One of the keys to my success throughout the years has been my ability to learn new technology. Innovation in digital marketing has been rapid but consistent… until now. As I watch artificial intelligence (AI) advancements, I fear that I’m falling behind… and it could cost me a great career where I’ve spent every spare minute studying, applying, and implementing with my customers. And, because it’s AI, I know that once I fall behind, the machines will far outpace any chance of catching up.
So… I’m studying the tools daily, watching the sales and marketing adoption, and sharing everything along the way. I should disclose that I have one incredible edge over most business professionals: my son Bill is the Chief Data Scientist at OpenINSIGHTS with a Ph.D. in Mathematics from the University of Illinois. In addition to being an AI practitioner on the leading edge, he’s an exceptional teacher… he led the geometry lab at his university, tutored dozens of students at all levels, and taught calculus courses. Thankfully, he supports my 50+ mind and helps me break down the concepts to understand them better.
Martech Zone And Artificial Intelligence
After years of having the same primary categories on my site, I updated Martech Zone to have an AI category. I do not doubt that it will likely take over every other category as it’s adopted into every aspect of our jobs. Still, I wanted an easy way for you, the readers here, to research, learn, and discover AI in the sales and marketing field. I want to curate content on this site so that it’s presented to the average business person… not a data scientist or someone with a PhD. They have plenty of resources out there already.
In that spirit, I believe a good start may be to help business people understand some of the key concepts of AI and why it will be indispensable in every aspect of our day-to-day work. For many business professionals, the concept of AI can be difficult to grasp. Here’s a breakdown:
- What is Artificial Intelligence?
- A brief history of AI
- What is GenAI?
- Sales and marketing and AI
- A sample AI implementation
- What if you don’t have enough data?
This article aims to provide a clear understanding of AI for those who may not have a strong technical background. We’ll cover some common AI-related terminology, provide analogies to clarify concepts and explore the history of AI up to the development of ChatGPT.
What Is Artificial Intelligence?
AI, or Artificial Intelligence, refers to developing computer systems or machines that can perform tasks that typically require human intelligence. These tasks include problem-solving, learning, understanding, and processing natural language, recognizing patterns, and making decisions.
AI is a multidisciplinary field that combines computer science, mathematics, and domain-specific knowledge to create algorithms and models that enable machines to mimic human cognitive abilities. This allows AI systems to analyze large volumes of data, identify patterns, and make predictions or recommendations based on the information they process.
If I’m already confusing you, let’s provide an analogy. Imagine a data scientist as a chef who creates recipes. The chef (data scientist) provides the ingredients (data) and instructions (algorithms) for preparing a dish. Like a skilled apprentice, the AI system learns from these recipes and can create similar dishes on its own. Over time, the AI system may develop new recipes based on its understanding of the ingredients and techniques.
In AI, the data scientist designs the algorithms and provides the data, while the AI system (the intelligence) learns from this input and can perform tasks autonomously. Improvements to the algorithms happen during training or retraining the algorithms. For example, the Facebook or X (formerly Twitter) AI algorithm only gets better at understanding you because it has more data about you over time or because developers create a better one, not because it is autonomously improving itself. When you interact with a model these days, it’s usually pre-trained.
Artificial Narrow Intelligence
ANI (or Weak AI) refers to AI systems that excel at specific tasks within a limited domain, such as a recommendation engine on a shopping website that suggests products based on your browsing history and preferences. Since ANI systems are designed to focus on a particular task, they need human guidance and input to be applied in different contexts or for new tasks.
Artificial General Intelligence
AGI would be an AI system that can understand, learn, and apply its intelligence autonomously to a wide range of tasks, similar to human intelligence. For example, an AGI could learn to play chess, write poetry, and diagnose diseases, all while adapting its knowledge and skills to new domains.
Artificial Super Intelligence
ASI represents a hypothetical level of AI that surpasses human intelligence in every aspect. An ASI could rapidly solve complex global problems, make groundbreaking scientific discoveries, and create innovative solutions beyond the capabilities of human experts, all while continually improving its own abilities.
A Brief History Of AI
AI’s journey from a concept to reality began with Turing’s work in the 1950s. The creation of LISP, the first AI language, marked a significant stride in the 1960s. Machine learning took center stage in the 1990s, revolutionizing the field. The 2000s brought a new focus on robotics and language processing in AI. The real game-changer has been the development of OpenAI’s GPT series, signifying a notable leap in AI. GPT-3 and GPT-4 showcase the limitless potential that AI holds.
- The 1950s-1960s: AI’s foundations were laid with the work of Alan Turing and John McCarthy, who developed the concept of the Turing Test and coined the term Artificial Intelligence, respectively. Researchers during this period were optimistic that computers could be programmed to solve general problems, use reason, and make decisions.
- The 1970s-1980s: AI research expanded, with a focus on rule-based expert systems, which could emulate the decision-making of human experts within specific domains. However, the optimism of the early researchers waned as there was little progress on more general problem-solving AI systems.
