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

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. © 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. © 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. Are You Optimizing?

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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!

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How do you differentiate?
How do you Win?

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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

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What do
they need
to do?

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Virtually all research falls
short

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Recommend!
(make a stand, be an expert, be
specific)
Dig!
(identify specific decisions and
actions)
How do you do this?
Support!
(with a story)

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Ensures Recommendation
Empowers Recommendation
Optimization
Make you a more valuable resource
Promotes Specificity

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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

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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

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Conjoint
Product and Product Line
Management
Segmentation
For communications or
product development
Targeting
Database or Demographic
Classification
Positioning
Brand Image Management
Examples of Optimization

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How Optimization Ensures Differentiating
Quality of Work
Segment
prioritization
Brand
positioning
Conjoint

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Conjoint
ProscriptiveDescriptive
2%
4%
8%
13%
18%
24%
Margin Impact
1%
2%
3%
4%
6%
9%
Sensitivity= -1.61

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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

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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

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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

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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. © 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

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