This document discusses how combining big data with human experience can provide smarter decision making. It notes that while big data is good at pattern recognition, it is limited by what data is available. Human experience can add logical reasoning to smooth out biases. Advances now allow assigning numerical values to human experience to integrate it with data, providing more precise predictive decision support. This approach overcomes common modeling issues and allows for transparency, collaboration, and immediacy. The models are constantly optimized to balance data and knowledge and produce learning models updated with new inputs. This goes beyond traditional modeling to account for past decisions, subjective experience, and market changes.

1. SXSW 2013: Big Data & Human Experience
Think love match not shotgun wedding
Submitted for SXSW 2013
Dr. Lauren Tucker
Director of Consumer Forensics
The Martin Agency
Chris Dickey
Director of Analytics
The Martin Agency
1

2. Core Business Challenge:
Making smart decisions in an uncertain world
Information explosion
Great Opportunities
Economic implosion
Great Risks
Market globalization
2

3. Big Data, especially post recession, can have gaps that limit it’s
predictive power.
Big Data
Strength Pattern recognition
Weakness
Limited by
data

4. Human Experience can be an important additive to Big Data, which
can smooth out the biases of human instinct.
Statistical Analysis Human Experience
Strength Pattern recognition Logical reasoning
Weakness
Limited by Limited by
data instinctual biases

5. Advances in technology and mathematics allow for a numerical value
to be assigned to human experience so it can be integrated with data
to deliver smarter decisions
Better Choices from
Hard Data + Human Experience = Scenario Simulation
Historical Data Real world expertise Range of Predictive
Outcomes
Alone: Alone: Together:
Past isn’t always Experience isn’t Precise, predictive
predictive always precise decision support

6. This approach overcomes common modeling issues, allows for
transparency, collaboration and immediacy.
Data
vs.
Lots
of
Data Lots
of
Knowledge
Knowledge (Tight
fit,
Lots
of
data) (Lots
of
experience,
strong
basis)
Li@le
Knowledge
(Li@le
experience,
weak
basis)
Li@le
Data
(Erroneous
fit,
li@le
data)
6

7. Models that integrate data and knowledge are constantly
optimized to achieve a balance of both.
Illustra(ve
75th
percenGle • Experience
25th
percenGle
• MarkeGng
research
• Database
a@ribuGon
• Sales
over
Gme
• MarkeGng
acGvity
over
Gme
• CorrelaGons
between
data
sales
and
markeGng
acGvity
25% 75%
7

8. This approach produces a learning model that is continually updated
with new inputs.
Historical data: Investment guidance
Sales demand Optimal investment according to forecast
targets, fixed budget constraints and
Sales force activity profit maximization
Marketing activity Analysis Optimized tactical allocation across
Financials sales and marketing channels
Simulation
Optimization
Management information: Scenario results
Sales and marketing budget
Past marketing allocation decisions
Revenue forecasts short & long term
Risk assessments

9. The advantage for marketers over traditional
modeling approaches used for decision support.
Table Stakes …And Beyond
Scenario Planning: Account for Impact of Past Decisions:
The development of optimal media Assigns values to subjective experience,
plans with projected outcomes events and changes in market.
Budget Planning: Adaptive Testing:
Optimizes investments to Employs an iterative process and
business targets constantly optimizes by updating priors
to improve models.
Basic Analysis: Strategic Investment Planning:
ROI for all communications channels and Long and short term ROI
for each individual channel

10. For more information:
Dr. Lauren Tucker
Director of Consumer Forensics
The Martin Agency
Lauren.tucker@martinagency.com
Chris Dickey
Director of Analytics
The Martin Agency
Chris.Dickey@martinagency.com