using data science & predictive models to produce foresightforesight: the case of the...
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Using Data Science & Predictive Models to Produce Foresight:The case of the presumptuous assumptions
Steve CohenPartnerin4mation insightsNeedham, MA
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Big Data Analytics Consumer & Market Segmentation Customer Lifetime Value & Churn Digital Attribution Market Structure Marketing Mix Modeling New Product & Service Design Pricing & Promotion Optimization Assortment Optimization Retail Site Location Marketing Ecosystem Models
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Proprietary Hardware & Software
Hierarchical Bayesian Statistics
Top Marketing Science Advisors
Founders are Thought Leaders
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Why should you care what I have to say?
First to do Choice based Conjoint Analysis commercially (1983)
First academic paper for Latent Class CBCA (1995)
First integrated model for multiway segmentation based on Latent Class Models (1996)
First academic paper for Menu based Conjoint Analysis (2000)
Introduced MaxDiff scaling at ESOMAR (2000)
Best paper at Sawtooth Conference: MaxDiff (2003)
ESOMAR best paper of the year award for MaxDiff (2004)
Best paper in Marketing Research Magazine: MaxDiff (2005)
American Marketing Association Parlin Award (2011)
NextGen Marketing Research LinkedIn group: Individual Disruptive Innovator award (2012)
Marketing Research Council of NYC: MR Hall of Fame (2013)
Over two dozen papers and presentations at industry conferences on analytics & modeling
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What is Data Science?
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What is Data Science?
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What is Data Science?
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Credit: Drew Conway
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What is Data Science?
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Market Segmentation
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Analytic steps in a typical segmentation study
Collect RatingsFactor
AnalysisCluster
AnalysisAssignment
Tool
Tandem Clustering
“Tandem clustering (i.e. factor analysis followed by cluster analysis) is an outmoded and statistically insupportable practice.”
Arabie & Hubert (1994)© 2014 by in4mation insights, LLC 9
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Principle Components Analysis Principal Components Analysis
What are you doing?
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Discriminate Analysis Discriminant Analysis
What are you doing?
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Rating Scales
Factor Analysis
Cluster Analysis
What’s my beef with common segmentation practice? The short list.
Guiding Principle:
Segmentation is a search for differences
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Hierarchical Bayesian Modeling
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��1 = ��1 +��11 ∗����������+��21 ∗����������_��������_����������+��31 ∗��ℎ����������+��41 ∗����������_������������
��2 = ��2 +��12 ∗����������+��22 ∗����������_��������_����������+��32 ∗��ℎ����������+��42 ∗����������_������������
What are the effects of price and in-store display on sales of supermarket product?
�����������������!= ��+��1 ∗��������������+��2 ∗ln(����������)
Lower ModelUpper Model
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What could effect sales of SKUs in a store?
Lower Model
National TV
Local TV
Radio
Outdoor
Magazines
Newspapers
Social media activity
Website & search
Upper Model
Channel
Geography
Ingredients
Location at point of sale
Store size
Store age
Store format
Company vs. franchise
Demos of trading area
Lower Model
Base Price
Discounted Price
Feature
Display
Form
Size
Coupons
Seasonality
Holidays
Weather
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Effectiveness Index per $1MM in Brand Size:
121 30 97 91 72 125 181 111 72
$28,328
$16,980
$11,939
$9,318
$6,828
$4,766 $4,751 $3,312
$1,518
Brand A Brand B Brand C Brand D Brand E Brand F Brand G Brand H Brand I
Bayesian analysis works best when there are many items, brands, stores or regions that need to be compared.
Category Average $9,722
121 30 97 91 72 125 181 111 72
TV Effectiveness:Sales/GRPs
Items can be compared to
average
Items can be indexed
against their volume
Growth opportunity
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Choice Modeling &Trade-Up
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Discreet Choice Model Discrete Choice Model
What are you doing?
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True or False:Discrete Choice Models are the exact same thing as Choice-based Conjoint Analysis
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A typology of choice models
How many brands or items chosen?
Only one More than oneH
ow
man
y u
nit
s o
f eac
h it
em
ch
ose
n?
Mo
re t
han
on
eO
nly
on
e
Dell100 GB Hard Drive
4 MB RAMBasic Processor17-inch Screen
MS Office90-day Warranty
Total Price: $1,170
HP200 GB Hard Drive
2 MB RAMEnhanced Processor
19-inch ScreenMS Office
90-day WarrantyTotal Price: $1,480
Sony Vaio500 GB Hard Drive
4 MB RAMBasic Processor14-inch ScreenNo MS Office
1 Year WarrantyTotal Price: $1,840
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Price elasticity is about substitutability
$13.99 $10.99
$12.99
$229 $249
$234
$179 $199
$199
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Trade-up happens when shoppers are willing to spend more.
