BluEnt creates reliable predictive models so organizations can better understand their consumers' future behavior.
Advantages of Predictive Analytics
- Drive Buyer Preferences: Analyze the customer-buying pattern and up-sell/cross-sell the related products and services to them.
- Demand Forecasting: Analyze the historical sales data and predict the demand for a product or service to better manage resources, inventory and supply chain management.
- Improved Pricing Strategy: Analyze the sales data and decide whether to increase or decrease the price for a particular product or service.
- Identify Loyal Customers: Analyze historical data to identify loyal customers and develop retention plans for them.
- Identify Rewards and Risks: Analyze historical and transactional data of a customer to identify the risk or opportunity associated with that specific customer.
- Forecast Marketing Budget: Analyze the marketing budget of previous years and predict where to spend more to maximize returns on marketing spend.
- Better Campaign Management: Analyze the click-through rates of previous campaigns and recommend the best timing to send the campaign for maximum click through.
How It Works
Predictive analytics is the process of extracting information from big data and using it to predict future trends and behavior patterns.
It captures relationships between explanatory variables and predicted variables from the past occurrences and exploits them to predict future outcomes. It encompasses a variety of techniques, from statistics and data mining, that process current and historical data.
A well-known application, for example, is credit scoring, which is used in financial services. Scoring models process a customer's credit history, loan application, customer data, etc., to rank-order individuals by their likelihood of making future credit payments on time.