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Better Insights through Business Analytics

Big Data and Health Care Insurance

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This article was originally published in the Spring 2013 edition of OnAnalytics, published by the Institute for Business Analytics at Indiana University’s Kelley School of Business.

This article focuses on insights from Natalie Basich, a 2013 MBA graduate majoring in Finance and Business Analytics. This article is based on Natalie’s experiences during an internship with a major health care insurer in the Midwest.

Many industries face the challenges of big data and an ever-expanding number of applications, databases, and data warehouses. But the health care industry and health care insurers grapple with a unique set of tests. Once collectors of information, health care insurance companies now use data to make informed decisions, and they do so in a rapidly changing regulatory environment.

During her internship, Natalie Basich worked with a major health care insurance company to analyze how its retail centers were performing. The company wanted a more robust financial model that pulled multiple years of data and allowed it to view sales projections as well as assess past, current, and potential performance.

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‘Spackling’ as an Alternative to Offshore Production

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This article was originally published in the Spring 2012 edition of OnAnalytics, published by the Institute for Business Analytics at Indiana University’s Kelley School of Business.

This article focuses on insights from  Kyle Cattani, a professor in the department of Operations and Decision Technologies. Find more information about this research in the article, “Lowest Cost May Not Lower Total Cost: Using ‘Spackling’ to Smooth Mass-Customized Production“.

The past decade has witnessed a significant shift in manufacturing from the U.S. (and other western nations) to the Far East, in search of less-expensive, efficient manufacturing. For U.S. manufacturers that produce both make-to-order (MTO) and make-to-stock (MTS) items, a typical strategy might involve a (more expensive) flexible domestic factory creating the custom products and a (less expensive) overseas facility to make the standard variants. While this setup might intuitively appear to save money by minimizing the cost of the standard items, a more profitable approach for some companies might be to maximize use of the flexible domestic factory to also produce the standard items. By creating an analytical model and applying it to a real-world test case, the researchers show how some manufacturers can actually save money by foregoing offshore production in favor of an approach they refer to as “spackling,” wherein the flexible factory’s unused production capacity is filled in with production of standard-issue items. They also analyze a third strategy – “layered spackling” – that incorporates offshore production but also makes use of the flexible factory’s downtime between custom orders.

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A Recipe for Retail Assortment

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This article was originally published in the Spring 2012 edition of OnAnalytics, published by the Institute for Business Analytics at Indiana University’s Kelley School of Business.

This article focuses on insights from Venkat Venkataramanan a professor of Decision Sciences and Vice Provost for Finance and Strategy. Find more information about this research in the article, “Managing Variety on the Retail Shelf: Using Household Scanner Panel Data to Rationalize Assortments“. 

As the variety of products on the market continues to expand, retailers are faced with difficult decisions about their stock assortments. The fear of failing to stock customers’ preferred brands can cause retailers to fill their shelves with an overwhelming assortment of products, a potentially costly strategy that doesn’t always yield returns. Taking into account the heterogeneity of customer preferences, customer’s willingness to substitute a second or third choice if their first choice is unavailable, and the dissatisfaction customers experience when they cannot purchase their preferred brands, the researchers propose and test a model for retail category assortment that allows managers to balance customer satisfaction with short-term profit.  (more…)

Using Context to Improve Fraud Tests

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This article was originally published in the Fall 2012 edition of OnAnalytics, published by the Institute for Business Analytics at Indiana University’s Kelley School of Business.

This article focuses on insights from Joseph Cherianthundam, who at the time of this writing was a Managing Director specializing in financial analytics for StoneTurn, and international dispute consulting and financial investigations firm, 

One of the many ways that businesses can use analytics to improve their financial health is in the area of occupational fraud detection. This type of fraud, in which an employee abuses his or her role for personal gain, is estimated to cost organizations an average of 5% of annual revenues, or a worldwide total loss of $3.5 trillion (Association of Certified Fraud Examiners, 2012). There are many sophisticated techniques now used to detect occupational fraud including:

  • Segmentation and classification of transactions to model normal and abnormal patterns
  • Encoding programs to deliver alerts when abnormalities are detected
  • Testing for missing/anomalous values in sequential and other data
  • Analyzing text data – for example, addresses – to identify suspect entries (more…)

Estimating Consumer Search Costs in Online Markets

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This article was originally published in the Fall 2012 edition of OnAnalytics, published by the Institute for Business Analytics at Indiana University’s Kelley School of Business.

This article focuses on insights from Matthijis R Wildenbeest, Associate Professor of Business Economics. Find more information about this research in the article, “Testing Models of Consumer Search Using Data on Web Browsing and Purchasing Behavior”.

Theoretical models of consumer search behavior offer two primary frameworks: a fixed sample size search model, in which customers cost-compare across a fixed, predetermined number of retailers, and a sequential search model, in which customers investigate one retailer after another until they find an acceptable price and make a purchase. Both models assume that searching has a cost to the consumer, which factors into the decision to buy or continue searching.

