Transcript Big Data + Big Analytics = Big Wins

Big Data + Big Analytics =
Big Wins
September 19, 2012
Marco Vriens
The Modellers
My Background
• SVP at the Modellers, Innovation
& Analytics
• Microsoft and GE
• Academics and marketing
research firms
• Editor of The Handbook of
Marketing Research (Sage, 2006)
• Author of The Insights
Advantage: Knowing How to Win
(2012)
Discussion Points
1. The Big-Data Challenge
2. New and Big Data
3. Smart Use of Big-Data
4. Potential Value Areas
5. Some Examples
6. How “True” and Valuable Are Big-Data Insights
7. Key Take-Aways
The Big-Data Challenge
“Everywhere you look, the quantity of
information in the world is soaring. Merely
keeping up with this flood, and storing the
bits that might be useful, is difficult
enough. Analyzing it, to spot patterns and
extract useful information, is harder still.”
The Economist; “The Data Deluge”; 2/10/2010
Gartner says:
• “The Big Data Challenge Involves More
Than Just Managing Volumes of Data”
• “The real issue is making sense out of
the data and (…) helping organizations
make better decisions.”
Definition of Insights
• “Insights are thoughts, facts, data, or analysis of facts and
data that induce meaning and further understanding of a
business challenge and answer essential questions and
create an urgency to act or rethink a business challenge
in terms of its problems or solutions.”
New & Big Data
New & Big Data
Specific New Data Sources
• Source: Google.com/Insights/Search
• Data: Search frequencies for search terms (e.g. Red Wine, Flu)
• Source: Blogpulse.com
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Data: Blog content mapped to # of mentions, positive, negative
Worldwide no. of blogs estimated btw. 120-184M, 120,000 added every day
50% of US Internet population reads at least 1 blog
Blogs can be a good predictor of market outcomes for new products
• Source: Tweets scraped from Twitter and twitter.com/trendistic
• Data: Time series of key phrases (Tweets cleaned for Key phrases) or Time series of Mood
measures (Tweets mapped to mood states)
• Source: twitter.com/trendistic.com
• Text scraped from across the Internet
• Data: conversations
• Source: proprietary firms
Data Strategy
Data Strategy
• Data inventory
• Data readiness
• Integrated designs
Insights Discovery Process
Big-Data Should Start with a Business Problem
*
Potential Value Areas
New & Big Data Cases
1. Quantitative Trend Spotting
2. Early Warning Systems
3. Market Forecasting
4. New Product Idea Generation
5. Crisis Identification
6. Marketing Mix Modeling
7. Better Prioritization
Question: Do Big-Data studies help
the firm make
better decisions?
Big-Data, Different Decisions?
KEY VALUE AREAS
Time series on 40+ search terms on Wine varieties
identified key trends
Microsoft accurately identified in 2003 the size of the Linux
threat
BETTER DECISION?
Doubtful
Yes, $ 150 Million Ad
campaign impacted
Tweets mapped to mood states improves prediction of the
DJIA
No
Intuit uses Twitter comments to identify product
improvements
Yes
Unilever quickly assessed brand risk after Dove’s Anti-Age
Ad was pulled
Yes
Various cases: Improved the predictive power of marketing
mix models because Big-Data facilitates the inclusion of
indirect effects and latent demand
Don’t Know, but likely
More opportunities for validation help prioritize insights
Yes
Two Scenarios
Grab Big Data and…
SCENARIO 1:
Do a stand-alone analysis to get
insights
Example: Twitter Mood improves
predicting the Dow Jones
Industrial Average over and
above its own past.
SCENARIO 2:
Mix the Big-Data with with more
traditional marketing research data
to get more and better insights
Use Social Media data and mix it
with traditional marketing mix
data to show value of social
media marketing in context of all
other media options
Scenario 1 Example
Twitter Mood Predicts DJIA
• Hypothesis: Emotions can drive behavior in addition to new behavior
• Doing surveys at this scale to measure public mood would be very
expensive and time consuming
