Excel Data Science for Marketing in Action PDF

Excel Data Science for marketing in action November, 2024 Adam Berros 04. Introduction to Machine Learning for Marketing What is Machine Learning ? Machine Learning is a subset of Artificial Intelligence that enables systems to learn and improve from experience and historic without being exp...

Excel Data Science for marketing in action November, 2024 Adam Berros 04. Introduction to Machine Learning for Marketing What is Machine Learning ? Machine Learning is a subset of Artificial Intelligence that enables systems to learn and improve from experience and historic without being explicitly programmed The main goals of machine learning are predictions, discover patterns and automate decision-making Many Business Applications: ○ Advanced chatbots, Sales forecast, Churn Prediction, ○ Dynamic Pricing models, Fraud Detection system, ○ Predictive Maintenance, Healthcare diagnostics, Recommendation System… 3 Why Machine Learning for Marketing? Key Reasons : ○ Personalization: Customizing user experience at scale ○ Predictive Power: Anticipating customer needs and trends ○ Automation: Automate repetitive marketing tasks 4 Types of Machine Learning 1. Supervised Learning : ○ → Models learn from labeled data 2. Unsupervised Learning : ○ → Models find patterns in unlabeled data 3. Reinforcement Learning : ○ → Models learn by maximizing rewards in an environment 5 Supervised Learning Definition: Learning from labeled data where input-output pairs are known Examples in Marketing : ○ Predicting customer purchase likelihood based on historical data. ○ Email classification for customer support. Techniques: Regression, Classification, Decision Trees, and Support Vector Machines 6 Unsupervised Learning Definition: Finding hidden patterns in data without pre-existing labels. Examples in Marketing: ○ Customer segmentation based on purchasing behavior. ○ Grouping products for recommendations. Techniques: Clustering (K-means), Association Rules, and Principal Component Analysis (PCA). 7 Reinforcement Learning Definition: Learning through trial and error to maximize a reward. Examples in Marketing: ○ Optimizing ad placements based on user behavior feedback. ○ Dynamic pricing to increase conversion rates. Techniques: Q-Learning, Deep Q Networks (DQN), and Markov Decision Processes (MDP). 8 Key ML Concepts for Marketers Data Preprocessing in ML Objective: Ensuring data quality for effective ML models. Key Steps: 1. Cleaning (handling missing values, outliers). 2. Feature Engineering (creating meaningful variables from raw data). 3. Data Transformation (normalization, encoding). Marketing Impact: Accurate customer data = more reliable predictions. 9 Overfitting and Underfitting 10 11 Overfitting and Underfitting Overfitting: The model learns "too well" on training data, performing poorly on new data. Underfitting: Model fails to capture data patterns. Marketing Relevance: Ensures that predictions are generalizable across diverse customer data. 12 Model Evaluation Metrics Common metrics in ML include: Accuracy, Precision, Recall: For classification tasks. Mean Squared Error (MSE): For regression tasks. → Marketing Example: High recall in a churn model means fewer customers slipping through the prediction net. 13 Applications of ML in Marketing Customer Segmentation Segment customers into targeted groups. Example Techniques: K-means clustering, Hierarchical clustering. Marketing Impact: Enhanced targeting for personalized email campaigns, ads, and product recommendations. 14 Applications of ML in Marketing Predictive Analytics & Customer Lifetime Value (CLV) Use regression to predict CLV, allowing for more effective resource allocation. Tools and Models: Linear regression, Decision Trees, Random Forest. Real-Life Example: Predicting future spending patterns for premium or churn-prone customers 15 Applications of ML in Marketing Recommendation Systems Content-Based Filtering: Recommends based on customer preferences. Collaborative Filtering: Recommends based on similarities to other customers. Marketing Impact: Boosts cross-selling and up-selling by delivering personalized product recommendations. 16 Algorithms in Python Linear & Logistic Regression examples 17 Create a client lifecycle & Identify Machine Learning Touchpoints Choose the Type of Company: Each group should choose the type of company they will work on. It could be an online store, a mobile app, a streaming service, an insurance company, an e-commerce site etc… Create the Customer Lifecycle: You should define each stage of the customer journey, from the first interaction with the company to customer retention or churn. You need to create a customer journey map using https://www.mindmeister.com/fr or https://excalidraw.com/ for example. Important Note: Each group must detail the interactions at each stage and the channels used (website, app, email, chatbot, etc.). Identify Machine Learning Opportunities at Each Stage: After defining the customer journey, each group needs to identify where machine learning can be applied at each point in the lifecycle. Here are some examples of where ML can improve the customer experience : cart abandonment reminders, personalized offers, churn prediction etc… Presentation Time : 5 minutes - Groups of 3 18 Group Exercise Dataset File : https://docs.google.com/spreadsheets/d/1qrJ-0caF5JdxHKiiZbCBVhpCnDcDYDl9y-n9 8COdnV0/edit?gid=1833910757#gid=1833910757 19