Silk Data helps its clients optimize their businesses by using Predictive Analytics based on customer data and state of the art Machine Learning techniques. The predictions help determine the probability of future results for the business and chose the optimal strategy.
Time: 3 months
Predictive Analytics is the use of data, statistical algorithms, and machine learning techniques to determine the probability of future outcomes based on historical data. The goal is to go beyond knowing what has happened and provide a better assessment of what will happen in the future. Predictive Analytics is perhaps one of the most common uses of Machine Learning and AI in business as well as Text Summarization. This includes applications ranging from customer churn prediction and credit scoring to equipment failure prediction.
Predictive Analytics business goal is the optimization of corporate data's processes through the use of hidden patterns. Predictions, if made with sufficient time lag, make it possible to improve the customers lifespan, optimize the storage of spare parts, have better ads campaign or prevent leave of important employees.
There are a lot of sectors where Predictive Analytics can be relevant and applied. Here are some of them:
Rich experience in ML and AI allowed us to test Predictive Analytics in different areas. Moreover, each case has shown successful results, as well as some points to pay attention to. You can learn more about our experience below.
The goal of the project was to recommend a banner with maximal probability of user click. The project used the information like geolocation (country), referring domain, user language and the like to predict the banner the user would click. An important feature of this project was that most of the users do not click on any banners. Therefore, for reliable prediction it is necessary to collect tens to hundreds million records.
The goal was to predict the survival probability of animals at large farms depending on the animal history (illnesses, weight gain, blood tests and other important features). The key issue of this project was missing or incorrect data, such as animal weight of several tens of tons. Nevertheless, proper selection of algorithms and parameters lead to a successful pilot project.
The success of the Predictive Analytics project significantly depends on several key factors, namely
For more information on using Predictive Analytics, machine learning and artificial intelligence in business, please check our guide.