Predictive analytics is at the heart of data-driven strategies that give organizations their competitive edge. Analytical models for long were based on hindsight. Organizations are moving to advanced analytics and are tightly integrating it into their key decision making process.
Predictive Analytics Capabilities
Address the entire analytical process:
- Data collection
Make use of our powerful :
Model-building, Evaluation and Automation capabilities.
Empower your analytics with our advanced model management and analytic decision management on premises or on cloud.
Build strong and effective business strategies using the predictive insights gained from analyzing Big Data
Applications of Predictive analytics :
Dividing a customer base into groups of individuals that are similar in specific ways such as age, gender, interests and spending habits is what customer segmentation is. This enables companies to accurately target customers who are most likely to buy their products with tailored marketing messages. It has been established that predictive analytics can identify potential customers better than traditional techniques.
A situation where insight into potential outcomes guides the decision made by you/your team is ideal for predictive analytics. Predictive algorithms are essential in discerning the risks involved in a particular decision/investment or to choose another course of action. Tailoring your decisions to ensure success and mitigate risk can be done by comparing present conditions against the past and analyzing the risk factor.
Predicting why and when customers end their relationship with a company is what churn prevention aims to do. As the cost of retaining an existing customer is much lower than acquiring a new one, churn can prove to be very expensive for a company. Predictive models that enable proactive intervention can be developed by harnessing the power of big customer data sets.
Any company’s cornerstone when it comes to planning is examination of prior history, seasonality, market moving events etc that help in realistic prediction of sales. Data mining can be used to anticipate the response from customers and their changing attitudes. Sales forecasting can be applied to short, medium or long term forecasting.
Each and every company will not have the same challenges, each company is unique and so are their challenges. Initially there may not be a need to use predictive analytics to get your company out of a tight fix, however, the right algorithm can help make sense of data that previously appeared meaningless. These insights obtained via the tools and algorithms of predictive analytics can help any organization stay ahead of the crowd. Those companies that can take raw data and turn it into actionable intelligence will thrive.
Machine Learning / Predictive Models
Survival Analysis | Marketing Mix | Demand Forecasting | Customer Churn Analysis | Segmentation/Clustering Analysis | Recommendation Engines | Pattern recognition | Lead Scoring | Credit Scoring | Outlier Analysis
ARIMA/ARIMAX | Linear Regression / Regression Tress | News Vendor Model | Random Forests | Logistic Regression | Support Vector Machines | k – Nearest Neighbours | Apriori