Data Science in Marketing: An Introduction Course 2022

Discover how data science and machine learning drive data-driven marketing, from customer segmentation and behavior prediction to marketing analytics and predictive campaign optimization.

Explore key performance indicators and visualizations for marketing, including sales, revenue, cost per acquisition, CTR, and conversion rate, and learn to compute and visualize these KPIs with Python.

Learn to compute and visualize KPIs by loading data with pandas read_csv using a semicolon separator and encoding binary yes/no fields as 1 or 0 with apply.

Compute the aggregate conversion rate by summing the 1s for converted and dividing by the total client count. Show the result as 11.27 percent in the example.

Break down the customer base into age segments, compute conversions per segment, and plot the results to compare campaign performance. Drop small age groups to reduce noise.

Explore how to compare conversion and non-conversion groups using pandas, grouping by criteria and aggregating conversions. Visualize with a pie chart to interpret distribution.

Analyze conversion by age and marital status using two-column group by and bar charts to reveal how conversion rates vary across groups.

Explore descriptive analyses for marketing progress and performance. Apply kpi, sales revenue metrics, campaign attribution, cpa, and digital marketing channel metrics, plus regression and machine learning for explanatory insights.

Compare logistic regression and decision trees for marketing analytics by examining impurity and information gain, and learn how non-linear splits reveal relationships between features and target.

Explore how to load marketing data with pandas, encode target variables, compute conversion rates with group-by analysis, and interpret results for data-driven marketing insights.

Analyze conversion rates by job category, compute conversions divided by total customers per category, and visualize differences with a bar chart, highlighting top and bottom performers.

Explore how default rates differ between conversion and non-conversion groups using pandas pivot_table to aggregate customer counts and visualize the results.

Compare buying balances by conversion status using a box plot, and analyze how conversion rates vary with the number of contacts in marketing campaigns.

Explore encoding categorical variables with pandas by extracting unique month values from January to December and encoding them for analysis.

Apply one-hot encoding to job categories and generate dummy variables, then concatenate these new columns to the existing data frame in pandas.

Learn how to encode marital status and housing and loan variables by creating new columns, mapping yes/no to 1/0, and applying a function to derive married, single, and divorce categories.

Build and train a decision tree classifier by encoding categorical variables, selecting features and a target, tuning depth and criterion, and exporting the trained graph for visualization.

Interpret a decision tree to understand split criteria, impurity changes, and node counts, and identify customer attributes that link to conversion for targeted marketing campaigns.

Explore a decision tree approach for marketing analytics, train and visualize models in Python, interpret results to identify customer groups likely to convert.

Discover how product analytics with data science unlocks granular insights into customer engagement, time series trends, and purchasing patterns to boost retention and optimize marketing return on investment.

Implement product analytics by loading a dataset, cleaning canceled orders, and analyzing monthly orders and revenue with pandas-powered visualizations of quantity distribution.

Compute monthly sales revenue by multiplying unit price by quantity, aggregate to a time series, and compare with the previous year to identify seasonal effects and growth.

Analyze repeat customers by tracking monthly repeat purchases, attributing revenue to repeats, and cleaning and grouping invoice data to count customers with multiple orders per month.

Analyze monthly marketing data to quantify repeat customers, showing that 20–30 percent are repeat customers and that repeat revenue accounts for 40–50 percent of total monthly revenue.

Analyze how customer interactions with individual products reveal trends over time. Identify the top five best sellers for November 2011 by aggregating monthly sales by stock code and quantity.

Transform monthly sales data for the top five products into a tabular time series with a pivot, then visualize trending items over time to reveal demand patterns.

Analyze seasonal drivers behind November spikes in the top five products. Use these trends to tailor marketing messages and product recommendations for higher engagement.

Explore product recommender systems that tailor suggestions to individuals, covering collaborative filtering and content-based filtering, with user-based and item-based approaches and their impact on conversion, revenue, and engagement.

Prepare data for a collaborative filtering recommender by handling missing values and building a custom user-item matrix, then clean records with missing customer ids.

Create a customer-item matrix from purchase data to enable collaborative filtering for product recommendations. Use a pivot table with customer id rows, item columns, and quantity values.

Explore collaborative filtering by examining user-based and item-based approaches to measure similarity between users via their item purchases and between items to build a product recommendation engine.

Learn item-based collaborative filtering by computing item similarities with cosine similarity, building an item similarity matrix, and recommending the most similar items to customers based on past purchases.

Explore item-based collaborative filtering to generate top 10 similar items and craft targeted marketing messages that drive product recommendations and higher conversion rates.

Quantify customer lifetime value and build a Python regression model to predict clv over a period. Evaluate regression models with mean squared error, mean absolute error, and r-squared.

Clean and prepare a retail dataset in pandas by removing negative quantities and invalid orders, handling missing data, computing per-order total sales, and grouping by customer id and invoice number.

Learn data analysis for marketing by computing customer level metrics like frequency, recency, and total purchases, using group by operations, purchase duration, and average frequency calculations.

Continue analyzing the purchase data to explain results, examine purchase distribution and the average days between purchases, and reveal that repeat customers buy roughly every 20 to 50 days.

Prepare data for a predictive model to forecast the last three months of customer value. Aggregate data by customer over three-month periods, encode dates, and train a linear regression model.

Explains data preparation for marketing analytics, training models on tabular data, building a pivot_table with customer_id as the index, and forecasting the next three months' sales for customers.

Continue building the machine learning model by assembling features and the target variable. Merge dataframes on a custom id, handle three-month purchase gaps as zero, and review model results.

Learn to build and evaluate a linear regression model with train/test split to predict next three months' customer value from prior features, and inform targeted marketing strategies.

Evaluate a regression model trained to predict customer value, using predicted versus actual values and metrics like squared error and median absolute error, with train-test splits and visual plots.

Explore data driven customer segmentation and learn how k-means clustering groups customers using historical data to enable targeted marketing strategies and personalized promotions.

Drop records with no customer id and exclude December 2011 data. Compute total sales from quantity and unit price, then normalize to mean zero and unit variance for customer segmentation.

Learn how k-means clustering segments customers into four marketing clusters by normalizing data, fitting a model, and visualizing total sales and average order value to identify low- and high-value groups.

Learn to select the optimal number of clusters by comparing k-means runs across counts and using a cluster score to identify the best solution.

Interpret customer segments from a k-means clustering analysis, using orders and average order value to identify high-, medium-, and low-value customers, then tailor marketing and item recommendations.

Celebrate your progress in deep learning, then set goals, practice relentlessly, and build models using datasets from Google and UCI to sharpen your skills.