$3.99
Coca-ColaClassic
6-pk, 12oz cans
$5.49
Coca-ColaClassic
12-pk, 12oz cans
$1.49
Coca-ColaClassic
20 oz bottle
$1.89
Coca-ColaClassic
2 liter bottle
Quality Count Size
Coca-ColaClassic
12-pk, 12oz cans
$5.49
Private LabelCola
12-pk, 12oz cans
$2.99
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Trade-Up Model assumptions
Products are not substitutes
Trade up/down is asymmetric
Consumers will purchase the most quantity that they can
Subject to their budget limit
Subject to diminishing returns
Having money left over after making the purchase is good
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Market share simulations: Trade-Up vs. HBCBCA Logit Model
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0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
$299 $399 $499 $699 $899
Pre
dic
ted
Mark
et
Sh
are
Price of Brand A
BRAND C (Tradeup)BRAND C (HB Logit)
BRAND B (Tradeup)BRAND B (HB Logit) Market share is
predicted to be higher for Brand A in the Trade-up model.
BRAND A (Tradeup)
BRAND A (HB Logit)
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Market share simulations: Trade-Up vs. HBCBCA Logit Model
© 2014 by in4mation insights, LLC
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
$299 $399 $499 $699 $899
Pre
dic
ted
Mark
et
Sh
are
Price of Brand A
Which price elasticity makes more sense?
BRAND A (Tradeup)
BRAND A (HB Logit)
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Modeling the Marketing Ecosystem
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The Business Intelligence Landscape is changing.
More holistic view of business needed
Increasing role of social & digital media
Fusing data sources into new databases
Mine existing data
Existing analytic tools assume static rather than dynamic view
Integrate consumer-based metrics into modeling and planning models
Need to accurately measure both short- and long-term marketing effects
Need reliable measurement of effects of traditional marketing vs. social/digital media
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Any time series can be modeled as a simple process, where next month is a function of previous months.
Offline & Online Marketing Tactics
BHMs & Social Metrics
Sales
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Some marketing tactics may have an immediate effect on sales, while others may take time to change opinions.
Sales
Imm
ed
iate
Offline & Online Marketing Tactics
BHMs & Social Metrics
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Once opinions have changed, time passesbefore the impact on sales may be seen.
Sales
Imm
ed
iate
Offline & Online Marketing Tactics
BHMs & Social Metrics
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Feedback occurs as higher sales affect consumer perceptions, leading to changes in consumer sentiment and more online ‘buzz” and activity.
Sales
Imm
ed
iate
Offline & Online Marketing Tactics
BHMs & Social Metrics
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Recent application
200 Millward Brown attributes & funnel metrics
20 marketing spend tactics
Number of channels = 11
Number of SKUs = 15
Number of time periods = 39 each SKU
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Hierarchical Bayesian statistics
Complex systems of linear or nonlinear equations
Often no analytic solution
Uses Monte Carlo simulation
Predict quantitative or qualitative phenomena
Incorporate sensible prior beliefs or knowledge
Different coefficient for each unit of analysis at the “lower”
level
“Upper” level = Context = “why behind the what”
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Big Data in, Big Data out
So, if Lower = 50 and Upper = 100, for 5,000 iterations
Confectionery ~ 3,000 SKUs 15,000,000 coefficients
Laundry products ~ 5,000 SKUs 25,000,000 coefficients
Auto Parts ~ 75,000 SKUs ~ 400 Million coefficients
Total coefficients = N_Units * (Lower + Lower * Upper)
at every iteration of the Bayesian estimation
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Not all SKUs and retailers are created equal.
Low HighLow High Low High
Lower Price Elasticity Higher
Fragrances Makeup Skin Care
AB
CD
Retaile
rs
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Trade-up has become a familiar part of the global consumer landscape.
Perceptually superior and higher price
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Behavioral Model
WhatMarketers Do
`What Consumers
Think & FeelWhat
Consumers Do
Brand Tracking
Advertising Testing
Market Response Models
Marketing Tactics
Brand Health &Social Metrics
Sales Performance
Analytic Framework
Proposed model
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Lack comprehensive
view
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