Predictive Analytics
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
What is Predictive Analytics?
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.
Purpose
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.
How does it work?
- Data analysis. This step includes the visual analysis of data to spot evident patterns, value ranges, and to clarify plan for next steps.
- Data cleaning. This step includes elimination of records with missed values and outliers (improbable values). For example, if one analyses the house prices somewhere in US, the USD 1 for a house can be a legitimate deal, but this price is clearly non-useful for typical price prediction.
Benefits
- It helps avoid major risks. Analysis of data enables to minimize risks and identify the best strategy and business decisions.
- Business decision-making optimization. Predictive Analytics greatly facilitates the work of specialists when it comes to determining customers' needs and responds.
- Fraud detection. As cyber security increases, Predictive Analysis makes it possible to identify anomalies that can indicate cyber vulnerabilities and threats.
- Operational effectiveness improvement. It will help your business function in a faster.
Industries
There are a lot of sectors where Predictive Analytics can be relevant and applied. Here are some of them:
- Insurance
- Financial Services
- Healthcare
- Government Sector
- Real Estate
- Online Applications
- Manufacturing
- Logistics
- Agriculture
Our experience
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.
Click prediction for online ads
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.
Predictive Analytics of for animal farms
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.
Key takeaways
The success of the Predictive Analytics project significantly depends on several key factors, namely
- Access to SMEs (subject matter experts) and efficient collaboration with the customer
- Access to relevant data
- Data understanding, by discussing data preprocessing with customer and discussions of visualization and other analytics
For more information on using Predictive Analytics, machine learning and artificial intelligence in business, please check our guide.
