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Unit 8.1

AI and Machine Learning in Marketing

Digital Marketing Course

Learning Objectives

By the end of this chapter, you will be able to:

  • ✅ Define Artificial Intelligence (AI) and Machine Learning (ML) in a marketing context.
  • ✅ Differentiate between the broader concept of AI and the specific function of ML.
  • ✅ Identify and explain the core applications of AI/ML in modern marketing.
  • ✅ Analyze real-world examples of AI-driven marketing strategies.

The New Marketing Powerhouse ⚡

Artificial Intelligence and Machine Learning are not just buzzwords; they are fundamentally changing how we connect with customers.

They help marketers move from "best guesses" to data-driven, predictive decisions.

  • Personalize messages for individuals, not segments.
  • Automate complex and repetitive tasks.
  • Predict customer behavior with stunning accuracy.

What is AI in Marketing? 🎯

Definition: The use of artificial intelligence to make automated decisions based on data collection, data analysis, and observations of audience or economic trends that may impact marketing efforts.

In simple terms: AI is the "brain" of the operation. It's the overall system designed to perform smart tasks that typically require human intelligence.

What is Machine Learning (ML) in Marketing? 📊

Definition: A subset of AI that allows computer systems to automatically learn and improve from experience (data) without being explicitly programmed.

In simple terms: If AI is the brain, ML is the part that is constantly learning. It finds hidden patterns in your customer data to make the AI "smarter" over time.

AI vs. ML: A Quick Comparison

Artificial Intelligence (The Car)

The broader concept of a machine that can simulate human intelligence.

  • Executes tasks
  • Makes decisions
  • The complete system

Machine Learning (The Engine)

The core component that enables the system to learn from data.

  • Finds patterns
  • Makes predictions
  • Powers the AI's intelligence

Core Applications of AI & ML

Personalization

Creating tailored experiences for each individual user, from product recommendations to website content.

Predictive Analytics

Using historical data to forecast future trends, customer churn, and campaign success.

Automation

Handling repetitive marketing tasks like email sends, ad bidding, and social media posting intelligently.

Application 1: Hyper-Personalization

AI allows for personalization at a scale never before possible.

Examples you see every day:

  • Netflix & Spotify: "Recommended for You" sections are powered by ML algorithms analyzing your viewing/listening history.
  • Amazon & Daraz: "Customers who bought this also bought..." product suggestions.
  • Dynamic Emails: Subject lines and offers that change based on a user's past behavior.

Application 2: Predictive Analytics

Answering "what will happen next?" with data.

ML models analyze past data to predict future outcomes, allowing marketers to be proactive instead of reactive.

  • 🔮 Predict Customer Churn: Identify customers at risk of leaving and target them with retention campaigns.
  • 🛒 Market Basket Analysis: Predict which products are likely to be purchased together.
  • 📈 Lead Scoring: Prioritize sales leads most likely to convert.

Application 3: Intelligent Automation

Automating tasks smartly, not just mechanically.

AI-powered automation doesn't just "do" tasks; it optimizes them based on real-time data.

  • Programmatic Advertising: AI algorithms bid on ad space in real-time, optimizing for the best audience at the best price.
  • AI Chatbots: Provide 24/7 customer support, answer common questions, and even qualify leads.
  • Automated Content Generation: Tools that assist in writing ad copy, social media posts, or email drafts.

AI in Action: The Nepali Context 🇳🇵

How local companies are using AI/ML:

  • E-commerce (Daraz): Uses ML for its "Just for You" product recommendations and AI-powered search to understand local dialects and spellings.
  • Ride-Sharing (Pathao): Employs ML for dynamic surge pricing based on real-time demand, traffic, and weather conditions.
  • FinTech (eSewa, Khalti): Leverages AI for real-time fraud detection by analyzing transaction patterns and flagging unusual activity.

Summary: Key Takeaways 🔍

  • AI is the Big Idea, ML is the Engine: AI is the broad field of creating intelligent machines, while ML is the specific method of learning from data to achieve that intelligence.
  • The 3 Pillars of AI Marketing: Most applications fall under Personalization, Predictive Analytics, or intelligent Automation.
  • From Mass to Micro: These technologies enable a shift from broadcasting to one-size-fits-all segments to having personalized, one-on-one conversations at scale.
  • It's Already Here: AI and ML are not future concepts; they are actively shaping the digital landscape in Nepal and around the world today.

Thank You!

Any questions?

Next Up: Unit 8.2 - SEO and AI-Powered Content Strategy

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