Presentation for Slalom

1. Big data Psychology
Vishal Singh
NYU-Stern

2. History of Data Collection
Database
Marketers
Advent of Retail
Scanner
WWW
Mobile/GPS RFID
Astronomy/
Census

3. Psychological Insights from Mundane choices
 Psychological research confined to small experiments primarily
on students (98% of all research is with WEIRD subjects!)
 Thesis: Seemingly innocuous information such as aggregated
measures of internet search or mundane choices of grocery
products can reveal aspects of our deep-rooted ideologies,
values, and personality traits
Secondary objective: Automated and fully replicable empirical work flow

4. Automated Analytics Workflow
Dynamic Reproducible Documents
o Data download/munging part of
document
o Documents are dynamic: Models,
graphics, analysis, and write-up is updated
with flow of new data
o Documents are Interactive and 100%
Reproducible to other co-workers (and
future self)

5. Nature of Modern Data
The x- V’s of Data
o Volume
o Velocity
o Variety (cognitive challenge lies here)
o Integrating and Harmonizing data from a variety of sources and
formats (numeric, text, image, videos, social media)
Major progress
(AWS/Goggle cloud)
Era of Open Source: Machine Learning / AI Algorithms
Implication: Use of Analytics/Machine learning is simply a good
business practice (almost a necessity) rather than a differentiator.

6. Example: “Variety” in Data
Online Reviews: Empirical Generalizations
Joint work with Poppy Zhang (Phd Student, NYU) & Karsten Hansen (UCSD)

7. Scope of the Data
(Clean data files & R codes @ onreviews.org)
Amazon.com: Entire database, all products (1998-2014)
IMDB: All movies (1999-2015)
Vacation Rentals (All of Airbn’b & Homeaway)
Glassdoor (Employee ratings of firms)
YELP (Selected categories & geographies)
Expedia

8. Volume of Data

9. Analytics for what?
Primary focus is understanding and insights.
Tools: Visualization, Econometric models,
Interpretable machine learning
Primary focus is deployment. Eg.
Classification of Spam, Banner Ad
Targeting

10. Example:
What makes a review helpful?
Are there systematic Gender differences?
Approach 1: Classification exercise, take labeled data and run
CNN/RNN on Text and labels of Helpfulness. Get over 90% accuracy

11. Making Data Usable
Broad Categorization of Variables
 Review Attributes
 Star ratings
 Timing/sequence
 Helpfulness (judged by others)
 Language use
1. Review length
2. Valence (Positive vs Negative)
3. Readability ( words/sentence)

12. Extract Features of
Customers/Products
Reviewer Attributes
 Heavy vs. occasional reviewer
 Purchase information (sometimes)
 Geography (sometime)
 Gender (proxies)
Product Attributes
 Hedonic/experiential/durable
 Average Rating
 Within category (e.g. Action vs. Comedy),
 Sales Rank
 Popularity ( accumulated number of reviews)
 Price
Once the data is
harmonized, analytics
is simplified drastically.

13. Example: Quantifying Image
Clarifai

14. Helpfulness of Review
What makes a review helpful?
Which type of review is most helpful?
1-Star, 3-Star, 5-Star

15. Psychology Literature
“There is a general bias, based on both innate predispositions and
experience, in animals and humans, to give greater weight to
negative entities (e.g., events, objects, personal traits)” Rozin &
Royzman (2011)
• Negative assessments are perceived as more
diagnostic, particularly when the assessment is
well-reasoned and elaborated at some length

16. 5-Star Reviews Most Helpful

17. Price and Helpful% : Electronic Products

18. IMDB
 Review offers quick inference after movie release
 On average, each movie gets 147 reviews
 On average, each review gets 6 helpful votes out
of total 11 votes
Sample review for Avatar

19. Example 2:
Transaction Data

20. Context
 ACNielsen's Homescan Consumer Panel
 Detailed purchase histories (2004—2015)
 Use hand-held scanners to record every bar-coded item purchased
 Detailed demographic information
 Additional demographics supplemented using location
information (e.g., Religion, Conservative)