- The 1990s-2000s: Machine learning (ML) began to take center stage, as researchers explored algorithms that could learn from data, leading to the development of support vector machines, decision trees, and other ML techniques.
- The 2010s: With advancements in computational power and the availability of larger datasets, deep learning emerged as a powerful approach to solving complex AI problems in image recognition and natural language processing.
- The 2020s: The development of transformer-based large language models (LLMs) like Open AI‘s GPT-3 and Google’s BERT revolutionized natural language processing. Open AI combined its large language models with reinforcement learning to build ChatGPT, a powerful generative natural language AI system. Other generative AI tools like DALL-E and Midjourney are being developed.
- The 2030s and beyond: The continued integration of AI systems will move from Artificial Narrow Intelligence (ANI) could lead to Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI) with the potential to radically transform the world as we know it.
The rapid advancement of AI in recent years can be attributed to several key factors, including the increase in available bandwidth, improvements in computing speed, the widespread adoption of cloud computing, and advancements in programming. These factors have created a synergistic environment that has propelled AI development and adoption.
- Bandwidth: The growth of the internet and increased bandwidth availability have enabled the transfer and processing of massive amounts of data at high speeds. This has facilitated the development of AI models that rely on large datasets for training and analysis. Greater bandwidth also allows AI applications to operate efficiently and deliver real-time insights and predictions.
- Computing speed: The advancements in computer hardware, particularly in Graphics Processing Units (GPUs) and specialized AI chips, have significantly increased computing speed. This has enabled AI algorithms to process large volumes of data and perform complex calculations more quickly. Faster computing speeds have accelerated the training and deployment of AI models, making it feasible to tackle more sophisticated and demanding tasks that were previously impossible.
- Cloud Computing: The rise of cloud computing has provided businesses and researchers with easy access to powerful computing resources and infrastructure. This has lowered the barriers to entry for AI development, as organizations no longer need to invest heavily in on-premises hardware to build and deploy AI models. Cloud-based AI platforms also enable seamless collaboration, scalability, and flexibility in deploying AI applications.
- Programming: Advances in programming languages, libraries, and frameworks have simplified the process of developing AI applications. Open-source libraries like TensorFlow, PyTorch, and scikit-learn offer pre-built functions and tools to help developers create AI models with relative ease. These libraries have fostered a collaborative environment, enabling developers to share their work and benefit from each other’s innovations. This has led to rapid advancements in AI algorithms, techniques, and applications.
The intersection of these factors has created a perfect storm for AI development. Greater bandwidth and computing speed have made it possible to process and analyze vast amounts of data, while cloud computing has made advanced computing resources accessible and affordable. Here are common applications of AI:
- Classification: AI can be used to categorize data into different classes based on their features. For example, email spam filters, image recognition systems, and sentiment analysis tools rely on classification algorithms.
- Regression: AI can predict continuous values based on input features. Examples include predicting house prices based on various attributes, forecasting sales, and estimating the likelihood of a customer churning.
- Recommendation systems: AI algorithms can provide personalized recommendations to users based on their preferences, behavior, and historical data. Examples include movie recommendations on streaming platforms and product suggestions on e-commerce websites.
- Natural language processing (NLP): AI can be used to analyze, understand, and generate human language. Some common NLP tasks include machine translation, text summarization, and named entity recognition.
- Speech recognition: AI can transcribe spoken language into written text. This technology is used in applications like virtual assistants, transcription services, and voice-controlled systems.
- Computer vision: AI can process and analyze visual information, such as images and videos. Applications include object detection, facial recognition, and optical character recognition (OCR).
- Robotics: AI is employed in the development of robots that can perform tasks autonomously or semi-autonomously. Examples include self-driving cars, drones, and robotic assistants.
- Game playing: AI can learn to play and excel in various games, such as chess, Go, and video games, often outperforming human players.
- Anomaly detection: AI can be used to identify unusual patterns or outliers in data, which can be useful in fraud detection, network security, and quality control.
Perhaps the most important advancement in artificial intelligence today is Generative AI (GenAI):
What is Generative AI?
GenAI is a subset of artificial intelligence that focuses on creating content, including text, images, videos, and more. GenAI systems are designed to mimic human creativity and generate content autonomously. These systems utilize deep learning techniques, mainly variants of neural networks like Recurrent Neural Networks (RNNs) and Generative Adversarial Networks (GANs), to accomplish their tasks.
Key features and applications of GenAI in the context of sales and marketing include:
- Content Generation: GenAI can generate high-quality marketing content, such as blog articles, social media posts, and email campaigns, without human intervention. This capability is particularly valuable for maintaining a consistent content schedule and engaging with a broader audience.
- Personalization: GenAI can analyze customer data and preferences to create personalized product recommendations and marketing messages. This personalization enhances customer experiences and increases the likelihood of conversion.
