- About This Book -- Foolish Assumptions -- Icons Used in This Book -- Beyond the Book -- Where to Go from Here -- Part 1 Getting Started with Marketing with AI -- Chapter 1 A Brief History of AI -- Early Technological Advances -- Alan Turing and Machine Intelligence -- The Turing Test in 1950 -- The Turing test: 1960s and beyond -- The Dartmouth Conference of 1956 -- Machine Learning and Expert Systems Emerge -- Meeting machine learning -- Examining expert systems -- An AI Winter Sets In -- The Stanford Cart: From the '60s to the '80s -- More AI Developments in the 1980s -- Rapid Advancements of AI in the 1990s and Beyond -- Watching machine learning grow up -- Playing a pivotal chess match -- Tracking the deep learning revolution -- Demonstrating intuition in the age of AI -- Creating content with generative AI -- Chapter 2 Exploring AI Business Use Cases -- Automating Customer Service -- Serving customers by using chatbots -- Resolving customer issues with virtual assistants -- Seeking out trends and solutions with sentiment analysis -- Enhancing Product and Technology with AI -- Streamlining product validation -- Simulating user experience testing -- Writing code -- Detecting and resolving software bugs -- Testing software and creating documentation -- Accelerating Research and Development -- Generating and exploring ideas -- Extracting insights from data -- Optimizing product designs and production processes -- Giving Marketing an AI Boost -- Creating coherent, consistent content -- Personalizing marketing messages -- Managing digital advertising -- Streamlining search engine optimization (SEO) -- Optimizing Sales with AI -- Driving profitability -- Nurturing leads -- Forecasting sales -- Adding AI to Legal Activities -- Analyzing documentation for legal research Evaluating and drafting contracts -- Performing due diligence -- Managing intellectual property -- Chapter 3 Launching into the AI Marketing Era -- Ready or Not: AI Is Your New Marketing Copilot -- Putting performance marketers at risk -- Competing with creative directors -- Watching AI Upend the Corporate World -- Taking Foundational Steps Toward AI Marketing -- Addressing the marketing dichotomy -- Assessing progress with the AI checklist -- Adopting a Strategic Framework for Entering the AI Era -- Going for liftoff -- Initiating atmospheric ascent -- Reaching escape velocity -- Dominating deep space -- Part 2 Exploring Fundamental AI Structures and Concepts -- Chapter 4 Collecting, Organizing, and Transforming Data -- Defining Data in the Context of AI -- Considering the quality of data -- Getting an appropriate quantity of data -- Choosing Data Collection Methods for Marketing with AI -- Identifying data sources and methods -- Minding data privacy and ethics -- Putting Your Marketing Data in Its Place -- Understanding Data via Manual and Automated Systems -- Preparing the Data for Use by AI Algorithms and Models -- Perfecting data by cleaning -- Transforming data -- Splitting data into subsets -- Trimming down data -- Handling imbalanced and irrelevant data -- Chapter 5 Making Connections: Machine Learning and Neural Networks -- Examining the Process of Machine Learning -- Understanding Neural Networks -- Layers of a neural network -- Challenges with neural networks -- Supervised and Unsupervised Learning -- Following the path of supervised learning -- Embracing the freedom of unsupervised learning -- Exploring Reinforcement Learning -- Reinforcement learning in e-mail marketing -- Weighing explorations against exploits -- Mastering Sequences and Time Series -- Seeing how neural networks excel at time series analysis Embracing time series features, challenges, and tools -- Developing Vision and Image Processing in AI -- Exploiting the power of convolutional neural networks (CNNs) -- Looking deeper: Advanced vision techniques -- Tools for Machine Learning and Neural Networks -- Participating with Python -- Diving into deep learning platforms -- Chapter 6 Adding Natural Language Processing and Sentiment Analysis -- Demystifying the Backbone of NLP -- Exploring linguistics for NLP -- Seeing the big picture with statistical NLP -- Why linguistics and NLP both matter -- Elevating NLP with Machine Learning -- Integrating NLP and machine learning -- Adapting to the emotional spectrum -- Examining Transformers and Attention Mechanisms -- Unpacking Sentiment Analysis -- Catching the feeling -- Understanding language nuances -- Integrating social media analytics -- Challenges for NLP and Sentiment Analysis -- Engaging Best Practices for Using NLP and Sentiment Analysis -- Chapter 7 Collaborating via Predictions, Procedures, Systems, and Filtering -- Understanding Predictive Analytics -- Using predictive analytics in various industries -- Building predictive models -- Best practices for predictive analytics -- Putting AI Procedures into Practice -- The AI System