<div align="left">
[![Rete logo](https://github.com/retentioneering/pics/blob/master/pics/logo_long_black.png)](https://github.com/retentioneering/retentioneering-tools)
[![Pipi version](https://img.shields.io/pypi/v/retentioneering)](https://pypi.org/project/retentioneering/)
[![Telegram](https://img.shields.io/badge/channel-on%20telegram-blue)](https://t.me/retentioneering_meetups)
[![Python version](https://img.shields.io/pypi/pyversions/retentioneering)](https://pypi.org/project/retentioneering/)
[![Downloads](https://pepy.tech/badge/retentioneering)](https://pepy.tech/project/retentioneering)
[![Travis Build Status](https://travis-ci.com/retentioneering/retentioneering-tools.svg)](https://travis-ci.com/github/retentioneering/retentioneering-tools)
## What is Retentioneering?
Retentioneering is a Python framework and library 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 isights 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.
Most recent is Retentioneering 2.0.0, this version has major updates from 1.0.x
and it is not reverse compatible with previous releases due to major syntax changes.
With significant improvements we now provided architecture and the solid ground for
farther updates and rapid development of analytical tools. Please update, leave your
feedback and stay tuned.
[![intro 0](https://github.com/retentioneering/pics/blob/master/pics/rete20/intro_0.png)](https://github.com/retentioneering/retentioneering-tools)
## Changelog
This is new major release Retentioneering 2.0. Change log is available [here](https://retentioneering.github.io/retentioneering-tools/_build/html/release_notes.html).
Complete documentation is available [here](https://retentioneering.github.io/retentioneering-tools/).
## Installation
Option 1. Run directly from google.colab. Open google.colab and click File-> “new notebook”.
In the code cell run following to install Retentioneering (same command will install directly
from Jupyter notebook):
```bash
!pip3 install retentioneering
```
Option 2. Install Retentioneering from PyPI:
```bash
pip3 install retentioneering
```
Option 3. Install Retentioneering directly from the source:
```bash
git clone https://github.com/retentioneering/retentioneering-tools
cd retentioneering-tools
python3 setup.py install
```
## Quick start
[Start using Retentioneering for clickstream analysis](https://retentioneering.github.io/retentioneering-tools/_build/html/getting_started.html)
Or directly open this notebook in [Google Colab](https://colab.research.google.com/github/retentioneering/retentioneering-tools/blob/master/docs/source/_static/examples/graph_tutorial.ipynb) to run with sample data.
Suggested first steps:
```python
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
{'user_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](https://retentioneering.github.io/retentioneering-tools/_build/html/plot_graph.html)):
<div align="left">
```python
data.rete.plot_graph(norm_type='node',
weight_col='user_id',
thresh=0.2,
targets = {'payment_done':'green',
'lost':'red'})
```
[![intro 1](https://github.com/retentioneering/pics/blob/master/pics/rete20/graph_0.png)](https://github.com/retentioneering/retentioneering-tools)
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](https://retentioneering.github.io/retentioneering-tools/_build/html/clustering.html)):
<div align="left">
```pyhton
data.rete.get_clusters(method='kmeans',
n_clusters=8,
ngram_range=(1,2),
plot_type='cluster_bar',
targets=['payment_done','cart']);
```
[![intro 1](https://github.com/retentioneering/pics/blob/master/pics/rete20/clustering_2.svg)](https://github.com/retentioneering/retentioneering-tools)
<div align="left">
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.
```python
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'})
```
<div align="left">
[![intro 1](https://github.com/retentioneering/pics/blob/master/pics/rete20/graph_1.png)](https://github.com/retentioneering/retentioneering-tools)
To explore more features please see the [documentation](https://retentioneering.github.io/retentioneering-tools/)
## Step-by-step guides
- [Visualize users behavior](https://retentioneering.github.io/retentioneering-tools/_build/html/plot_graph.html)
- [Users flow and step matrix](https://retentioneering.github.io/retentioneering-tools/_build/html/step_matrix.html)
- [Users behavioral segmentation](https://retentioneering.github.io/retentioneering-tools/_build/html/clustering.html)
- [Compare segments and AB tests](https://retentioneering.github.io/retentioneering-tools/_build/html/compare.html)
- [Funnel analysis](https://retentioneering.github.io/retentioneering-tools/_build/html/funnel.html)
## 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](https://www.linkedin.com/in/godsie/) and
[Anatoly Zay
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温馨提示
什么是保持运动? Retentioneering是一个Python框架和库,可帮助产品分析人员和市场分析人员使用,因为它使处理和分析点击流,事件流,轨迹和事件日志变得更加容易。 您可以细分用户,客户(代理),构建ML管道以根据历史数据预测代理类别或目标事件的概率。 在常见情况下,您可以使用事件和时间戳的形式使用Google Analytics(分析)BigQuery流或任何其他Silimal流中的原始数据给用户使用,而Retentioneering就是您从该数据中探索用户行为所需要的一切,它可以揭示更多的远见而不是渠道分析,因为它会自动构建行为细分及其模式,突出显示哪些事件和模式会影响您的转化率,保留率和收入。 Retentioneering扩展了Pandas,NetworkX,Scikit-learn,以进行事件序列数据的深入处理,特别是Retentioneering提供了一个强大的环境来执行对客户旅程图的深入分析,从而将行为驱动的用户细分和机器学习管道带入产品中分析。 最新的是Retentioneering 2.0.0,此版本从1.0.x起具有重大更新,由于主要的语法更改,
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