© Absolutdata 2014 Proprietary and Confidential
Chicago New York London Dubai New Delhi Bangalore SingaporeSan Francisco
w...
© Absolutdata 2014 Proprietary and Confidential 2
Mission
Vision
We are Decision Engineers and apply decision sciences
eve...
© Absolutdata 2014 Proprietary and Confidential 3
Recommendation Systems
Constrained Optimization
Linear Programming
Machi...
Are You Optimizing?
© Absolutdata 2014 Proprietary and Confidential 5
Do the work instead of liaising and thought partnering
Questions of rela...
© Absolutdata 2014 Proprietary and Confidential 6
How do you differentiate?
How do you Win?
© Absolutdata 2014 Proprietary and Confidential 7
Problem: An Insight is not an Insight, is not an Insight
RESEARCH
MUST
M...
© Absolutdata 2014 Proprietary and Confidential 8
What do
they need
to do?
© Absolutdata 2014 Proprietary and Confidential 9
Virtually all research falls
short
© Absolutdata 2014 Proprietary and Confidential 10
Recommend!
(make a stand, be an expert, be
specific)
Dig!
(identify spe...
© Absolutdata 2014 Proprietary and Confidential 11
Ensures Recommendation
Empowers Recommendation
Optimization
Make you a ...
© Absolutdata 2014 Proprietary and Confidential 12
Bringing a System to its Peak Performance
Need constraints Need a goal ...
© Absolutdata 2014 Proprietary and Confidential 13
DATA
SIMULATOR
ENGINE
Potential
constraints
Building a model to
find th...
© Absolutdata 2014 Proprietary and Confidential 14
Conjoint
Product and Product Line
Management
Segmentation
For communica...
© Absolutdata 2014 Proprietary and Confidential 15
How Optimization Ensures Differentiating
Quality of Work
Segment
priori...
© Absolutdata 2014 Proprietary and Confidential 16
Conjoint
ProscriptiveDescriptive
2%
4%
8%
13%
18%
24%
Margin Impact
1%
...
© Absolutdata 2014 Proprietary and Confidential 17
Brand positioning
ProscriptiveDescriptive
Quick dry
Fresh all day
Dry a...
© Absolutdata 2014 Proprietary and Confidential 18
Segment prioritization table
ProscriptiveDescriptive
Highest
value in
e...
© Absolutdata 2014 Proprietary and Confidential 19
Finding the first three
product changes to make
Knowing who to target f...
© Absolutdata 2014 Proprietary and Confidential 20
Identify critical decision criteria that can be
the basis of a maximiza...
© Absolutdata 2014 Proprietary and Confidential 21
Advanced Optimization Applications
Segmentation
Targeting optimization
...
© Absolutdata 2014 Proprietary and Confidential 22
If you need help with Analytics or Research, please write to us:
americ...
© Absolutdata 2014 Proprietary and Confidential 23
What does your research
look like?
Name
Designation
Phone:
Email:
Follow us on:
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Optimization as a Golden Layer - Chris Diener, SVP Analytics, Absolutdata

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Chris Diener, SVP - Analytics, AbsolutData delivered a session in MRA insights and strategies conference, 2013, on the topic ‘Optimization as a golden layer’, where he discussed optimization and constrained optimization and then showed how it can be applied effectively across a number of common and emerging MR technologies.

AbsolutData is a global leader in applying analytics to drive sales and increase profits for its customers. AbsolutData has built strong expertise and traction with Fortune 1000 companies across 40 countries. We specialize in big data, high end business analytics, predictive modeling, research, reporting, social media analytics and data management services. AbsolutData delivers world class analytics solutions by combining their expertise in industry domains, analytical techniques and sophisticated tools.

Visit us here : www.absolutdata.com

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Optimization as a Golden Layer - Chris Diener, SVP Analytics, Absolutdata

