Customer lifetime value predictive model with Python

Learn features used in customer lifetime value modeling with Python, from transaction data to aggregation, decide on unit price and quantity, and aggregate records to customer level.

Explore features for CLV modeling, including frequency, recency, and tenure, and learn to generate additional metrics like six-month frequency and recent value to boost model accuracy.

Perform data exploration and cleaning with Python to prepare transaction data. Use pandas to read data into a data frame, handle missing values, and engineer recency and frequency features.

Install the Anaconda distribution on Windows, Mac, or Linux, then install essential packages for data exploration, data preparation, and CRM model building, using Spyder or notebook and handling third-party data.

Learn to download and install the Anaconda software, use Spyder as the editor, and install lifetime and LightGBM to build customer lifetime value models with data preparation.

Learn Python-based data exploration for CLV modeling by loading a CSV with pandas, inspecting top five records and shape, listing column features, and preparing data for visualization and lifetimes modeling.

Practice data analysis with pandas to explore a dataset, inspect data types, and prepare features like recency and customer age while handling missing values and dropping records with missing ids.

Use pandas to inspect data types and missing values, assess missing-value proportions, and apply imputation or removal. Prepare customer-level data by filtering and aggregating for CLV modeling.

Explore data preparation for clv modeling in Python: check data shape, handle missing values, filter by positive quantity, compute monetary spend, convert invoice dates, and run describe for statistics.

Generate clv features from transaction data using the lifetimes package and pandas. Build a dataset with customer id, end date, and t, focusing on frequency, age, recency, and monetary value.

Explore the BG/NBD framework for customer lifetime value, linking data preparation and features (frequency, tenure, recency) to a Poisson process with gamma and beta distributions for retention and purchases.

Explore how the Poisson distribution, with lambda as the transaction rate, models purchase frequency and repurchase probability for customer lifetime value in Python CLV modeling.

Explore how gamma distribution models customer transaction rates, deriving lambda via likelihood estimation to predict Poisson-based repurchases and future purchase counts.

Explore how Poisson–gamma and beta–geometric models link transaction rates, churn, and customer lifetime value in Python, showing how lambda, p, and the corresponding likelihoods shape predictions.

Explore the CLV model's statistical background by linking Poisson, geometric, and gamma distributions to a likelihood framework that estimates churn probability and future spend from purchase frequency.

Learn to build a BG/NBD model with the lifetimes package in Python, fitting the model to data using frequency, recency, and time since first transaction to predict future purchases.

Build a customer lifetime value model with python's lifetimes, fitting using frequency and recency data. Analyze the frequency–recency matrix plot to identify valuable customers and the importance of recency.

Explain the probability_alive_matrix within a customer lifetime value model, focusing on churn risk, active probability, and how recency and frequency drive purchase likelihood.

Apply the CLV model to forecast daily purchase quantities for each customer using frequency, recency, and age, and create a 'predicted_purchase' feature to rank customers by expected purchases tomorrow.

Validate the CLV model with a period_transaction plot that compares predictions to actual values during the calibration period (training data).

Partition the data by time into calibration and holdout periods, add validation columns, and fit the BGF model on pre-date data; then compare predictions to holdout purchases using calibration_purchases_holdout_purchases plot.

Plot the historical probability of being alive for a specific customer using the plot_history_alive function from the lifetime plotting package, based on past transactions and a customer ID.

Select customers with at least one repeat purchase using pandas to create a returning customer summary and focus analysis on this targeted group.

Explore gamma-gamma modeling and its connection to the customer lifetime value model, focusing on predicting monetary value per transaction using the Python lifetime package.

Fit the gamma-gamma model with the Python lifetime package to estimate each customer's average transaction value, then use the predicted value to inform the customer lifetime value calculation.

Compare parametric and non-parametric CLV modeling with Python’s lifetime package, using recency, frequency and age features; contrast Poisson–beta with non-parametric methods such as gradient boosting and random forests.

Explore enriching customer lifetime value modeling by integrating score and discount data with transaction data, creating features like recency, frequency, and spend in Python, and comparing parametric versus nonparametric approaches.

Develop a flexible, nonparametric customer lifetime value model in Python by performing data exploration, feature generation (duration, late time, early time, frequency), and careful train-test partitioning with boosting techniques.

Learn to build a CLV model in Python by creating frequency, recency, and three-month frequency features, merging datasets by customer ID, pairing third-party score and discount, and evaluating boosting models.

Explore how gradient boosting builds tree models to predict residuals and refine forecasts, using Python implementations like XGBoost and LightGBM for customer lifetime value modeling.

Apply xgboost and gradient boosting to build a customer lifetime value model in python, using features like recency, frequency, customer age, and external data, with train-test split and mse evaluation.

Apply the xgboost regressor to build a customer lifetime value model in Python, train and evaluate with mse and rmse, and analyze key driver importance using gain and weight plots.

Learn how to apply XGBoost for a customer lifetime value model in Python, assess feature importance using gain and weight, and inspect top drivers to guide feature selection.

Learn to build a CLV model with LightGBM booster in Python, comparing LGBM regressor and classifier, tuning max_depth and learning rate, using fit with MSE objective.

Partition the data into training and testing sets (70 percent training, 30 percent testing) with X1, X2 features and y target, and monitor performance with a watchlist during Lightgbm model training to build a customer lifetime value prediction.

Build a customer lifetime value predictive model with Python using LightGBM, training with a watch list and two thousand two hundred trees, and evaluate with MSE and feature importance.