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 the predicted variables from the past occurrences and exploits it to predict future outcomes. Predictive analytics encompasses a variety of techniques from statistics and data mining that process current and historical data in order to make "predictions" about future events.
How It Works
In business, the models often process historical and transactional data to identify the risk or opportunity associated with a specific customer or transaction. These analyses weigh the relationship between many data elements to isolate each customer's risk or potential, which guides the action on that customer.
Advantages of Predictive Analytics:
- Identify Loyal Customers: Analyze historical data to identify loyal customers and develop retention plans for them.
- Rewards and Risks: Analyze historical and transactional data of the customer to identify the risk or opportunity associated with a specific customer.
- 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.
- Forecast the Marketing Budget: Analyze the marketing budget of previous years and predict where to spend more to maximize returns on marketing spend.
- Improved Pricing Strategy: Analyze the sales data and decide whether to increase or decrease the price for a particular product or service.
- Better Campaign Management Strategy: Analyze the click-through rates of previous campaigns and recommend the best timing to send the campaign for maximum click through.
Predictive analytics is widely used in making customer decisions. One of the most well-known applications is credit scoring, which is used throughout financial services. Scoring models process a customer's credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time. Predictive analytics are also used in insurance, telecommunications, retail, travel, healthcare, pharmaceuticals and other fields.
To know more about how predictive analytics can help your business, contact us!