Ibis Data Analytics (IDA)
Systematic solution for Churn prevention available to everyone
IDA software solution is intended to solve Churn problems in Telco operators as well as banks, but can be used for any binary classification problem in various industries. The solution is intuitive and very easy to implement, so it can be used by those who have no experience in the field of Data Science, as well as Data Scientists themselves to speed up their work. IDA aims to automate the machine learning process, whose purpose is to construct algorithms that are able to learn based on experience and then adapt to new situations, ie. make predictions.
All that is required is for the user to enter historical data and data over which he wants to make predictions. With the click of a button, an automated process of checking and processing data, testing various algorithms and setting their parameters is started. Estimation of all tested models is also performed and the best one is selected, which is used for making predictions.
- Testing tens of different models for specific dataset
- Defines variables with the biggest correlation with the targeted case
- Defines model that fits the best
- Ease of use thanks to intuitive solutions
- Allows people without technical knowledge in the field of Data Science to quickly get answers
- No integration is required, usable results can be obtained in a few minutes
- Accelerates and facilitates the work of experienced Data Scientists
How IDA can help you solve Churn
If you want to keep the Churn issue under control, ie. To have a clear insight into which users are most likely to leave your company in the coming period, our software solution will give you all necessary information so you can act proactively and prevent user outflow.
IDA will give you answers to the following questions through visual interpretation:
- What are the parameters that most affect the departure of users and why
- How accurate the model is
- Which users are most likely to leave
- Which is why they are most likely to leave
Based on these issues, it is possible to define a strategy that would:
- Reduced the percentage of users leaving the company (by looking at the variables that generally lead to user departures)
- Prevented individual users from leaving (by looking at variables that affect their probability of leaving)
Functionalities that IDA brings you
- In relation to the data entered by the platform user, information is displayed for the model that gave the best results. The information concerns metrics for calculating error levels, as well as the display of key variables.
- After the best model is automatically selected, the drop-down list shows all the observations from the test set sorted by the probability that the observed scenario will occur. By selecting an individual observation, the attributes that most influence the possibility of the observed scenario being realized are shown.