- Automation: GenAI can automate repetitive marketing tasks, such as A/B testing, data analysis, and ad optimization. This frees up marketing professionals to focus on strategy and creative aspects of their campaigns.
- Language Translation: GenAI can assist in translating marketing materials into multiple languages, enabling businesses to reach global markets.
- Visual Content Creation: GenAI can generate visuals like infographics, logos, and video clips, reducing the time and cost of hiring designers and videographers.
- Market Research: GenAI can help analyze market trends and customer sentiment by processing vast amounts of data from social media, reviews, and news sources. This information is valuable for making data-driven marketing decisions.
GenAI is playing a pivotal role in the sales and marketing industry by automating content creation, personalizing customer interactions, and streamlining marketing operations. It empowers businesses to stay competitive in the digital landscape by leveraging AI-driven creativity and efficiency.
Sales and Marketing AI
AI is transformative in sales and marketing for several reasons:
- Personalization: AI can analyze large amounts of data and identify patterns that help create personalized customer experiences. This can lead to better customer engagement, higher conversion rates, and increased customer loyalty.
- Automation: AI can automate many repetitive and time-consuming tasks, such as lead scoring, email marketing, and customer segmentation, allowing sales and marketing teams to focus on more strategic activities.
- Predictive analytics: AI can analyze historical data to predict future customer behavior, allowing companies to anticipate customer needs, optimize marketing campaigns, and improve sales strategies.
- Enhanced decision-making: AI can process vast amounts of data quickly and accurately, providing sales and marketing teams with valuable insights to make informed decisions about targeting, messaging, and product development.
- Improved efficiency: AI can help companies optimize their sales and marketing processes, reducing costs and increasing productivity.
A Sample AI Implementation
Here’s a common use case we’re seeing AI have an impact with today… B2B lead scoring. Given your CRM and historical client data, combining firmographic and behavioral data, and building algorithms, companies can score a lead database of prospective customers. Here are the steps:
Step 1: Data extraction and preparation
- Collect customer information from your CRM system. This includes details about their companies, such as size and industry, and how they’ve interacted with your business (e.g., emails, website visits, etc.).
- Gather additional information about your customers and potential leads, like the revenue their companies generate, the number of employees they have, and where they’re located.
- Combine the information from your CRM and the additional data into one dataset.
- Clean and organize the data, filling in any missing details and ensuring all the different types of information are in a format the AI can use.
Step 2: Feature engineering and selection
- Create new data points that might help predict which leads are likely to become customers. These could be combinations or ratios of existing data points.
- Identify the most important data points for predicting lead conversion by using techniques that help you determine which factors have the strongest relationship with becoming a customer.
Step 3: Model development and training
- Divide the dataset into two parts: one for training the AI and another for testing its performance.
- Choose a suitable AI method that can learn patterns in the data and make predictions. Examples include logistic regression, support vector machines, or gradient boosting machines. We’re not going to go into those details here!
- Teach the AI using the training data, showing it the patterns in the data and the outcomes (whether a lead became a customer or not).
Step 4: Model evaluation and lead scoring
- Test the AI’s performance on the testing set by comparing its predictions to the known outcomes. Measure its accuracy using metrics that help you understand how well it’s doing, such as precision, recall, F1-score, and area under the ROC curve.
- If the AI performs well, use it to predict the likelihood of potential leads becoming customers.
Step 5: Lead prioritization and follow-up
- Organize the leads based on their predicted likelihood of becoming customers.
- Focus your sales and marketing efforts on the leads with the highest predicted likelihood, as they have the best chance of converting into customers.
By following these steps, you can use AI to analyze your customer data and prioritize leads based on their likelihood of conversion, which can help make your sales and marketing efforts more efficient.
What If You Don’t Have Enough Data?
AI isn’t just for large companies with massive data sets that can afford a data scientist and the necessary infrastructure. For companies with small datasets and no data scientist, leveraging AI is still possible through the following approaches:
- Third-party AI tools: Many AI platforms and tools cater to small businesses or companies without dedicated data science teams. These tools can help with tasks like customer segmentation, lead scoring, and marketing automation without requiring extensive in-house expertise.
- Pre-trained models: Some AI tools offer pre-trained models that can be applied to specific tasks, like sentiment analysis or image recognition. While these models may not be as accurate as custom models built with your data, they can still provide valuable insights.
- Collaborative platforms: Leverage platforms like Kaggle or engage with freelance data scientists who can help you build AI models for your specific needs. By outsourcing the data science work, you can focus on utilizing the insights generated by AI to improve your sales and marketing strategies.
- Data augmentation: Even if your dataset is small, you can still use techniques like data augmentation to expand your dataset by creating new examples from the existing data. This can help improve the performance of AI models trained on your data.
By utilizing these approaches, companies with limited data and resources can still harness the transformative power of AI to improve their sales and marketing efforts. I’d also recommend encouraging your team to learn the basics of AI and machine learning through online courses, workshops, or certifications. This can help them better understand how to leverage AI in their work and increase the overall data literacy within your organization.