Predictive Software

Churn Prediction

Churn Prediction is a SaaS solution that predicts which of your users will quit before they do so.

How it works

Using its API, it receives user events, can predict on a pool of active users and communicate which of those users will quit by next week. A prediction will be issued every day by a model powered by a deep learning ensemble. The model learns from past behavior of other users in order to predict on current users.

To start using it, first contact us to discuss how to best tailor the service to your needs. After signing with us, you'll need to feed user events from your application to our API. The API will publish predictions daily, which you can then use to incentivise your users to stay.

Depending on the specifics of your application, it might take some time until the data collected yields a strong enough learner. During this time, the service is free and the API will provide a breakdown of the model's performance.

Anonymized breakdown of model performance

Optionally, we can provide a service that learns from the incentives given (provided that you're sending that feedback to the API), what worked and what didn't, and recommend what incentive to use for each user specifically so that you maximize your revenue and retention and further automate your application.

Learning duration and accuracy

The average accuracy achieved on real data so far was 85%. In order to reach it, the data required was on average about 3 months at 10,000 DAU.

Pricing

This service is billed monthly, with the months until accuracy exceeds 70% being free. Afterwards, the cost will be set depending on these factors:

*- A predictor is quantifiable event type that can be aggregated on a daily basis. Example predictors: hard currency spent, number of missions completed, number of main page requests etc.

Application Programming Interface


/api/v1/send_events

This is a POST call that accepts the following params:

application_id - the id assigned for your application
events - JSON-encoded array of events, e.g.

            [{"user_id":54321,
              "date":"2017-09-06",
              "predictor":"hard_currency_spent",
              "value":25},
             {"user_id":54321,
              "date":"2017-09-06",
              "predictor":"daily_missions",
              "value":2}
        

If you need to add new predictors, make sure you communicate it with us, as only accepted predictors will work.


/api/v1/predictions/<application_id>/<day>/<anticipation>

This is a GET call that returns a JSON-encoded list of users that are predicted to quit sometime between the given day and anticipation days later.


/api/v1/performance/<application_id>/<day>

This is a GET call that returns the HTML formatted table computing the performance of that day's prediction.

Contact

I'm Valentin Brasso, the owner of Predictive Software, a company focused on providing machine learning solutions. I have a background in Web Development (skilled in Python, SQL, Couchbase, Node.js, and PHP) and have made the switch two years ago to Machine Learning. Strong entrepreneurship professional with a Bachelor's degree in Automated Control and Computer Science from Polytechnic University of Bucharest. You can write to me at .