23. Store Level Data
(35K+ Stores, 2006—2015, All Categories)

24. Example 1:
Habitual Buying Behavior
(with Karsten Hansen)

25. Context
o Thought Experiment:
 Suppose you recorded your shopping history for every cereal,
toothpaste, detergent etc. for past 3 or 5 or 10 years
 What can we learn from this information?
 Questions to ask?
 Example: Habits vs. Variety seeking
 What would your product portfolio look like?
25

26. Positive=>Higher Concentration

27. Conservativeness (as measured by Voting & Religiosity) associated with:
 Preference for established brands
 Lower propensity to try new products
 Higher brand loyalty (repetitive buying)

28. Breaking Habits
Will a Fat Tax Work?
Small price differences when reflected in shelf
prices at the point of purchase, have significant
& long-term impact on food choices.

29. Previous Evidence
o Field Work
Econometric/data problems
Focus on Sales Tax
Industry Funded
Experimental Work 
Lab/Cafeteria/Vending Machines
Small non-representative samples

30. This Paper: Quasi Natural Experiment
$2.91 $2.91 $2.91 $2.90
$2.87
$2.73
$2.71
$2.60
$2.40
$2.45
$2.50
$2.55
$2.60
$2.65
$2.70
$2.75
$2.80
$2.85
$2.90
$2.95
Whole milk 2% milk 1% milk Skim milk
Uniform Price Non-Uniform Price
Depending on where you live and what supermarket chain you patronize, you see one of these patterns.
Milk Pricing in the US

31. Milk Pricing in the US
Vishal Singh, Stern School of Business, NYU 31
Non Flat Pricing
Primarily Non-Flat
Mixed
Primarily Flat
Flat Pricing
No Data Available
Southeast FMMO
Pennsylvania: Large milk
producer. State
regulations.
Uniform/Non-Uniform price
structure is consistent across
stores within a chain, even in
mixed states.
Upper Midwest FMMO: Wisconsin is
2nd largest producer
Central FMMO
Northeast FMMO
MidEast
FMMO
DATA
 1800 + supermarkets
 6 Years weekly data
 UPC level sales,
price, promotion etc.
 Counties represent
approximately 50% of
the population

32. a) Comparison of Demographic Profile between Flat and NonFlat Stores
Flat stores Non-Flat stores
Mean
Std
Dev Mean
Std
Dev p-value
Low income 18% 38% 21% 41% 0.08
High income 19% 39% 20% 40% 0.60
% Poverty 2% 1% 2% 1% 0.22
% Children 4% 1% 4% 1% 0.62
% College 39% 49% 41% 49% 0.58
% White 78% 19% 77% 19% 0.49
% Elderly 12% 4% 12% 5% 0.32
Population density 0.12 0.31 0.13 0.18 0.52
(b) (1) Regression of (Price Whole/ Price 2%) milk and (2) Variance Decomposition
(1) (2)
Estimate Std Error
% of explained variation
accounted for by:
Intercept 1.0393 (0.006)
Median Income -0.0017 (0.002) 0.06%
% HH Kids -0.0003 (0.001) 0.00%
% College -0.0005 (0.002) 0.01%
% White -0.0014 (0.001) 0.09%
Population Density -0.0003 (0.001) 0.00%
Wage 0.0028 (0.002) 0.14%
All retailers within 5 miles -0.0002 (0.001) 0.00%
Discount retailers within 10 miles -0.0021 (0.001) 0.18%
Marketing Order Fixed Effects Included 15.44%
Chain Fixed Effects Included 84.07%
R square 0.658
Is the Pricing Structure Exogenous?

33. Does it Change Behavior?

34. Large Response to Small Price Changes

36. 3. Automation/Deployment

37. Automating Scientific Reporting

39. Example
Workflow for the Consumer Package Industry

40. American Politics

41. Final Thoughts
 Trends
o Data proliferation & Rapid advancement in scalable algorithms
o Era of open source: Standardization of analytical methods & algorithms
o Provided as a Service by Cloud Hosting providers
 Key:
 Intuition & Critical Thinking at every stage rather than a
“Ctrl-C Ctrl-V” approach
My Work: Streamlining this Analytical workflow with Dynamic
Reproducible Documents
Data Intelligence Analytics Deployment