Marketing With AI For Dummies 2

Marketing With AI For Dummies

Shiv Singh

📚 GENRE: Business & Finance

📃 PAGES: 400

✅ COMPLETED: January 25, 2025

🧐 RATING: ⭐⭐⭐

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Short Summary

Artificial Intelligence is transforming the way businesses reach customers and prospects. In Marketing With AI for Dummies, Shiv Singh takes a look at how marketing professionals can leverage the power of AI to increase productivity and enhance the overall customer experience.

Key Takeaways

1️⃣ Data Drives AI — All AI models are dependent on data. AI models trained on a large amount of good, high-quality data will produce strong results. Chat GPT, for example, is a generative AI model trained on articles, books, and other content on the Internet. It draws on this data to generate answers to prompts. AI models trained on incomplete and biased information will produce poor results. Everything comes back to the data an AI model is trained on. 

2️⃣ The Heart of AI: Machine Learning — Machine Learning is a key feature of AI. It’s basically the process of teaching computers to learn from experience, and it’s the driver behind so many AI features we enjoy today. FaceID on an iPhone is an example — you’ve used machine learning to train the phone to recognize your face. Song recommendations on Spotify, show suggestions on Netflix, Amazon product recommendations, Siri on your iPhone, the content that shows up on your social media feed — these are just a few other examples of machine learning in action. Your behavior on these platforms, in addition to behavior from other users, is the data that AI — driven by machine learning — analyzes and acts on to provide recommendations. 

3️⃣ Generative AI — Because a lot of what marketing professionals do involves content creation, Generative AI has become a major asset. Generative AI is a subset of AI that focuses on creating new content, ranging from text and images to music, videos, code, and much more. Machine Learning (ML) is at the heart of Generative AI models, which are also known as Large Language Models (LLMs). These models are trained on huge data sets (e.g. web text, books, articles, etc.) and use “deep learning” (a type of machine learning) to predict the most appropriate words, phrases, or images based on your request or input. That’s really all ChatGPT is — a giant, well-trained word predictor that understands context. 

Favorite Quote

“In the world of artificial intelligence (AI), data acts as the vital force driving machine learning (ML) algorithms. Data lays the foundation for the construction of Al systems, and you need a sufficient quality and quantity of data to make sure that your Al programs perform successfully. Data is the fuel that trains the Al to discern patterns and interpret the inputs provided”

Introduction

  • About the Book — Marketing With AI For Dummies outlines the various AI tools being used to redefine the field of marketing. AI’s capabilities are vast, and it will be important for those working in marketing to get an understanding of how these resources can help them better connect with customers and prospects. This book seeks to do exactly that. 
  • About the Author — Shiv Singh is a marketing aficionado. He has served as Head of Digital for PepsiCo Beverages, SVP Innovation Go-to-Market for Visa, and Chief Marketing and Customer Experience Officer at LendingTree, where he managed a media budget of $650 million and led a team of 150 marketers. He brings a lot of credibility to this topic. 

Ch. 1: A Brief History of AI

  • The Rise of AI — Although AI exploded onto the scene in the early 2020s, its rise dates back decades. In fact, the term artificial intelligence was first coined at the Dartmouth Conference in 1956. Over the years, it has slowly advanced as researchers have developed and harnessed machine learning and deep learning. Heads were turned when robots powered by AI defeated the world’s best chess player (1997) and Go player (2016.)
  • Machine Learning — At the heart of most AI systems is something called machine learning. Machine learning emerged as a field of research following the Dartmouth Conference of 1956 and represented a major shift in approach that aimed to build systems that learn from data, rather than by following programmed scripts. Machine learning relies on algorithms to analyze, interpret, and produce results based on data. It can identify patterns, make decisions, and predict outcomes based on huge sets of existing data. This technology is behind things like Netflix show recommendations, Amazon product suggestions, and more. Large language models like ChatGPT are powered by a specialized branch of machine learning called deep learning. Again, this idea of learning from a data set is much different than the idea of programming through written code. Machine learning represented a big shift in approach from traditional computing methods. 