These models were developed in the 1960s and ’70s, however, prior to the advent of the Internet. How do today’s consumers behave online, when searching requires only a few keystrokes?

 

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Triple Jeopardy for Small Brands

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This article was originally published in the Spring 2012 edition of OnAnalytics, published by the Institute for Business Analytics at Indiana University’s Kelley School of Business.

This article focuses on insights from Lopo Rego an associate professor in Kelley’s department of Marketing. Find more information about this research in the article, “Excess Loyalty in CPG Markets: A Comprehensive Examination“. 

This study tests the emerging theory of “Triple Jeopardy” for small brands in the marketplace: brands with less market share not only have fewer customers but are also purchased less often (Double Jeopardy) and, as this study demonstrates, experience lower levels of loyalty than larger brands. Using a comprehensive sample that captures a large percentage of purchases at mass merchandisers, grocery stores, and drug stores in the United States, the researchers demonstrate the fallacy of the commonly held assumption that small brands survive through the repeat purchase of a small but fiercely loyal minority. They also examine the influence that traditional marketing efforts have on loyalty, finding that promotional efforts actually result in decreased brand loyalty.

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Vehicle Replacement in Humanitarian Operations

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This article was originally published in the Fall 2012 edition of OnAnalytics, published by the Institute for Business Analytics at Indiana University’s Kelley School of Business.

This article focuses on insights from Alfonso Pedraza Martinez an associate professor in Kelley’s department of Operations and Decision Technologies. Find more information about this research in the article, “Vehicle Replacement in the International Committee of the Red Cross“

Transportation represents an enormous overhead cost for humanitarian agencies, second only to personnel. The International Committee of the Red Cross (ICRC) relies on its fleet of 4 x 4 vehicles to transport people and aid across territories that are often in conflict and roads that are rarely paved. Because the reliability of the fleet is critical to the ICRC’s mission, the organization’s vehicle replacement policy is a topic of major importance. With this study, the researchers examine the ICRC’s Standard Replacement Policy (SRP) in the context of the organization’s data on vehicle usage and replacement. They find that the current SRP is not being followed due to a misalignment of incentives between the organization’s headquarters (HQ) in charge of purchasing the vehicles and the national delegations (ND) that use them. Based on the usage and sales data, the researchers propose a new policy yielding cost savings. 

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Unblocking Patient Flow

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This article was originally published in the Fall 2012 edition of OnAnalytics, published by the Institute for Business Analytics at Indiana University’s Kelley School of Business.

This article focuses on insights from Kurt Bretthauer, professor of Operations & Decision Technologies. Find more information about this research in the article, “Blocking In Healthcare Operations: A New Heuristic and an Application“

Hospitals face the dual imperative of delivering the most effective medical care while attempting to minimize costs in an environment of limited resources and increasing expenditures. One crucial determination is the mix of inpatient beds provided for different levels of care. Viewed in isolation, post-acute or skilled nursing units may not appear as vital or as cost-effective as more intensive treatment units. If these post-acute care units fill, however, patient flow will be blocked out of – and into – more acute care. With this study, the researchers developed a model for minimizing this blocking while also addressing a variety of management considerations such as overall revenue, patient acuity, and the objective of not turning patients away from the hospital.

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A Superior Strategy for Predicting Earnings and Picking Stocks

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This article was originally published in the Fall 2012 edition of OnAnalytics, published by the Institute for Business Analytics at Indiana University’s Kelley School of Business.

This article focuses on insights from James M. Whalen, the chairperson of the Kelley School of Business department of Accounting. Find more information about this research in the article, “Can financial statement analysis beat consensus analysts’ recommendations?”

Analysts’ consensus recommendations are a tool used by investors, but do they really reflect the full information available from financial statement reports? With this study, Jim Wahlen and Matt Wieland developed and tested a rating system that predicts future changes in earnings based on financial statement information. When these future earnings predictions are used to design an investment strategy, the researchers’ stock portfolios significantly outperformed portfolios formed using consensus recommendations, suggesting that consensus recommendations fail to incorporate information that could more reliably predict future earnings changes and identify under- or overpriced stocks.

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Digital née Web Analytics: Driven by Search and Social Media

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This article was originally published in the Fall 2012 edition of OnAnalytics, published by the Institute for Business Analytics at Indiana University’s Kelley School of Business.

This article focuses on insights from Vaibhav Gardé, who at the time of the article’s publishing was a Marketing Principal in the Interactive Marketing practice at FedEx.

Digital marketing today is driving rapid adoption of measurement techniques across devices and channels. According to Forrester Research, the shift to digital marketing is placing a premium on web intelligence: the collection, measurement, and utilization of multichannel digital data to drive mutually beneficial customer relationships. A 2011 Forrester survey shows that 84% of businesses are currently using web analytics technology. This market is expected to evolve into three main categories: Enterprise Marketing Platforms, Online Marketing Suites, and Digital Analytics Specialists. The technology helps track the influence of online channels on sales, customer experience, loyalty, and issue resolution, among other things.

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