Data mining was the buzzword about 10 years ago, but the terms predictive modeling and predictive analytics have become more popular recently. Are they all the same thing? Not exactly, but they are all related.
Data mining has been defined in a lot of ways, but at the heart of all of those definitions is a process for analyzing data that typically includes the following steps:
- Formulate the problem
- Accumulate data.
- Transform and select data.
- Train models.
- Evaluate models.
- Deploy models.
- Monitor results.
- Who’s likely to respond to a campaign?
- How much do first-time purchasers usually spend?
- Which customers are likely to default?
Forecasting or predictive modeling
I run into decision makers all the time who have a hard time understanding the difference between forecasting and predictive modeling. Here’s a quick analogy to illustrate the difference:
- Forecasts tell you how many ice cream cones will be sold in July, so you can set expectations for planned costs, profits, supply chain impacts and other considerations.
- Predictive models tell you the characteristics of ideal ice cream customers, the flavors they will choose and coupon offers that will entice them.
Consider these real-world forecasting examples. The hospitality industry uses forecasting to determine demand for particular rooms or properties. Financial companies use it to generate accurate sales forecasts, which feed into the planning process. Bankers use it to generate cash forecast for ATMs and branches. Retailers create forecasts to manage pricing, staffing and inventory.
Predictive modeling delivers a different set of answers. In retail, predictive modeling identifies the most profitable customers and the underlying reasons for their loyalty. In finance, credit scoring is a type of predictive modeling used to grow customer profitability and reduce risk exposure. In the life sciences, it helps companies find promising new molecular drug compounds.
Optimization is about making right decision across multiple dimensions with known contraints. In Banks, people use optimization techniques to determine optimal cash that they need to keep in ATMs and branches to ensure better customer service and minimize idle cash. In Retail, optimization techniques are used to determine optimal stock of premium items to eliminate number of stock outs and minimize inventory cost.
What is the difference between Text mining & Text Analytics?
Text mining was a buzz word 10 years ago but the term text analytics has become popular recently. Text mining and Text analytics can be used interchangeably. Text analytics converts text into data for analysis via application of natural language processing (NLP) and analytical methods.
In manufacturing, it is used to identify emerging issues or defect in a product. In retail, it is used to analyze customer feedback. In telecom industry, it is used to analyze customer complains and identify potential churners.