In this chapter, you will explore the basics of machine learning methods used in marketing. You will learn about different types of machine learning, data preparation steps, and will run several end to end models to understand their power.

Why use ML for marketing? Strategies and use cases

Identify supervised learning examples

Supervised vs. unsupervised learning

Preparation for modeling

Investigate the data

Separate numerical and categorical columns

Encode categorical and scale numerical variables

ML modeling steps

Split data to training and testing

Fit a decision tree

Predict churn with decision tree

Machine learning for marketing basics

In this chapter you will learn churn prediction fundamentals, then fit logistic regression and decision tree models to predict churn. Finally, you will explore the results and extract insights on what are the drivers of the churn.

Dasar-dasar prediksi churn

Eksplorasi tingkat churn dan pemisahan data

Pisahkan fitur dan variabel target

Prediksi churn dengan regresi logistik

Melatih model logistic regression

Melatih logistic regression dengan regularisasi L1

Identifikasi koefisien penalti L1 yang optimal

Prediksi churn dengan decision tree

Melatih model decision tree

Mengidentifikasi kedalaman pohon optimal

Identifikasi dan interpretasikan pendorong churn

Menjelajahi koefisien regresi logistik

Uraikan aturan decision tree

Churn prediction and drivers

In this chapter, you will learn the basics of Customer Lifetime Value (CLV) and its different calculation methodologies. You will harness this knowledge to build customer level purchase features to predict next month's transactions using linear regression.

Customer Lifetime Value (CLV) basics

Build retention and churn tables

Explore retention and churn

Calculating and projecting CLV

Calculate basic CLV

Calculate granular CLV

Calculate traditional CLV

Data preparation for purchase prediction

Build features

Define target variable

Predicting customer transactions

Predict next month transactions

Measure model fit

Explore model coefficients

Customer Lifetime Value (CLV) prediction

This final chapter dives into customer segmentation based on product purchase history. You will explore two different models that provide insights into purchasing patterns of customers and group them into well separated and interpretable customer segments.

Customer and product segmentation basics

Explore customer product purchase dataset

Understand differences in variables

Data preparation for segmentation

Unskew the variables

Normalize the variables

Build customer and product segmentation

Determine the optimal number of clusters

Build segmentation with k-means clustering

Alternative segmentation with NMF

Visualize and interpret segmentation solutions

K-means segmentation averages

NMF segmentation averages

Congratulations!

Customer segmentation

Telecom Dataset

Bangkitnya machine learning (hampir terdengar seperti "kebangkitan mesin"?) dan penerapan metode statistik pada pemasaran telah mengubah bidang ini selamanya. Machine learning digunakan untuk mengoptimalkan perjalanan pelanggan agar memaksimalkan kepuasan dan nilai seumur hidup mereka. Kursus ini akan membekali Anda dengan alat dasar yang dapat langsung diterapkan untuk meningkatkan strategi pemasaran perusahaan Anda. Anda akan mempelajari berbagai teknik untuk memprediksi churn pelanggan dan menafsirkan pendorongnya, mengukur dan memprakirakan Customer Lifetime Value, dan akhirnya membangun segmen pelanggan berdasarkan pola pembelian produk mereka. Anda akan menggunakan data pelanggan dari sebuah perusahaan telekomunikasi untuk memprediksi churn, menyusun himpunan data recency-frequency-monetary dari peritel daring untuk prediksi Customer Lifetime Value, dan membangun segmen pelanggan dari data pembelian produk sebuah toko bahan makanan.

Supervised Learning with scikit-learn

Pelajari dan terapkan kasus Machine Learning untuk Pemasaran di Python: nilai pelanggan, churn, segmentasi.

Machine Learning untuk Pemasaran dengan Python

Dari nilai seumur hidup pelanggan, memprediksi churn hingga segmentasi - pelajari dan terapkan kasus penggunaan Machine Learning untuk Pemasaran dalam Python.

Identifikasi dan interpretasikan pendorong churn

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