WHY BA is a Business Analytics tools and application for making decision so that insight can be with drawn.pptx

business analytics

1. Why Business Analytics?

“40% of important decisions are not based on facts but

rather on intuition, experience, and anecdotal
evidence.”

Jeamne X. Harris Accenture

2. Purpose of Business Analytics

Better decisions
Better Actions

3. Goals of Analytics:

Gain Insight

Solve Problems

Make better and quicker decisions
Take action

4. Business Intelligence

Consumes stored
information

Monitors the dials on a
dashboard
Answers existing questions

Business Analytics

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

5. 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

6. Descriptive vs. Inferential Analytics

Descriptive

Inferential

Reactive

Standard Reports

Ad Hoc Reports

Query Drilldown (or OLAP)
Alerts

Proactive
Statistical Analysis
Forecasting
Predictive Modeling
Optimization

7. Case for Statistics

Statistics is more confirmatory than exploratory.

Great business analysts search for confirmation that
two or more factors driving their data are related.

8. Forecasting vs. Predictive Modeling

Forecasts

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

Predictive models

Tell you the
characteristics of ideal
ice scream customers,
the flavors they will
choose and coupon offers
that will entice them

9. Forecasting vs. Predictive Modeling

When to use:

Forecasts

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

Predictive models

If the marketing
department is trying to
figure out how, where,
and which most
attractive customers to
market the ice scream

10. Customer Value Management

A. Most profitable customer

11. Customer Lifetime Value

Which customer is more important for a pharmaceutical
supplier?

Dentist A Dentist B
Sales = $ 750,000 Sales = $ 375,000
Profits = $ 100,000 Profits = $ 40,000
Age 61 Age 25

More profitable More valuable

12. Customer Acquisition Strategy

Focusing on the number of customers acquired results in a degraded mix as
low-value customers are easier to acquire

Acustomer-centric strategy will not acquire any customers: only high-value
ones

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

13. Optimizing Customer Value -
“Smart” Sales Growth

* You can destroy shareholder wealth
creation, (erode your profits) by:

si

Angel Customers

& * Over-spending unnecessarily on loyal
Demon Customers customers for what is needed to retain
them
BAER nich le which and * Under-spending on marginally loyal

TURBO-CHARGE YOUR STOCK à x >
customers and risk their defection to a

competitor

14. Role of Analytics

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

15. The impact of reduction in uncertainty

Forecasting error

Copyright 2014 www garycokins com Analylics-Based Perlormance Management LLG

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.

16. Analytics: Probabilistic Planning Scenarios

Which budget report would you prefer?

(measuring sales, expenses, profit, etc.)

probability
worst base best br rs -
$.5M $10M
#1 / single point #2 | three points #3 / multiple probabilistic

Copyright 2014 www.garycokins.com Analytics-Based Perlormance Management LLG

17. Two BA Views: Hindsight and Foresight

Time =0

Historical, Descriptive Future
(trends, insights, inferences) (proactive)

Past EA
(reactive) Predictive

(uncertainty, risk mgmt.)

What happened? Where? And

why is this happening? What will happen next? What is

the best that can happen?

Copyright 2014 wanwaarveokins com Analytics-Based Performance Management LLG

18. The Intelligence Hierarchy

Power of
Information
$ROI
Standard OLAP

Raw Reports

9 ©
Two types of Data —— Information Knowledge —— Intelligence
software are

, Transactional systems (e.g.. ERP) Business Analytics and Performance Management

like a brain's “the reptilian brain stem” “the cerebral cortex”
two halves. (breathing, blinking, digesting) {thinking and decision making)

Copyright 2014 wawgarvcokins com Analytics Based Performance Management LLG

19. * 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

20. Risks from pursuing Business Analytics

* 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

You can do one thing wrong and fail..
You have to do many things correct to succeed!

21. “Beyond the Wall of Resistance”
By Rick Maurer

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.”

22. Barrier categories

‘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

23. “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.