Business Analytics - Highlights Gary Cokins, CPIM Illinois CPA Society Seminar October 21, 2014 Slideshare by:
Jul 07, 2015
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
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.
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