Uplift Modeling Made Easy

Review modern uplift data, define id, dependent, treatment, and predictors, and explain splitting data into learning and validation samples to build and validate predictive models.

Explore uplift modeling methodology with a high-level overview of three approaches, assess model performance against campaign results, and learn thresholds for selecting incremental responders in test and control groups.

Explore uplift modeling methods, comparing separate test and control models with a one-model approach using interaction terms to optimize treatment impact and lift curves.

Explore three uplift modeling approaches, including decision-tree based and interaction-based methods, with stopping criteria to prevent overfitting and identify incremental responders for marketing treatment.

Explore a five-step uplift modeling process that covers data preparation, data quality control, variable selection, transformation, and model development and validation using SAS outputs.

Demonstrate a SAS macro-based data qc workflow for modeling data, including contents, dependent variable distribution, and cross-tab checks, with macro variables for dataset, id, and long-name renaming.

Learn to generate and interpret SAS macro QC reports, including numeric variable summaries, data duplication checks, variable exception findings, and a quick look at the modeling dataset.

Transform data for modeling by removing high-missing values or identical variables. Then split into training and validation samples with a random seed and verify with a quick QC in SAS.

Select the top 50 variables by p-values, assign them to a macro variable, and print results to the SAS log to illustrate uplift from significant p-values and interactions.

Explore how a SAS macro analyzes response rates across groups defined by the number of crakes in the last 12 months, comparing training and validation samples.

Explore how to generate and evaluate interaction effects in uplift modeling using SAS macros, comparing training and validation samples to assess stability of predictor–outcome relationships.

Demonstrates valuable variable transformations using the transformation macro, including imputation of missing values and setting low and high values for training and validation samples.

Demonstrates variable transformations for uplift modeling, handling missing data as zero, and exploring interaction effects to preserve relationships across training and validation samples.

Demonstrate variable transformations in uplift modeling by creating interaction variables with I_ prefix and transform variables with t_ prefix using a SAS macro. Apply process to training and validation data.

Explore stepwise regression and diagnostics in uplift modeling, using SAS output to assess convergence, null-hypothesis tests, residual chi-square, and to generate a model scoring equation with a SAS macro.

Develop and validate an uplift model demo by scoring training and validation samples with logit-based equations, generating incremental response probabilities, and interpreting treatment interactions.

Evaluate model performance using AUC to separate responders from non-responders on training and validation samples, and rank models by scores via a live chat to form ten equal cohorts.

Explore uplift modeling by comparing training and validation samples, using area under the curve and treatment versus control response rates to identify high-response groups and incremental value.

Present concise modeling results to both technical and non-technical audiences, covering business objective, analytical approach, expected impact, and model performance with final variables.