Ch. 2: Exploring AI Business Use Cases

  • Generative AI & Data Sets — Generative AI is a branch of AI that enables you to create new content (such as text, images, and music) by learning from existing data. It is the most significant development in the history of Al. What’s important to realize about AI, including generative AI, is that it is trained using data sets. These data sets could be numbers, text, or anything else you want to feed it. The technology then draws on the data when you ask it something. Granular data sets lead to granular outputs. For example, Pizza Hut could train its ChatGPT AI model using copy from its website and database. Because it’s been trained on Pizza Hut copy, employees can have ChatGPT spit out text in Pizza Hut’s brand voice. As this chapter outlines, there is almost no area of business that can’t benefit from generative AI. 
  • AI Use Case: Customer Service — Enhancing customer service is one of the ways companies are using AI. Customer service typically involves humans answering phones or emails to address customer questions. AI can be trained to do this. It can analyze questions, deliver personalized recommendations, and improve overall support. Below are a few ways AI is being used in the area of customer service.
    • Chatbots — Chatbots are applications designed to simulate human-like conversations based on user inputs. Generative AI can be used to power chatbots. Natural Language Processing (NLP) enables the chatbot to read and analyze the question, identify the intent, and locate the resources needed to assist. Often, this just requires using a ChatGPT plug-in, but there are other vendors available as well. You then train the AI chatbot using an internal customer service database and other company data. The result is a chatbot agent that continually improves with each customer interaction. Each customer interaction acts like a new data set, so the bot gets stronger over time as it gains experience. We’re starting to see companies everywhere using AI chatbots — Chipotle’s ‘Pepper’ chatbot is an example. These chatbots are being used on company websites, apps, and even on the phones. 
    • Virtual Assistant — AI can be used to do a lot of administrative work, such as answering emails, setting appointments, resolving customer complaints, and identifying optimal products and services based on an individual’s needs. AI can also sit in on meetings and take detailed notes for you. Think of it as a personal assistant in your office. 
    • Identifying Trends — It can be difficult to sift through a mountain of customer inputs to find key trends and patterns. AI is excellent at this. Surveys and customer reviews are good examples — sometimes, you get a ton of responses. It can take a lot of time to read through all of them and uncover common themes. By feeding the survey responses and customer reviews to AI, you can get a general overview of the responses in a matter of seconds. Amazon does this with the customer review section toward the bottom of every product page. The AI bot summarizes the entire set of customer reviews in a few sentences (“Buyers find this product to be . . .”). This is great for people who work in product or service development. Feedback from customers is a valuable development tool, and AI can help developers understand what customers are saying very quickly.  
  • AI Use Case: Enhancing Products and Technology — AI can be used to assist with product and technology development. It is being used to write code, test new products before launch, simulate user experiences in a testing environment, detect software bugs, experiment with refinements, and much more. The value here is being able to enhance your testing capabilities before launching to the customer. Companies now have a much easier, efficient, and better way to test products and services before they introduce them to the public, leading to fewer errors and a more enjoyable customer experience. 
  • AI Use Case: Research and Idea Generation — AI is very good at research. Models like ChatGPT can scan the Internet, read through dozens and dozens of articles on a topic, and deliver a concise summary of its findings — all in a matter of seconds. This process takes hours for humans to complete. Another area AI excels at is idea generation. A few minutes of tinkering with AI can deliver an almost infinite number of ideas on anything you’re working on, whether you’re trying to create headlines for an article, develop a new product, or anything else. There’s really no reason not to use this technology. The idea of a co-pilot or personal assistant is a great way to think of AI’s role in making humans more efficient. 
  • AI Use Case: Data Analysis and Predictive Analytics — AI can scan large data sets and deliver patterns, trends, and other insights based on its analysis. This process takes hours for humans. It can also give you predictive analytics based on historical data. Predictive analytics refers to the process of using your existing data (e.g. historical sales numbers) to predict future outcomes. In other words, this is forecasting. 
  • AI Use Case: Marketing — Marketing is perhaps where generative AI is having the most impact. From crafting blog posts, whitepapers, targeted e-mails, brochures, social media posts, and other text-based communications, to developing video scripts, campaign taglines, graphics, and product imagery, generative Al is outstanding at creating and optimizing content. It’s also an incredible idea generation tool. There’s almost no area of marketing that AI can’t enhance. Below are a few ways other ways it can be used:
    • Personalized Messaging — Using your CRM as its data set, AI can analyze customer journeys, purchasing histories, and interaction patterns to develop personalized emails and offers that are most likely to resonate with each of your customers. This not only increases the likelihood of somebody buying from you, but it also enhances the overall customer experience. 
    • Digital Advertising — AI can be used to create ad copy and serve ads to the right group of people, at the right time. Al tools can also swiftly assess the performance of your ads and adjust in real time to ensure that you’re serving ads that are most likely to achieve your desired outcome (whether you want clicks, conversions, or some other metric). In this way, AI helps optimize and execute your digital advertising. 
    • SEO — AI can review your final content and suggest ways to optimize it for better SEO results. It can also support your keyword research process. 
  • Chapter Takeaway — There’s really no reason not to use generative AI. From sales and marketing to customer service and product development, it can be used to enhance almost every area of a business. Its ability to conduct research, identify trends and patterns within surveys and large data sets, and produce valuable written content in seconds will save you a ton of time.

