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What is Retentioneering?

Retentioneering is a Python framework and library (github link) to assist product analysts and marketing analysts as it makes it easier to process and analyze clickstreams, event streams, trajectories, and event logs. You can segment users, clients (agents), build ML pipelines to predict agent category or probability of target event based on historical data.

In a common scenario you can use raw data from Google Analytics BigQuery stream or any other silimal streams in form of events and their timestamps for users, and Retentioneering is all you need to explore the user behavior from that data, it can reveal much more insights than funnel analytics, as it will automatically build the behavioral segments and their patterns, highlighting what events and pattern impact your conversion rates, retention and revenue.

Retentioneering extends Pandas, NetworkX, Scikit-learn for in-depth processing of event sequences data, specifically Retentioneering provides a powerful environment to perform an in-depth analysis of customer journey maps, bringing behavior-driven segmentation of users and machine learning pipelines to product analytics.

You don’t need to be a Pandas expert, all the functions are specifically designed for analytics tasks, reduce data wrangling and simplify data cleaning and visualization.

Product analysts can apply Retentioneering Tools as a Python framework to explore, grow, and optimize the product based on deep analysis of user trajectories. Using Retentioneering you can vectorize clickstream logs and cluster user trajectories to automatically identify common successful or churn patterns. You can explore those patterns using our tools such as graph visualizer, step matrix, multiple clustering, and segmentation engines, and many others.

If you have csv datasets you can start with user behaviour data and try Retentioneering with “pip install retentioneering” and a few lines of code. Sample datasets are included in package for quick start.

Contents

TUTORIAL:

• What is Retentioneering?
• Installation
• Getting started
• Release notes

TOOLS DESCRIPTION:

• Visualize users behavior
  • Before you start
  • Introduction to rete.plot_graph()
  • rete.plot_graph() parameters
  • Edge weighting options
• Users flow and step matrix
  • Step matrix intro
  • Basic example
  • Thresholding
  • Targets analysis
  • Centered step matrix
  • Custom events sorting
  • Differential step matrix
• Explore users behavior with transition matrix
  • Before you start
  • Explore users transitions between events with adjacency matrix
• Funnel analysis
  • Basic example
  • Targets grouping
  • Users grouping
• Behavioral segmentation
  • Basic example
  • Trajectories vectorization
  • Clusterization
  • Visualizing results
  • Exploring individual clusters
  • Compare clusters
  • Visualize cluster using project()
• Compare segments and AB tests
  • Statistical comparison
  • Basic example
  • More complex metrics

API REFERENCE:

• Core functions
  • step_matrix
  • plot_graph
  • get_clusters
  • project
  • extract_features
  • compare
  • funnel
  • get_edgelist
  • get_adjacency

back to github: back to github.