What is Retentioneering?

Retentioneering is a Python framework to process and analyze clickstreams, event streams, trajectories, and event logs. You can segment users, clients (agents), explore user behavior patterns, build ML pipelines to predict agent category or probability of target event based on historical data.

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. Retentioneering is also developing customer journey map simulation engines that allow the data scientists to explore the business impact of CJM mutations and optimize product and online marketing.

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.

Getting started

Complete documentation is available here.

Retentioneering is currently installable from PyPI:

pip3 install retentioneering

If you use Pandas dataframes to work with user behaviour data you can start using Retentioneering with just a few lines of code:

import retentioneering

# load sample user behavior data as a pandas dataframe: 
data = retentioneering.datasets.load_simple_shop()

# update config to pass columns names:
retentioneering.config.update({
    'user_col': 'user_id',
    'event_col':'event',
    'event_time_col':'timestamp',
})

Above we imported sample dataset, which is regular pandas dataframe containing raw user behavior data from hypothetical web-site or app in form of sequence of records {'client_id', 'event', 'timestamp'}, and pass those column names to retentioneering.config. Now, let's plot the graph to visualize user behaviour from the dataset (read more about graphs here):

data.rete.plot_graph(norm_type='node',
                     weight_col='user_id',
                     thresh=0.2,
                     targets = {'payment_done':'green',
                                'lost':'red'})

Here we obtain the high-level graph of user activity where edge A --> B weight shows percent of users transitioning to event B from all users reached event A (note, edges with small weighs are thresholded to avoid visual clutter, read more in the documentation)

To automatically find distinct behavioral patterns we can cluster users from the dataset based on their behavior (read more about behavioral clustering here):

data.rete.get_clusters(method='kmeans',
                       n_clusters=8,
                       ngram_range=(1,2),
                       plot_type='cluster_bar',
                       targets=['payment_done','cart']);

Users with similar behavior grouped in the same cluster. Clusters with low conversion rate can represent systematic problem in the product: specific behavior pattern which does not lead to product goals. Obtained user segments can be explored deeper to understand problematic behavior pattern. In the example above for instance, cluster 4 has low conversion rate to purchase but high conversion rate to cart visit.

clus_4 = data.rete.filter_cluster(4)
clus_4.rete.plot_graph(thresh=0.1,
                        weight_col='user_id',
                        targets = {'lost':'red',
                                   'payment_done':'green'})

To explore more features please see the documentation

Step-by-step guides

• Visualize users behavior
• Users flow and step matrix
• Users behavioral segmentation
• Compare segments and AB tests
• Funnel analysis

Contributing

This is community-driven open source project in active development. Any contributions, new ideas, bug reports, bug fixes, documentation improvements are very welcome.

Retentioneering now provides several opensource solutions for data-driven product analytics and web analytics. Please checkout this repository for JS library to track the mutations of the website elements: https://github.com/retentioneering/retentioneering-dom-observer

Apps are better with math!:) Retentioneering is a research laboratory, analytics methodology and opensource tools founded by Maxim Godzi and Anatoly Zaytsev in 2015. Please feel free to contact us at [email protected] if you have any questions regarding this repo, or to obtain more tools that we are not able to provide though the public rep.