7 factors driving
predictive analysis to
dodge incoming losses

7 factors driving predictive analysis to dodge incoming losses

Dec 04 2015

Predictive Analytics

1

Margin shrinkage or the uncontrollable loss of revenue is a real issue that is plaguing many industries now. Thus, the crucial concern for today’s industries is to devise business practices to not only curb losses, but also to predict and prevent losses even before they happen. Though the types of revenue losses differ from one industry to another, a common observation across all industry sectors is that most revenue losses are caused but either human errors or environmental deficiencies.

The Current Business Challenge

Here are some key statistics:

    • In the retail industry, revenue leakage equals half of the retailer’s net income.
    • In most industries, the absence of ‘real-time data analysis’ results in revenue loss
    • Because of lack of predictive analytics, most data reports cannot prove the correlation between identified shrinkage factors and shrinkage.
    • Lack of analytics capabilities result in insufficient solutions for preventing losses.

In the data-driven age, the most powerful loss-prevention solution is available within predictive analytics. Most industries can judiciously apply predictive analytics to not only reduce shrinkage but also to avoid to altogether. Some common factors that form the core foundation of predictive analytics for loss prevention in business are:

  • Assessing risks associated to business losses
  • Identification of human errors
  • Identification of environmental lapses
  • Extracting value from operational data analysis
  • Predictive modeling of real-world business situations
  • Tracking shrinkage with domain expertise (reactive to proactive)
  • Devising strategies to mitigate losses

Factor 1: Assessing risks associated to business losses

The first driver of predictive analytics for loss prevention is risk assessment. For example, the TCS white paper, Predictive Analytics for HR discusses how HR leaders struggle to attract the right workforce due to budgetary and time constraints, and then struggle to retain the talent! Thus, both talent acquisition and talent attrition are major risks for the HR business. In Prepayment Fraud, the readers will find that the insurance industry is constantly worried about having to pay for millions of fraudulent claims every year! Thus, both prepayment and post-payment risk assessments and analytics may help to detect fraudulent claims before such payments are made.

Factor 2: Identification of human errors

Across industries, it is widely believed that human behavior contributes to much of the shrinkage or revenue losses. Thus, it is imperative to understand the role of the human factor in daily business operations. Operational data, when strengthened by predictive analytics, can provide solid insights into human errors and uncover strategies to counteract these human lapses. The discovery, identification, and analysis of human errors in businesses can only happen due to advanced predictive analytics capabilities.

Factor 3: Identification of environmental lapses

In the same way, environmental lapses can also be uncovered, identified, and analyzed in day-to-day, business situations.

Factor 4: Extracting value from operational data analysis

Big Data for Healthcare Payer indicates that big-data enabled predictive analytics can help to spot theft trends and patterns in fraudulent health-insurance claims. Similar analytics strategies can be applied in other industry sectors as well to extract valuable shrinkage trends and patterns from operational data.

Factor 5: Tracking shrinkage with domain expertise (reactive to proactive)

As discussed above, the ‘predictive’ approach can often result in improved payer outcomes, reduced cost, and enhanced profitability. Additionally, big data solutions are used to predict churn to avoid revenue losses

Factor 6: Predictive modeling of real-world business situations

Cognizant’s white paper titled Analytics to the Rescue talks about using predictive modeling as a proactive measure to prevent loss in the retail industry, but these same analytic strategies are equally applicable in any industry sector. For example, margin loss has a serious impact on the bottom lines of the retail and hospitality industries, and this paper offers proven techniques to mitigate operational and systemic losses that often go overlooked.

Factor 7: Devising strategies to mitigate losses

Predictive Response to Combat Retail Shrink indicates that advanced predictive analytics techniques can help uncover and predict trends to reduce shrinkage, increase revenue, and improve business processes.



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