Ch. 3: Launching Into the Marketing Era

  • Adopting AI — You should approach AI as if it is a personal assistant that stands ready to help you with any project you’re working on. Ask it to do research. Ask it to help you write some copy. Ask it to review your work and provide suggestions for enhancement. AI will make you more productive and efficient across the board. Put it to use. If you don’t, you’re falling behind the competition. 

Ch. 4: Collecting, Organizing, and Transforming Data

  • Data Drives AI — AI and its machine learning algorithms are trained on data. The quality and quantity of data you feed to AI are critically important. If the data is biased, incomplete, or full of errors, the output you receive will be biased, incomplete, and full of errors. Data isn’t limited to numbers in a spreadsheet; it can be almost anything: text, images, videos, books, a company’s brand style guide and sales figures, survey responses, customer reviews, and anything else you can compile. ChatGPT for example, is trained on Internet content, books, and published articles. When you ask it something, it sifts through the data it’s been trained on to generate a response. People around the world are building their own AI models using data that is unique to them and what they are trying to achieve. For example, if somebody trained an AI model using Pizza Hut website copy, they could generate outputs that are consistently written in Pizza Hut’s unique brand voice.
    • Quote (P. 63): “In the world of artificial intelligence (AI), data acts as the vital force driving machine learning (ML) algorithms. Data lays the foundation for the construction of Al systems, and you need a sufficient quality and quantity of data to make sure that your Al programs perform successfully. Data is the fuel that trains the Al to discern patterns and interpret the inputs provided.”
    • Quote (P. 64): “When you talk about data in the Al realm, you may just think about numbers, text, and code. But the data discussion encompasses so much more. Data can include that song you heard this morning, the video clip you shared with a friend, or even this book’s text that you’re reading right now. You have data all around you, and Al feeds on that data to make smart decisions for you.”
    • Quote (P. 64): “Data can take various forms, including text, images, videos, audio, structured tables, and more. The quality and quantity of data largely determine the performance and reliability of Al systems.”
  • Chapter Takeaway — AI is only as good as the data it has been trained on. If the model is trained using crappy data sets, it will generate crappy outputs. Garbage in, garbage out. This is why it’s important to understand what kind of data an AI model has been trained on.