• Twitter-feeds-based sentiment tracking tools have been developed
• Two tools were used:
1. Opinion Finder (software tool): provides a positive/negative daily
time series of public mood
2. Google Profile of Mood States (GPOMS): composes a 6dimensional daily time series: Calm, Alert, Sure, Vital, Kind, Happy
• To make original psychometric instrument applicable to Twitter data the
researchers expanded the original 72 items in the POMS instrument to a
lexicon of 964 associated terms
• This allowed mapping to the 6 mood states
(for details: www.terramood.informatics.Indiana.edu/data)
Scenario 1 Example
Twitter Mood Predicts DJIA
• Data:
• 10 million tweets by 2.7 Million users
• Yahoo Finance closing values of DJIA
• Opinion Finder & GPOMS were cross-validated against significant events
such as Election day, etc.
• Analysis:
• Granger Causality Tests: Indicated that Calm Granger caused DJIA
• Self-Organizing Fuzzy Neural Networks:
• Base model: DJIA predicted based on its own history:
73% direction accuracy
• Adding Calm dimension: 86.7% direction accuracy
Scenario 2 – Example 1
Big Data Shows Value of Social Media
• Firm-facilitated social media interaction can help explain variations in mind set metrics
such as awareness, consideration, and preference
• Firm-facilitated social media interactions explain an additional 25% of the
variance in sales over and above the amount that is explained by media expenditures
• This work was feasible through the combination of Big-Data + Advanced VectorAuto Regression.
Source: de Vries et al. (2012), VAR Model for Effects of Social Media Interactions on Firm Outcomes, presented at the Marketing Dynamics Conference, 2012
Indirect Effects Improve Marketing Mix Models
Better Estimates + Better Understanding
Total effect = C + (A*B)
Indirect Effects Improve Marketing Mix Models
Better Estimates + Better Understanding
Total effect = C + (A*B)
Indirect Effect of Adwords
Leads
Quotes
Orders
Profit
Online
Online
Online
27
%
Offline
Offline
Offline
73
%
Adwords
Online
Visits
Scenario 2 – Example 2
Big-Data Insights Into Car Advertising
• One published case study for a car manufacturer showed how
online product information and quote request significantly added
to the insights of a marketing mix model
• Questions:
• Does advertising lead to increased search?
• Does advertising lead to accelerated sales?
• Does advertising create new demand?
• Data:
• Advertising expenditures
• Online search (117 online sources were used) – “hand-raising” data
• Unit sales data
Based on Dotson, et al (2012), Identifying the Dynamic Role of Advertising Through Online Search and Online Sales, Working Paper.
Scenario 2 – Example 2
Big-Data Insights Into Car Advertising
• Offline sales and online search dependent variables
and indicators of latent demand
• Modeling approach: Dynamic Linear Modeling
TV
Offline Sales
Radio
Latent Demand for
Car Brand
Online Search
Print
Adding online
search significantly
adds to the
forecasting ability
Scenario 2 – Example 2
Big-Data Insights Into Car Advertising
• Modeling approach + Big-Data allows for better forecast quality
• Results show that TV advertising drives demand creation, not just
purchase acceleration
• Approach allows “nowcasting”:
• This is a great benefit – because in the car market online search data can
be observed in real time, but reporting of actual sales often lags
• This feature helps managers intervene much earlier if sales are expected
to go down and gives them a vital advantage over their competitors
Based on Dotson, et al (2012), Identifying the Dynamic Role of Advertising Through Online Search and Online Sales, Working Paper.
Scenario 2 – Example 3
Data Fusion & Interactive Decision Support Tools
• Survey data – indicating what engagement types customers used for
engaging with firm; correlate with overall satisfaction
• CRM data on what engagements customers actually used
• Monthly reach data by country (35+) by engagement (15+) –
resulting in huge cumbersome Excel spreadsheets
• YTD Spend on engagements
