Big Data + Big Analytics = Big Wins September 19, 2012 Marco
Vriens The Modellers
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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) My Background
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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
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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.
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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. Definition of Insights
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New & Big Data
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Source: Google.com/Insights/Search Data: Search frequencies for
search terms (e.g. Red Wine, Flu) Source: Blogpulse.com 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 Specific New Data
Sources
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Data Strategy Data inventory Data readiness Integrated designs
Data Strategy
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Insights Discovery Process Big-Data Should Start with a
Business Problem *
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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 Potential Value Areas Question: Do Big-Data studies
help the firm make better decisions?
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Big-Data, Different Decisions? KEY VALUE AREASBETTER DECISION?
Time series on 40+ search terms on Wine varieties identified key
trends Doubtful Microsoft accurately identified in 2003 the size of
the Linux threat 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 Doves 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 Dont Know, but likely More opportunities
for validation help prioritize insights Yes
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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
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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 6-
dimensional 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)
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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
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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 Vector-
Auto 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
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Indirect Effects Improve Marketing Mix Models Better Estimates
+ Better Understanding Total effect = C + (A*B)
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.
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Offline sales and online search dependent variables and
indicators of latent demand Modeling approach: Dynamic Linear
Modeling Scenario 2 Example 2 Big-Data Insights Into Car
Advertising Latent Demand for Car Brand Offline Sales TV Radio
Print Online Search Adding online search significantly adds to the
forecasting ability
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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.
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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
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Internal Engagement Data Benchmarking + Recommendations Survey
Data Engagement ROI External data Correlation of Engagement with
Overall Satisfaction Audience Population Numbers Reach Penetration
Cost per Touch YTD Spent Data Reach Data (Monthly) Integrating
Diverse Data Streams
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Scenario 2 Example 3 Interactive Decision Support Tools
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Big Analytics DEGREE OF ADVANCED OR BIG ANALYTICS
BasicIntermediateHighly Linear regressionLogit & MNL
RegressionSupport Vector Machines Asymmetric loss functions Factor
AnalysisStructural equation modeling Hierarchy of effects models
K-MeansLatent Class Models Time Series Regression Vector Auto
Regressive (VARX) Kalmam Filters/State Space models
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Big Analytics Can Yield Big Returns Case StudiesReturn on
Modeling John Deere Double digit profit increase ABB Electric $100
Million RhenaniaProfits increase 4x + Significant advantage of
competition BauMax 8% profit increase Inofec Double digit profit
increase Marriot $200 million initially, LT 1 Billion
Qantas/Jetstar4% market share growth, $45 million increase in
revenue
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Using Big Analytics Comes With a Challenge Why Managers Dont
Trust Predictive Models
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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
Using Big Analytics Comes With a Challenge Why Managers Dont Trust
Predictive Models
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How True? How Valid Are Big-Data Insights? Targets prediction
of pregnant teen Twitter-based prediction of flu Some Validation
Risks Correlation is not causation Misinterpretation Stability and
Validity Lucas Critique Managers do not trust predictive models
because they know that different datasets can give different
results different analysts or different modelers can get different
insights and they realize correlation is not causation
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Key Take-Aways Big-Data should be part of broader data
strategy. We should start with a business problem and assess
whether its 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.
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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 dont just arise naturally in Big-Data.
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/)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)www.allanalytics.com Avoiding Big Data
Disaster (www.allanalytics.com)www.allanalytics.com
www.theinsightsadvantage.com Nucleus Research Note: The Big returns
of Big Data
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