We have delivered deep cohort analytics for millions of consumers, tens of thousands of businesses through combination of unsupervised machine learning, NLP, and classification techniques.
Segmentation and cohort analysis are often performed using a mix of supervised and unsupervised machine learning models. This needs careful architecture of data models and data prep pipelines. Coditation has the experience and expertise to architect and delivers such complex data prep pipelines using Cloud Data warehouses (Snowflake, Redshift, BigQuery), Spark, Kafka, and various orchestration services/engines.
Segmentation is performed on a set of entity (typically a customer and/or business/brand) attributes. Some of these attributes are inferred through classification and regression techniques - especially in cases where 2nd and 3rd party data is involved. At Coditation, we have built and managed 10+ machine learning model chains powering segmentation of entities in retail/e-commerce, healthcare, telecom, and MarTech industries.
Segments change over the period of time - both in terms of their definition and data - hence, tracking and versioning of segment definitions and snapshots of the underlying data becomes imperative to analyze the performance of the segments as part of the data activation.
Working with several filters and grouping conditions makes the presentation of the data and insights complicated. Our UX designers, product managers, and data analysts have delivered 10+ segmentation and cohort analysis projects across 3 industry verticals. This enables you to deliver clutter-free, purpose-built, and insightful visualizations.