Risk Metrics: Leverage and Reputational Risk

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From the course by Duke University

Business Metrics for Data-Driven Companies

4252 ratings

Duke University

Business Metrics for Data-Driven Companies

4252 ratings

Course 1 of 5 in the Specialization Excel to MySQL: Analytic Techniques for Business

In this course, you will learn best practices for how to use data analytics to make any company more competitive and more profitable. You will be able to recognize the most critical business metrics and distinguish them from mere data. You’ll get a clear picture of the vital but different roles business analysts, business data analysts, and data scientists each play in various types of companies. And you’ll know exactly what skills are required to be hired for, and succeed at, these high-demand jobs. Finally, you will be able to use a checklist provided in the course to score any company on how effectively it is embracing big data culture. Digital companies like Amazon, Uber and Airbnb are transforming entire industries through their creative use of big data. You’ll understand why these companies are so disruptive and how they use data-analytics techniques to out-compete traditional companies.

From the lesson

Introducing Business Metrics

Welcome! This week we will explore business metrics - the critical numbers that help companies figure out how to survive and thrive. Inside every pile of data is a vital metric trying to get out! By the end of this week, you will be able to: distinguish business metrics from mere business data; identify critical business metrics such as cash flow, profitability, and online retail marketing metrics; distinguish revenue, profitability and risk metrics; and distinguish traditional from dynamic metrics. Included in this week’s course materials is a Cash Flow and P&L statement for Egger’s Roast Coffee, as a supplemental document, so be sure to review it carefully and refer to the glossary for key information.

Revenue Metrics – Traditional Enterprise Sales Funnel8:44

Revenue Metrics - Amazon.com as a Leading Example of Use of Dynamic Metrics - Part 18:37

Revenue Metrics - Amazon.com as a Leading Example of Use of Dynamic Metrics - Part 26:31

Profitability/Efficiency Metrics: Inventory Management10:31

Profitability/Efficiency Metrics: Hotel Room Occupancy Optimization9:41

Risk Metrics: Leverage and Reputational Risk4:19

Meet the Instructors

Daniel Egger
Executive in Residence and Director, Center for Quantitative Modeling
Pratt School of Engineering, Duke University
Jana Schaich Borg
Assistant Research Professor
Social Science Research Institute

The most common sources of business risk seem to stem from excessive leverage.

When a company owes more money than it is worth, it is unlikely to survive.

If you're in a business that depends upon making loans, and you are also leveraged,

the risk to you if your customers default is magnified many times.

In the United States, before the financial crisis of 2008,

banks were allowed to borrow $33 for every $1 that they held in equity.

So imagine a bank that could borrow money at 2% and lend it out at 3%.

If they had $10 million in equity they could borrow $330 million in debt,

lend out that money at 3% and

make a gross profit before expenses of 3.3 million per year.

Or a 33% return on their 10 million in equity.

However, only a very small portion of their loans

needed to become unrecoverable or in default for the bank to become insolvent.

10 million out of 333 million is only 3%.

Another type of risk is reputational, damage to your brand and

ability to sell in the future.

One area where any restaurant chain, manufacturer, grocery store, or retailer

is extremely vulnerable to bad publicity and damage to reputation is when they have

sold a food product that is tainted or contaminated and makes customers sick.

One successful example of risk management using big data

in the product recall space is Costco's rapid response to product recalls.

The metric for Costco is how long it takes them to contact every potentially

at-risk customer, when what's called a level one product recall occurs.

Because Costco requires all of its customers to purchase an annual

membership, and their membership database contains address information and

often email and mobile phone numbers as well, it's relatively easy for

Costco to track down customers.

This is easy because Costco stores a record of every purchase made

by every customer at the individual product ID, or SKU level.

So that data is indexed and when a product is recalled by the manufacturer,

as happened twice in the spring of 2014, with peanut butter and again with peaches.

Within 24 hours,

Costco was able to identify all of the more than 50,000 customers,

who had bought the product and used the fastest available means to contact them,

in some cases email and phone calls, but in any case a letter to a mailing address,

and they were able to prevent many, many people from getting sick.

An even more interesting example of using Costco's database to track

an epidemic to its source occurred in January, February of 2010,

when 272 people in 44 different states

became ill with a single genetically identical strain of salmonella.

United States Centers for Disease Control, or CDC, sought to track

down the source of contamination, but were initially unsuccessful.

Until someone had the very bright idea of analyzing Costco purchase records.

For those in the group of 272 who might have shopped at Costco.

Since Costco membership reaches about one-quarter of US families,

it would be reasonable to assume that this was about 70 people.

It was then possible to identify what food items those 70 had purchased in common.

The only common product was pepper salami, manufactured by a small sausage company,

Danielle International, based in Pascoag, Rhode Island, and

the contaminant turned out to be one single 25-pound box of crushed red

pepper provided by wholesaler, wholesome spice, based in Brooklyn, New York.

Not so wholesome after all.

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