Ch. 5: Making Connections — Machine Learning and Neural Networks

  • What Is Machine Learning (ML)? — Machine learning is a subset of artificial intelligence where computers learn from the data they encounter, rather than being programmed to behave in a certain way. Machine learning acts in much the same way a child does when learning something new. For example, when children are learning about fruits, they are shown a particular fruit and told what it is. Over time, children begin to recognize fruit without any help. With ML, scientists provide data (e.g. photos of fruits) and corresponding labels (e.g. names of fruits) to a computer, and the computer begins to recognize the data on its own. 
  • Machine Learning Is Everywhere — ML is basically the process of teaching computers to learn from experience, and it’s the driver behind so many AI features we enjoy today. The FaceID on an iPhone is an example — you’ve used ML to train the phone to recognize your face. Song recommendations on Spotify, show suggestions on Netflix, Amazon product recommendations, Siri on your iPhone, the content that shows up on your social media feed — these are just a few other examples of ML in action. Your behavior on these platforms, in addition to behavior from other users, is the data that AI — driven by ML — analyzes and acts on to provide recommendations.
    • Quote (P. 79): “Machine learning is everywhere! From recommending songs on music platforms, to predicting weather, to diagnosing diseases, to assisting in financial decisions, to powering voice assistants on smartphones — ML touches various aspects of our daily lives and has for a while. For example, if you use the music service Spotify, you get the benefit of its machine learning algorithms that help the service recommend songs you may like. Machine learning algorithms enhance marketing programs by providing personalized product recommendations based on customer behavior.”
  • Deep Learning: A Subset of ML — Deep Learning is a subset of machine learning that uses deep deep neural networks, which are layers of connected “neurons” whose connections have parameters or weights that can be trained. It is especially effective at learning from unstructured data such as images, text, and audio. 
  • Neural Networks and Training ML — Neural networks are sets of algorithms designed to recognize patterns. These networks mimic the human brain’s functionality and represent one of the ways ML is able to learn from experience. There are three main ways to train neural networks, and therefore ML. These are:
    • Supervised Learning — The example of training ML by showing photos of fruit with corresponding labels is supervised learning. This is the process of training the AI model to recognize certain things. You’re giving the model the answers. 
    • Unsupervised Learning — With unsupervised learning, the algorithm in the AI model receives data without any instructions about what the data represents or what to do with it. The model tries to learn the patterns and structure of the data without receiving any labeled responses to guide its learning. You’re hiding the answers from the model. 
    • Reinforcement Learning — This is a type of ML where AI is trained to make decisions by receiving rewards or penalties for its actions. This is the type of learning that drives song, show, and product recommendations. It is also responsible for the content that shows up on your social media feeds (e.g. X and TikTok). Reinforcement training either rewards or penalizes the AI model based on a user’s interactions. Over time, the model learns which actions generate rewards. A good example of this involves social media feeds: when you sit through a full video on TikTok or engage with certain posts on X, the AI driving these platforms begins to learn what you like and will give you more of it (i.e. reward). When you skip over videos and posts, it learns what you don’t like and gives you less of it (i.e. penalty). The same concept applies with Spotify and Netflix recommendations; these AI models are using reinforcement training to predict what you will want to consume next. The goal is to make these platforms as addictive as possible. Reinforcement training makes AI models highly adaptive.
      • Quote (P. 85): “And if you’re wondering how impactful reinforcement learning is, take a look at Netflix, which uses reinforcement learning (among other techniques). The company estimates that its recommendation system — which balances exploration and exploitation — saves it over $1 billion a year by reducing customer churn and enhancing user satisfaction. Needless to say, Netflix also uses reinforcement learning to optimize the e-mails that it sends to its customers.”
  • What Is Machine Vision? — Machine vision is a branch of AI that enables computers to interpret and analyze visual data from the world, such as images or videos. It works by using cameras to capture visual input, which is then processed through advanced algorithms and ML models to recognize patterns, objects, and actions. For example, in self-driving cars, machine vision is a critical component that helps the vehicle “see” its surroundings. Cameras mounted on the car feed visual data to the AI system, which identifies road signs, traffic lights, pedestrians, and other vehicles in real time. This information is combined with other sensors to help the car navigate safely and make decisions, such as when to stop, turn, or change lanes.
  • Chapter Takeaway — Machine Learning (ML) is a subset of AI that has become part of our daily lives in many ways. It’s the process of training computers and AI models to learn from existing data, which is a departure from the traditional programming approach that relies on explicitly defined rules and instructions. 

