Udemy

Marketing Analytics with Python: From Data to Insights

Name: Marketing Analytics with Python: From Data to Insights
Availability: InStock
Rating: 4.6 (58 reviews)

4.6(58)2k studentsBEGINNEREnglish

Korshub's take

Overview

This course applies Python to real marketing analytics problems, moving through five connected topics: customer segmentation with K-Means clustering, persona-building through similarity analysis, cohort analysis for retention, linear regression for marketing strategy, and modeling how advertising impact decays over time. Tools include pandas, seaborn, statsmodels, and Scipy, applied to genuinely practical marketing questions rather than abstract statistics exercises.

Who it's for

Marketers, analysts, and data enthusiasts who want to move past dashboards and into applied statistical methods for understanding customers and campaign performance.

Key takeaways

Segmenting customers with K-Means clustering, PCA, the Elbow Method, and Silhouette Analysis
Building an ideal customer persona using similarity analysis and Euclidean distance
Running cohort analysis with pivot tables and retention heatmaps
Using linear regression and lag/decay modeling to understand and optimize marketing spend

About this course

Dive deep into the captivating world of marketing analytics with our comprehensive course! Unlock the mysteries of customer behavior with cutting-edge clustering and segmentation, craft the perfect persona using similarity analysis, and journey through time with cohort insights. Harness the power of linear regression to master marketing strategies and decode the nuances of ad impact with our segments on lag and decay. This is more than just a course; it's your gateway to becoming a marketing maestro. Are you ready to transform data into actionable insights? Join us and revolutionize your marketing game!

Clustering and Segmentation

Dive into customer segmentation, learning the art of grouping individuals based on shared attributes.
Explore unsupervised segmentation techniques, primarily using the K-Means clustering algorithm.
Visualize customer clusters and reduce data complexity through dimension-reduction techniques, including PCA plots.
Determine the optimal number of clusters using the Elbow Method and Silhouette Analysis.

Similarity Analysis (Persona Building)

Grasp the concept of building the ideal customer profile using similarity analysis.
Preprocess and scale dataset features for uniformity.
Focus on "seeds" as representations of ideal customers and learn how to propagate these seeds to identify similar prospects.
Employ Euclidean distance to measure similarity and pinpoint top prospects for targeted outreach.

Cohort Analysis

Delve into cohort analysis, understanding its significance in marketing.
Craft columns to represent invoice dates and cohorts, aiding in customer grouping based on their first purchase month.
Construct pivot tables to showcase customer retention across different cohorts over time.
Visualize cohort retention rates using heatmaps for a clearer depiction of customer behavior.

Marketing Strategy (Linear Regression)

Begin with an introduction to marketing strategy analysis.
Load and inspect datasets using pandas, understanding their structure.
Emphasize feature engineering, creating new data attributes.
Engage in Exploratory Data Analysis (EDA), visualizing data distributions and the impact of marketing strategies on sales using seaborn.
Delve into correlation and regression analyses with the statsmodels library.

Marketing Lag and Decay

Dive into the "Erosion Effect" in marketing, understanding how advertising impact decays over time.
Learn to account for this effect by introducing lags to the data, and observing changing correlations.
Explore the synergy impact analysis, discerning how marketing channels interact and enhance each other's effects.
Determine marketing thresholds and budget optimizations using decision tree regressors and Scipy.