The G2M platform allows you to build models that fall into these broad categories:
Propensity models: also known as classifiers, propensity models are a type of supervised machine learning and usually predict a binary outcome such as 'Will this prospect buy my product?' or 'Will this customer cancel her service?' The algorithms available in the G2M platform to build a propensity model include:
Logistic regression
LightGBM
Random forest
Gradient boosting
XGBoost
Adaptive boosting
Extra trees
Clustering models: clustering models are a type of unsupervised machine learning and usually identify natural groupings, segments, or clusters in your data. Analysts use clustering to group prospects and customers into similar groups for the purpose of customer segmentation. It can also be applied to other use cases such as segmenting survey responses, operational events, etc. The algorithms available in the G2M platform to build a clustering model include:
K-Means
PCA / K-Means
BIRCH
DBSCAN
Gaussian mixture
Hierarchical agglomerative
Mean shift
OPTICS
Spectral clustering
Regression models: regression models are a type of supervised machine learning and usually predict a numerical variable such as total sales using a broad variety of inputs. The algorithms available in the G2M platform to build regression model include:
Linear regression
Random forest regression
Gradient boosting regression
XGBoost regression
LASSO regression
Ridge regression
Bayesian ridge regression
A/B testing models: A/B testing models are a type of analysis that compares outcomes by adjusting for a number of controlled variables so the comparison is as apples-to-apples as can be:
Propensity score matching
Propensity score blocking
Propensity score stratification
Propensity score weighting
Ordinary least squares
Marketing mix models: marketing mix models are a type of specialized regression that accounts for lagging and saturation effects commonly found in marketing mix modeling problems:
Bayesian regression with adstock
Bayesian regression with Hill adstock
Bayesian regression with carryover