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How Retailers Are Making More Informed Decisions With Data and Analytics


A Fortune 500 retailer sought objective, data-driven guidance to help determine its optimal insurance structure. The company conducted a risk financing optimization (RFO) exercise for its general and excess liability programs and undertook an economic cost of risk (ECOR) study. Upon completion, the company increased deductibles for its primary coverage, and moved its lead umbrella to another insurer.

The results: a 50% improvement in program efficiency and casualty premium savings of $2 million.

By using data-based tools to quantify and model risk, retailers can assign a value to the unknown, forecast the extent of potential financial damage, and determine what steps can be taken to prevent, manage, or transfer risk. In short, data and analytics can help drive intelligent business decisions and change.

In this issue of Taking Stock, we explore several ways retailers are using big data to minimize their total and economic cost of risk. For example, retailers are using data and analytics to:

  1. Organize and consolidate data.
  2. Capture and analyze potential volatility across risks.
  3. Forecast the probability of potential losses against comparable industry data.
  4. Identify potential risks, including those that haven’t historically led to losses.

For details, download the report.