Business Analytics - Highlights

Gary Cokins, CPIM

Illinois CPA Society Seminar October 21, 2014

Slideshare by:

About Gary

• Gary Cokins is an internationally recognized expert, speaker, and

author in performance improvement systems and cost

management.

• BS Degree (with honors) in Industrial Engineering/Operations

Research from Cornell University

• MBA (with honors) from Northwestern University

• Career highlights: FMC Corporation, Deloitte Consulting, KPMG,

EDS, SAS

• Professional affiliations: IMA, IFAC, CAM-I, AICPA, AAA…

• National Baseball Hall of Famer (oldest computer baseball game)

• Prolific book writer, blogger

http://www.garycokins.com/menu-bio

Gary Cokins, CPIM

Analytics-Based Performance Management LLC

Cary, North Carolina USA

www.garycokins.com

919.720.2718

gcokins@garycokins.com

Contact Gary

“40% of important decisions are not based on facts but rather on intuition, experience, and anecdotal evidence.”

Jeanne X. Harris, Accenture

Why Business Analytics?

Better decisions Better Actions

Purpose of Business Analytics

Goals of Analytics:

Gain Insight Solve Problems Make better and quicker decisions Take action

BI vs. Business Analytics

Business Intelligence Business Analytics

Consumes stored information Monitors the dials on a dashboard Answers existing questions

Produces new information Moves the dials on a dashboard Creates new questions Answers new complex, more relevant questions

Domains of Business Analytics

Retail: Markdown and assortment planning Marketing: CRM, segmentation, and churn analysis Financial services: Risk management, credit scoring Pharmaceutical: Drug development Text: Sentiment analytics Fraud: insurance and medical claims Manufacturing: Warranty claims Hospital: Patient scheduling Human Resources: Workforce planning Police: Crime pattern analytics … and more

Descriptive vs. Inferential Analytics

Reactive Standard Reports Ad Hoc Reports Query Drilldown (or OLAP) Alerts

Proactive Statistical Analysis Forecasting Predictive Modeling Optimization

Descriptive Inferential

Statistics is more confirmatory than exploratory. Great business analysts search for confirmation that two or more factors driving their data are related.

Case for Statistics

Forecasting vs. Predictive Modeling

Forecasts Predictive models

Tell you how many ice

scream cones will be sold

in July, so you can set

expectations for planned

costs, profits, supply

chain impacts and other

considerations

Tell you the

characteristics of ideal

ice scream customers,

the flavors they will

choose and coupon offers

that will entice them

Forecasting vs. Predictive Modeling

When to use:

Forecasts Predictive models

To help you do a better

job of buying raw

materials for the ice

scream, and to have them

at the factory at the right

time

If the marketing

department is trying to

figure out how, where,

and which most

attractive customers to

market the ice scream

Given the scarce resources of our marketing budget, which customer should we pursue?

A. Most profitable customer

B. Most valuable customer

The difference is Customer Lifetime Value

Customer Value Management

Which customer is more important for a pharmaceutical supplier?

Customer Lifetime Value

Dentist A

Sales = $ 750,000

Profits = $ 100,000

Age 61

Dentist B

Sales = $ 375,000

Profits = $ 40,000

Age 25

More profitable

More valuable

Focusing on the number of customers acquired results in a degraded mix as

low-value customers are easier to acquire

A customer-centric strategy will not acquire any customers; only high-value

ones

Customer Acquisition Strategy

Solution:

Determine which type of customer is attractive to acquire, retain grow, or

win back. Which customer types are not?

Create a spend budget for attracting, retaining, growing, or recovering each

customer segment

Optimizing Customer Value –

“Smart” Sales Growth

* You can destroy shareholder wealth

creation, (erode your profits) by:

* Over-spending unnecessarily on loyal

customers for what is needed to retain

them

* Under-spending on marginally loyal

customers and risk their defection to a

competitor

Role of Analytics

Analysts must overcome hunches and gut-feel guesses by others, and prove which actions yield the highest financial returns

The impact of reduction in uncertainty

Everything starts with sales!

The demand forecast of your product is the independent variable. (First domino)

All other measures are dependent variables. (Remaining dominos)

Forecasts are based on history. “Best methods selection” chooses a “best fit

forecasting method.”

As history changes, sometimes radically (new competitors), “best fit” method

becomes stale.

* Higher ROI from leveraging automation

* Deeper actionable insights and understanding

* Reducing uncertainty and managing risk

* More intelligent and tested decisions

* A bridge to culture of optimization

Benefits of Business Analytics

Competency with Business Analytics yields a lasting

and sustainable competitive advantage

* Fear of loss of power and decentralizing decision rights * Confirmation bias interpreting results to confirm preconceptions * Lack of analytical talent * Thinking small/”toll gate” approach * Lack of leadership and willpower

Risks from pursuing Business Analytics

You can do one thing wrong and fail..

You have to do many things correct to succeed!

Three types of concerns:

* Logical concern: Confusion versus understanding

* Your audience thinking, “I don’t get it”

* Emotional concern: Fear versus a favorable action

* Your audience thinking, “I don’t like it”

* Personal concern: Mistrust versus confidence

* Your audience thinking, “I don’t like you.”

“Beyond the Wall of Resistance”

By Rick Maurer

Technical barriers include IT-related issues

Perception barriers are excess complexity and affordability

Design deficiencies include poor measurements or their

calculations and weak models and assumptions

Organizational behavior barriers involve resistance to

change, culture, leadership

Barrier categories

“Moneyball” tells the story of how quantitative analysis can overcome perceptions of old school thinking. The Oakland As lowered their salary costs, but did not begin winning until they applied deep analytics.

@ExcelStrategies

http://www.ExcelStrategiesLLC.com http://Blog.ExcelStrategiesLLC.com http://Twitter.com/ExcelStrategies http://Google.com/+ExcelStrategiesLLC1 http://LinkedIn.com/in/ExcelStrategies http://YouTube.com/user/ExcelStrategies

Blog.ExcelStrategiesLLC.com

+ExcelStrategiesLLC1

ExcelStrategies

ExcelStrategies