• Several survey studies + macro economic data that allowed us to
estimate by country the size population
Integrating Diverse Data Streams
Survey Data
Correlation of
Engagement with
Overall Satisfaction
Internal
Engagement
Data
Reach Data
(Monthly)
External data
Audience Population
Numbers
YTD Spent
Data
Reach Penetration
Cost per Touch
Engagement
ROI
Benchmarking +
Recommendations
Scenario 2 – Example 3
Interactive Decision Support Tools
Big Analytics
DEGREE OF ADVANCED OR BIG ANALYTICS
‘Basic’
Intermediate
Highly
Linear regression
Logit & MNL Regression
Support Vector Machines
Asymmetric loss functions
Factor Analysis
Structural equation
modeling
Hierarchy of effects models
K-Means
Latent Class Models
Time Series
Regression
Vector Auto Regressive
(VARX)
Kalmam Filters/State Space
models
Big Analytics Can Yield Big Returns
Case Studies
Return on Modeling
John Deere
Double digit profit increase
ABB Electric
$100 Million
Rhenania
BauMax
Profits increase 4x + Significant advantage of
competition
8% profit increase
Inofec
Double digit profit increase
Marriot
$200 million initially, LT 1 Billion
Qantas/Jetstar
4% market share growth, $45 million increase in
revenue
Using Big Analytics Comes
With a Challenge
Why Managers Don’t Trust Predictive Models
Using Big Analytics Comes
With a Challenge
Why Managers Don’t Trust Predictive Models
Analytics Trend Troubles Scientists
In 2010, two research teams separately analyzed
data from the same UK patient database to see if
widely prescribed osteoporosis drugs increased the
risk of esophageal cancer. They came to
surprisingly different conclusions…
How True?
How Valid Are Big-Data Insights?
• Target’s prediction of pregnant teen
• Twitter-based prediction of flu
Managers do not trust predictive models
because they know that different datasets can
Some Validation Risks
Correlation is not causation
give different results – different analysts or
different modelers can get different insights –
and they realize correlation is not causation
Misinterpretation
Stability and Validity
Lucas Critique
Key Take-Aways
• Big-Data should be part of broader data strategy.
• We should start with a business problem and assess
whether it’s a logical target for Big-Data.
• Using a mix of “old,” “new,” and “big” data is a more
powerful approach than using Big-Data alone.
• To leverage the potential in Big-Data, we need
sophisticated Advanced Analytics – e.g., VAR/VEC
models, DLMs, Fuzzy Neural Networks, Data Squashing etc.
Key Take-Aways
• Big-Data is not the solution to everything. Right now
the killer apps seem to be marketing mix modeling and
customer sentiment and satisfaction.
• Validation of Big-Data insights is key, and…
• Last but not least, Big-Data will not replace survey
research. To get a true foundational understanding of
why people behave the way they do we will often need
to ask them specific questions that don’t just “arise”
naturally in Big-Data.
Thank you.
Marco Vriens, Ph.D.
SVP Methodology
(801) 290-3838
[email protected]
References

Vriens, M. (2012), The Insights Advantage: Knowing how to win, i-Universe.

Grover, R., and M. Vriens (2006), Handbook of Marketing Research: Uses, Misuses, and
Future Advances. Thousand Oaks: Sage Publishing.

de Vries, L. et al. (2012), VAR Model for Effects of Social Media Interactions on Firm
Outcomes, presented at the Marketing Dynamics Conference, 2012

Dotson, et al (2012), Identifying the Dynamic Role of Advertising Through Online Search and
Online Sales, Working Paper

Bollen, Mao, and Zeng (2011), Twitter Mood Predicts the Stock Market, J of Computational
Science, 2, 1-8.

My blogs…
•
10 Steps For Stretching Marketing Research For More And Better Insights
(http://www.greenbookblog.org/2012/05/29/10-steps-for-stretching-marketing-research-
for-more-and-better-insights/)
•
•
3 Reasons Why Big Data is Relevant (www.allanalytics.com)
•
Avoiding Big Data Disaster (www.allanalytics.com)
•
www.theinsightsadvantage.com
Nucleus Research Note: The Big returns of Big Data