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Decision Modeling & Implementation
Support for Strategic Management
April 2017
By Cem ?engezer
K.I.S.S. METHODS
INTRODUCTION
CORE of Modeling: Analytics
% 30 work hour
(hindsight, experience)
data processing
% 70 work hour
(insight, foresight)
information /optimization
Business Analytics
POSITIONING
STRATEGY
BOARD
TACTICAL
BOARD
ANALYTICS (%70 work)
(predictive, prescriptive)
ANALYTICS (%30 work)
(descriptive, diagnostic)
Data ETL
(extract, transform, load)
Various metrics (raw data from different sources )
INFORMATION
DEMAND
INFORMATION
SUPPLY
New Edge to Decision Support
OLD
Reliance on
survey data
Focus on
outcomes of
consumer
decisions
OLD
Measuring
business
effects
Use
descriptive
models
OLD
Focus on
short-run
profitability
NEW
Insights from
marketplace
data
Examine the
process
consumers
use to make
decisions
NEW
Optimize
activities
Use models
based on
behavioral
process
NEW
Emphasize
drivers of
long term
value
Critical Problems
solved with modeling
? Calibration of proper response (corporate…) in / before turbulence
? When
? How
? Investment boosting
? When
? How
BUSINESS ANALOGY
ECONOMY IS CYCLICAL
A well known example about cause-effect :
fashion business cycle determined by stock market
Behavior
cycles are
socio
economic
driven
Business Model Canvas
VALUE PROPOSITION
THE APPROACH
Business is cyclical
Need of decision models to predict cycles
GOAL:
Try to create unique solutions:
“always” in profit cycle
HOW:
Non Linear Predictive Modeling
VALUE PROPOSITION
Right Time DECISION SUPPORT
?Know your risk/reward before acting (investment, improvement…)
?Right time, real time
->Not on your time but market time
?Investment automation
->helps you make decision at the right time with the right budget
Full Service Value Proposition
Pain relievers
(moderate benefits
will replace low
margins –below
avg.)
Gain boosters
(high benefits
will replace
moderate
margins - avg.)
Smarter
Strategic
decisions
MAJOR BUSINESS ACTIVITIES
SPORTS INDUSTRY:
?Solution provider for every partners in industry
?Freelance Consultant
?Data science methodologies supportting sports science
?Some links:
?www.sportsmedicineturkey.com/en/Hakkimizda/Danismanlarimiz
?http://www.futbolekonomi.com/index.php/in-english/crisis-in-football/3984-
nedensellik-analizi-ned-ensellik-analizi-afutbol.html
?www.slideshare.net/vizyoner/black-box-analytics
?www.slideshare.net/vizyoner/value-proposition-for-sports-industry
FRANCHISING INDUSTRY:
?Finding futur consuming locations
?Freelance Consultant
?Up sell with gamification methodologies
?Some links:
?www.bayimolurmusun.com.tr/haber/1995
?www.slideshare.net/vizyoner/black-box-analyticsfranch
FINANCE INDUSTRY:
?Algorithmic trading methodologies applied for different markets
?Freelance Consultant
?Trade coaching
?Some links:
?www.paraborsa.net/kategori/yazarlar/cem-sengezer/
CRISIS MANAGEMENT CASE STUDIES
Each problem that I solved became a rule, which served afterwards to
solve other problems (René Descartes)
cem@momentumdanismanlik.com
Cem ?engezer
www.cemsengezer.com

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General black box_analytics

  • 1. Decision Modeling & Implementation Support for Strategic Management April 2017 By Cem ?engezer
  • 4. CORE of Modeling: Analytics
  • 5. % 30 work hour (hindsight, experience) data processing % 70 work hour (insight, foresight) information /optimization Business Analytics
  • 6. POSITIONING STRATEGY BOARD TACTICAL BOARD ANALYTICS (%70 work) (predictive, prescriptive) ANALYTICS (%30 work) (descriptive, diagnostic) Data ETL (extract, transform, load) Various metrics (raw data from different sources ) INFORMATION DEMAND INFORMATION SUPPLY
  • 7. New Edge to Decision Support OLD Reliance on survey data Focus on outcomes of consumer decisions OLD Measuring business effects Use descriptive models OLD Focus on short-run profitability NEW Insights from marketplace data Examine the process consumers use to make decisions NEW Optimize activities Use models based on behavioral process NEW Emphasize drivers of long term value
  • 8. Critical Problems solved with modeling ? Calibration of proper response (corporate…) in / before turbulence ? When ? How ? Investment boosting ? When ? How
  • 11. A well known example about cause-effect : fashion business cycle determined by stock market
  • 14. THE APPROACH Business is cyclical Need of decision models to predict cycles GOAL: Try to create unique solutions: “always” in profit cycle HOW: Non Linear Predictive Modeling
  • 15. VALUE PROPOSITION Right Time DECISION SUPPORT ?Know your risk/reward before acting (investment, improvement…) ?Right time, real time ->Not on your time but market time ?Investment automation ->helps you make decision at the right time with the right budget
  • 16. Full Service Value Proposition Pain relievers (moderate benefits will replace low margins –below avg.) Gain boosters (high benefits will replace moderate margins - avg.) Smarter Strategic decisions
  • 18. SPORTS INDUSTRY: ?Solution provider for every partners in industry ?Freelance Consultant ?Data science methodologies supportting sports science ?Some links: ?www.sportsmedicineturkey.com/en/Hakkimizda/Danismanlarimiz ?http://www.futbolekonomi.com/index.php/in-english/crisis-in-football/3984- nedensellik-analizi-ned-ensellik-analizi-afutbol.html ?www.slideshare.net/vizyoner/black-box-analytics ?www.slideshare.net/vizyoner/value-proposition-for-sports-industry
  • 19. FRANCHISING INDUSTRY: ?Finding futur consuming locations ?Freelance Consultant ?Up sell with gamification methodologies ?Some links: ?www.bayimolurmusun.com.tr/haber/1995 ?www.slideshare.net/vizyoner/black-box-analyticsfranch
  • 20. FINANCE INDUSTRY: ?Algorithmic trading methodologies applied for different markets ?Freelance Consultant ?Trade coaching ?Some links: ?www.paraborsa.net/kategori/yazarlar/cem-sengezer/
  • 22. Each problem that I solved became a rule, which served afterwards to solve other problems (René Descartes) cem@momentumdanismanlik.com Cem ?engezer www.cemsengezer.com