  1. 1. © Absolutdata 2014 Proprietary and Confidential Chicago New York London Dubai New Delhi Bangalore SingaporeSan Francisco www.absolutdata.com April 30, 2014 Optimization as a golden “layer” Presenter: Chris Diener, SVP – Analytics, Absolutdata Venue: MRA Insights and Strategies Conference June 10-12, 2013
  2. 2. © Absolutdata 2014 Proprietary and Confidential 2 Mission Vision We are Decision Engineers and apply decision sciences every day to improve decisions at the world’s largest companies To empower forward-looking organizations to reach new heights of business performance through the optimal use of data
  3. 3. © Absolutdata 2014 Proprietary and Confidential 3 Recommendation Systems Constrained Optimization Linear Programming Machine Learning Genetic Algorithms These are the tools of the 21st Century Ever Heard of This?
  4. 4. Are You Optimizing?
  5. 5. © Absolutdata 2014 Proprietary and Confidential 5 Do the work instead of liaising and thought partnering Questions of relative contribution compared to “Analytics” folks using non-survey data Order takers instead of consultants Client perspective Self-help explosion Budget squeeze between economy and “Analytics/Social” spend Commoditized: lack of true differentiation of services Vendor perspective MR Industry Conditions – Tough!
  6. 6. © Absolutdata 2014 Proprietary and Confidential 6 How do you differentiate? How do you Win?
  7. 7. © Absolutdata 2014 Proprietary and Confidential 7 Problem: An Insight is not an Insight, is not an Insight RESEARCH MUST MUST NOT Impact Decision Making remove specific pain or solve a specific problem Describe just address issues or answer questions
  8. 8. © Absolutdata 2014 Proprietary and Confidential 8 What do they need to do?
  9. 9. © Absolutdata 2014 Proprietary and Confidential 9 Virtually all research falls short
  10. 10. © Absolutdata 2014 Proprietary and Confidential 10 Recommend! (make a stand, be an expert, be specific) Dig! (identify specific decisions and actions) How do you do this? Support! (with a story)
  11. 11. © Absolutdata 2014 Proprietary and Confidential 11 Ensures Recommendation Empowers Recommendation Optimization Make you a more valuable resource Promotes Specificity
  12. 12. © Absolutdata 2014 Proprietary and Confidential 12 Bringing a System to its Peak Performance Need constraints Need a goal Need a system – or a Model Searching and recommending Need levers to pull to get to that goal
  13. 13. © Absolutdata 2014 Proprietary and Confidential 13 DATA SIMULATOR ENGINE Potential constraints Building a model to find the predictive relationships between levers and outcomes Identification of critical outcomes and management levers RECOMMENDATIONS
  14. 14. © Absolutdata 2014 Proprietary and Confidential 14 Conjoint Product and Product Line Management Segmentation For communications or product development Targeting Database or Demographic Classification Positioning Brand Image Management Examples of Optimization
  15. 15. © Absolutdata 2014 Proprietary and Confidential 15 How Optimization Ensures Differentiating Quality of Work Segment prioritization Brand positioning Conjoint
  16. 16. © Absolutdata 2014 Proprietary and Confidential 16 Conjoint ProscriptiveDescriptive 2% 4% 8% 13% 18% 24% Margin Impact 1% 2% 3% 4% 6% 9% Sensitivity= -1.61
  17. 17. © Absolutdata 2014 Proprietary and Confidential 17 Brand positioning ProscriptiveDescriptive Quick dry Fresh all day Dry all day Skin Comfortable Refreshing Suits Sensitive Skin Confidence Giving No Stickiness Effective against Odour Low Cost Quick dry Fresh all day Dry all day Comfortable on skin Refreshing Suits Sensitive Skin Confidence Giving No Stickiness Low Cost
  18. 18. © Absolutdata 2014 Proprietary and Confidential 18 Segment prioritization table ProscriptiveDescriptive Highest value in each row SEGMENT SIZE 1059 327 96 166 154 278 38 Lowest value in each row 31% 9% 16% 15% 26% 4% Importance of factors regarding electric energy provide - NEEDS BASED (Mean Scores) Low price 27.7516.1023.66 9.57 16.6461.7713.92 Predictable costs 9.87 8.37 9.61 7.18 7.93 7.63 59.34 Not locked into price 4.26 4.98 1.79 5.77 6.27 2.53 2.32 Customizable contracts 4.61 7.28 2.33 6.14 3.81 2.13 2.24 Detailed usage information 5.83 8.18 4.24 7.51 5.32 3.32 2.61 Products to reduce your energy use 5.60 3.42 3.25 6.06 18.36 2.10 2.11 Ongoing energy industry information 2.94 1.10 1.25 13.28 1.44 0.61 0.97 Selection assistance 5.24 8.54 4.01 6.58 4.69 1.79 1.68 Outstanding sales representative 5.11 8.76 2.66 6.55 3.62 2.13 1.45 Outstanding customer service after contract signed 8.01 10.42 7.42 7.61 7.27 6.50 4.68 Simple purchase process 7.69 7.10 29.40 5.69 5.34 4.09 2.58 Financially strong 6.22 9.92 3.10 7.43 4.58 3.44 4.08 Public utility 2.93 2.95 4.23 5.93 3.05 0.81 1.39 Energy from clean/renewable sources 3.93 2.89 3.05 4.70 11.69 1.17 0.63
  19. 19. © Absolutdata 2014 Proprietary and Confidential 19 Finding the first three product changes to make Knowing who to target first and with what message Knowing how much to invest in raising awareness of which specific brand qualities Finding the best product to make Key is identifying a Goal that is Actionable
  20. 20. © Absolutdata 2014 Proprietary and Confidential 20 Identify critical decision criteria that can be the basis of a maximization goal Define the relationships between levers and the goal Recommend the best lever combination(s) Frame your research in terms of specific decisions How to do it Using the model, search for lever combinations that get the best goal outcome
  21. 21. © Absolutdata 2014 Proprietary and Confidential 21 Advanced Optimization Applications Segmentation Targeting optimization Brand purchase driver modeling Marketing effectiveness and attribution modeling Path to purchase modeling CRM lifetime value calculations Promotion optimization in direct marketing So/Lo/Mo models for better targeting Optimizing product, people and message in the same system Inclusion of social media or other big data with survey data for better application and insight
  22. 22. © Absolutdata 2014 Proprietary and Confidential 22 If you need help with Analytics or Research, please write to us: americas.sales@absolutdata.com europe.sales@absolutdata.com asia.sales@absolutdata.com For Media related queries -media.relations@absolutdata.com For all other queries -info@absolutdata.com HEAD OFFICE 314 Marble Arch Tower, 55 Bryanston Street, London W1H 7AA Phone: + 44 207 868 2240 UK OFFICE DLF Cyber City SEZ, Building#14, 4th Floor, Tower B, DLF Phase-III, Sector 24 & 25A, Gurgaon-122002, Phone: +91.124.4953.400 INDIA OFFICE Absolutdata Analytics Middle East JLT Office 1604, Tower BB1 Mazaya Business Avenue Jumeirah Lake Towers Phone: +97150-1577257 DUBAI OFFICE 1851 Harbor Bay Parkway, Suite 125, Alameda, California, USA – 94502 Phone: +1 510 748 9922 Fax: +1 510 217 2387
  23. 23. © Absolutdata 2014 Proprietary and Confidential 23 What does your research look like?
  24. 24. Name Designation Phone: Email: Follow us on:

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