Understanding consumer segments is key to any successful business. Analytically, segmentation involves clustering a dataset to find groups of similar customers. Traditional approaches are often limited to certain data structures. Breaking out of these restrictions has been one of our top priorities since starting the company.

Subscriptions are no longer just for newspapers. In this post we explore how we can leverage multi-state churn analysis so brand decision makers can focus their outreach and acquisition efforts toward customers that have a higher probability of remaining in an active state for a longer period of time.

One of the most important steps in building a statistical model is deciding which data to include. With very large datasets and models that have a high computational cost, impressive efficiency can be realized by identifying the most (and least) useful features of a dataset prior to running a model.

Businesses often want to better understand their customers by segmenting them along a common set of attributes. In this post, we’ll show how to build segments based purely on the products that customers have purchased.