Ch. 6: Adding Natural Language Processing and Sentiment Analysis

  • Natural Language Processing (NLP) — NLP is a way for computers to understand and work with human language, like the words we write or speak. Think of it like teaching a computer to “read,” “listen,” and even “talk” in a way that makes sense to us. For example, when you ask a smart assistant like Siri or Alexa a question, they use NLP to figure out what you’re saying, find the answer, and respond. Or, when you type a message and your phone suggests the next word, that’s NLP helping out. It works by breaking down language into smaller parts, like words and sentences, and then teaching the computer to recognize patterns and meanings. Over time, thanks to machine learning, the computer gets better at understanding what we mean, even when we say things in different ways.
  • NLP and ChatGPT — In addition to smart assistants, chatbots, and predictive typing, NLP is a core technology that drives ChatGPT. NLP helps ChatGPT understand the words and sentences we type, figure out what we’re asking or saying, and then generate a response that makes sense based on our input. It’s like teaching the model to “read” and “write” in human language. More specifically, ChatGPT is powered by a type of NLP called “deep learning,” which uses large amounts of text data and advanced algorithms to recognize patterns, context, and meaning in language. This allows it to generate responses that sound like us. So when we interact with ChatGPT, we’re experiencing NLP in action!

Ch. 7: Collaborating Via Predictions, Procedures, Systems, and Filtering

  • Predictive Analytics — Predictive analytics is another subset of AI that uses data to make predictions about what might happen in the future. It works by analyzing patterns in past data and using those patterns to guess what will happen next. For example, if a store knows that people buy more umbrellas when it rains, they can predict that umbrella sales will go up if rain is in the forecast. In the real world, companies use predictive analytics in many ways. A bank might use it to figure out who is likely to pay back a loan. Streaming services like Netflix use it to recommend shows you might like based on what you’ve watched before. It helps businesses plan ahead and make smarter decisions by looking at data and guessing what comes next.
  • Filtering: How It Works — Filtering is a technique used within AI systems, but it is not a subset of AI. It involves sorting and organizing data based on specific criteria, helping systems or users focus only on relevant information. In AI, filtering is commonly used in recommendation systems (e.g. Netflix or Amazon) and spam email detection. For instance, Netflix filters movies and shows to suggest content that matches our viewing history. Similarly, our email inbox uses filtering to separate spam messages from important emails by analyzing patterns in the email’s content. Filtering works by applying algorithms to data, identifying patterns, and classifying or prioritizing items based on rules or learned preferences.

Ch. 8: Getting Comfortable With Generative AI

  • What Is Generative AI? — Generative AI burst onto the scene in November 2022, when OpenAI made ChatGPT accessible to the masses. Generative AI is a subset of AI that focuses on creating new content, ranging from text and images to music, videos, code, and much more. Machine Learning (ML) is at the heart of Generative AI models, which are also known as Large Language Models (LLMs). These models are trained on huge data sets (e.g. web text, books, articles, etc.) and use “deep learning” (a type of machine learning) to predict the most appropriate words, phrases, or images based on your request or input. That’s really all ChatGPT is — a giant, well-trained word predictor that understands context. 
    • Quote (P. 125): “GPT models generate text in an autoregressive manner, which means that they predict the next word in a sequence based on the previous words. They can understand and generate text based on context, making them suitable for tasks such as helping Al mimic conversation, summarize text, and create written content.”
    • Quote (P. 126): “During training, these models learn language patterns, structures, and nuances. They function as autoregressive models, generating text by predicting the next word or character based on the preceding context. This sequential prediction is iteratively repeated, enabling the generation of sentences and entire paragraphs.