Development Lifecycle -- Understanding Filtering in AI -- Knowing where you encounter filtering -- AI filtering in recommendation systems -- Chapter 8 Getting Comfortable with Generative AI -- Changing the Game with Generative AI -- Knowing core generative AI concepts and techniques -- Reviewing the training process for generative AI models -- Getting to Know GPT Models -- Training the models is intensive -- Exploring the models' operation -- Creating New Text, Images, and Video -- Generating text -- Creating images -- Producing video -- Introducing Major Consumer-Facing Generative AI Models Addressing the Challenges of Using Generative AI Models -- Seeing the technical challenges and limitations -- Exposing ethical and societal consequences -- Part 3 Using AI to Know Customers Better -- Chapter 9 Segmentation and Persona Development -- Exploring Behavioral Segmentation Elements -- Sourcing the Right Customer Data -- Seeing How AI Performs Segmentation -- Refining, Validating, and Enhancing Segmentation Models -- Two aspects of AI model refinement -- Validation techniques -- Aligning Persona Development -- Verifying the authentic core of AI-created personas -- Ethical considerations in persona development -- Leveraging AI Personas for All Business Efforts -- Driving the customer experience -- Directing marketing with personas -- Aligning product offerings with personas -- Employing Synthetic Customer Panels -- Creating synthetic panels -- Embracing the opportunities -- Managing the risks -- Chapter 10 Lead Scoring, LTV, and Dynamic Pricing -- Working Together: Three Core Concepts -- Identifying potential leads -- Maximizing customer potential -- Adapting to market conditions on the fly -- Scoring Leads with the Help of AI -- Instilling precision with AI solutions -- Leveraging machine learning algorithms -- Achieving precision through predictive analytics -- Enhancing customer interfaces (and experiences) with AI -- Validating AI-powered lead scoring via empirical evidence -- Enhancing data analysis with AI tools -- Finding companies that offer AI-infused lead-scoring capabilities -- Calculating Lifetime Value to Affect Lead Scoring -- Allowing for predictive customer analysis -- Finding companies that offer AI-infused LTV calculations -- Turning Lead Scoring and LTV Insights into Dynamic Pricing -- Chapter 11 Churn Modeling and Measurement with AI -- Getting the Scoop on Churn Modeling -- Building your churn model Validating, calibrating, and integrating your churn model -- Improving churn insights with generative AI -- Combating churn with customer retention strategies -- Personalizing customer interactions -- Enhancing customer support -- Implementing loyalty programs -- Conducting regular feedback and follow-up initiatives -- Using exit surveys and win-back campaigns -- Ramping Up Your Measurement Operations -- Letting AI drive data collection and monitoring -- Optimizing measurement operations with AI techniques -- Incorporating visualization and reporting solutions -- Checking Out Tools for Churn Modeling and Measurement Operations -- Part 4 Transforming Brand Content and Campaign Development -- Chapter 12 Using AI for Ideation and Planning -- Engaging AI to Ideate on Behalf of Human Beings -- Deciding whether AI Hallucinations Are a Feature or a Bug -- Bringing in unexpected ideas and concepts -- Branching out with non-traditional storytelling -- Facilitating testing and experimentation -- Staying the course with generative AI -- Following Practical Steps for Idea Generation with AI -- Starting with the right prompts -- Stepping through an AI-for- ideation exercise -- Deciding on AI Ideation Tools to Use -- Chapter 13 Perfecting Prompts for Conversational Interfaces -- Reviewing Use Cases for Conversational Interfaces -- Writing Strong Prompts to Guide AI Responses -- Setting the voice and tone -- Defining a role -- Identifying the AI's task -- Specifying the format -- Good and Bad Marketing Prompt Design Examples -- Refining and Iterating Strong Prompts -- Fighting AI Bias in Prompt Writing -- Using Prompt Design Apps -- Chapter 14 Developing Creative Assets -- Trying Out an AI-Generated Where's Waldo? Illustration -- Exploring an Approach for Creating Visual Assets with AI -- Minding the integrity of your customers, data, and teams Examining an example scenario Artificial intelligence |
| title | Marketing with AI for Dummies |
| title_auth | Marketing with AI for Dummies |
| title_exact_search | Marketing with AI for Dummies |
| title_full | Marketing with AI for Dummies |
| title_fullStr | Marketing with AI for Dummies |
| title_full_unstemmed | Marketing with AI for Dummies |
| title_short | Marketing with AI for Dummies |
| title_sort | marketing with ai for dummies |
| topic | Artificial intelligence |
| topic_facet | Artificial intelligence |
| work_keys_str_mv | AT singhshiv marketingwithaifordummies |