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From Data to Revenue:Prescriptive Analytics with RapidMiner
David Weisman, Ph.D.
LATEX compile time: August 21, 2014, 14:27
© 2014 David Weisman. All rights reserved.
If you’d like to use this material for any purpose,please contact [email protected].
Outline
Prescriptive Analytics Landscape
Sneak Peek: RapidMiner Prescriptive Analytics Extension
Outline
Prescriptive Analytics Landscape
Sneak Peek: RapidMiner Prescriptive Analytics Extension
Prescriptive Analytics delivers largest value
DescriptiveAnalytics
PrescriptiveAnalytics
How can wemake it happen?
PredictiveAnalytics
What willhappen?
Whathappened?
Difficulty
Value
Derived from: Gartner (December 2012)
Prescriptive Analytics provides large gains
20 millionmiles / year
UPS Right-hand turns minimizefuel consumption
$20 million / year American Airlines Optimize crew pairing
$200 million NBC Improve productivity
35% cost reduction Volkswagen USA Optimize supply chain
$294.8 million Proctor & Gamble Optimize sourcing withsuppliers
$3.5 million / year Fingerhut Choose most profitablesequence of catalogs
$53 million Taco Bell Optimize costs by schedulingand allocating work crews
Chen, DS, et al. Applied Integer Programming: Modeling and Solution, 2011
Gartner Hype Cycle describes technology adoption
Innovation Trigger
Peak of
Inflated Expectations
Trough of Disillusionment
Slope of Enlightenment Plateau of
Productivity
time
expectations
Plateau will be reached in:
As of July 2013 Bioacoustic Sensing
Smart Dust
Quantum Computing
Quantified Self 3D Bioprinting Brain-Computer Interface
Human Augmentation
Volumetric and Holographic Displays
Electrovibration
Affective Computing
Prescriptive Analytics
Autonomous Vehicles Biochips
Neurobusiness
3D Scanners Mobile Robots
Speech-to-Speech Translation
Internet of Things Natural-Language Question Answering
Big Data
Consumer 3D Printing Gamification Wearable User Interfaces
Complex-Event Processing
Content Analytics
In-Memory Database Management Systems Virtual Assistants
Augmented Reality Machine-to-Machine Communication Services
Mobile Health Monitoring NFC
Mesh Networks: Sensor
Cloud
Computing
Virtual Reality
In-Memory Analytics
Gesture Control
Activity Streams Enterprise 3D Printing
Biometric Authentication Methods
Consumer Telematics Location Intelligence
Speech Recognition
Predictive Analytics
Source: Gartner
Predictive Analytics is mainstream
g
Location Intelligence
Speech Recognition
Predictive Analytics
Source: Gartner
Prescriptive Analytics is coming fast
Electrovibration
Affective Computing
Prescriptive Analytics
Source: Gartner
Example: Maximize hospital bed utilizationOptimize hospital bed allocationNaïve allocation Optimized allocation
bed 1
bed 2
D1-3
A1-2
E2-4
B5-7
C4-7
November1 2 3 4 5 6 7
November1 2 3 4 5 6 7
D1-3
A1-2
E2-4
B5-7
C4-7
F3-5
In practice:I Many roomsI Many bedsI Many constraints (medical equipment, patient requirements)I Hugely inefficient with ad-hoc planning http://docs.jboss.org
Predictive Analytics → Prescriptive Analytics
Optimize hospital bed allocationNaïve allocation Optimized allocation
bed 1
bed 2
D1-3
A1-2
E2-4
B5-7
C4-7
November1 2 3 4 5 6 7
November1 2 3 4 5 6 7
D1-3
A1-2
E2-4
B5-7
C4-7
F3-5
→
Boston Globe, Smart Scheduling
Gartner describes Prescriptive Analytics
Descriptive Predictive Prescriptive
% Usage 43%–68% 13% < 3%
Technologies OptimizationSimulation
AnalyticalMethods
Linear ProgrammingInteger ProgrammingStochastic ProgrammingMonte-Carlo Approaches
Vendors Matlab, Wolfram,iThink, Oracle GAMS,CPLEX (IBM), Gurobi,Xpress (Fico), SAS-OR
Derived from Gartner materials
Prescriptive Analytics market has an enormous gap
Limited tools Operations Researcher tools
Prescriptive Analytics market has an enormous gap
Operations Researcher toolsLimited tools
Microsoft
Prescriptive Analytics market has an enormous gap
Limited tools Operations Researcher tools
POSITIVE VARIABLESDISASSEMB QUANTITY OF CHICKENS DISASSEMBLED
PARAMETER SALEPRICE(COMPONENTS)SALES PRICE OF COMPONENT PARTS/NECK 0.20, GIBLETS 0.70/
EQUATIONSOBJT OBJECTIVE FUNCTION (NETINCOME)LIMIT CHICKEN AVAILABILITY;OBJT.. NETINCOME =E=
+ SUM(COMPONENTS,SALEPRICE(COMPONENTS)* SALES(COMPONENTS));
LIMIT.. 1/3 *DISASSEMB =L= 1500;
GAMS sample from McCarl, BA, et al. Applied mathematical programming using algebraic systems, 1997
Prescriptive Analytics market has an enormous gap
Limited tools Operations Researcher tools
Optimize
obj
con
res
Business optimization has 3 components
Objective: Business goal
I Maximize sales revenueI Minimize manufacturing costI Maximize patient health
Decision variables: Elements we control to reach goal
I Which customers to up-sellI Volume to purchase from each supplierI Which patients get home treatment
Constraints: Limitations or requirements
I Boston sales must book >2× NYC salesI Each supplier gets < 20% of our businessI Hospital has 200 beds
Business optimization has 3 components
Objective: Business goal
I Maximize sales revenueI Minimize manufacturing costI Maximize patient health
Decision variables: Elements we control to reach goal
I Which customers to up-sellI Volume to purchase from each supplierI Which patients get home treatment
Constraints: Limitations or requirements
I Boston sales must book >2× NYC salesI Each supplier gets < 20% of our businessI Hospital has 200 beds
Business optimization has 3 components
Objective: Business goal
I Maximize sales revenueI Minimize manufacturing costI Maximize patient health
Decision variables: Elements we control to reach goal
I Which customers to up-sellI Volume to purchase from each supplierI Which patients get home treatment
Constraints: Limitations or requirements
I Boston sales must book >2× NYC salesI Each supplier gets < 20% of our businessI Hospital has 200 beds
Business optimization has 3 components
Objective: Business goal
I Maximize sales revenueI Minimize manufacturing costI Maximize patient health
Decision variables: Elements we control to reach goal
I Which customers to up-sellI Volume to purchase from each supplierI Which patients get home treatment
Constraints: Limitations or requirements
I Boston sales must book >2× NYC salesI Each supplier gets < 20% of our businessI Hospital has 200 beds
Outline
Prescriptive Analytics Landscape
Sneak Peek: RapidMiner Prescriptive Analytics Extension
Take a sneak peek: Prescriptive Analytics Extension
Optimize
obj
con
res
Today you’ll see:I Mixed-integer linear solverI Heuristic route optimizerI Fun Boston demoI Cloud-based optimization
Here are some optimization algorithms
HelpViewToolsProcessEditFile
Operators
Search
Prescriptive Analytics (5)
Local Prescriptive Analytics (4)
Linear And Integer Programming (3)
Heuristic (1)
Traveling-salesperson solver (simple 2d)
Cloud Prescriptive Analytics (1)
Linear And Integer Programming (1)
Cloud-based NEOS MILP solver
Prescriptive Analytics are easy in RapidMinerHelpViewToolsProcess
big help big process WizardResults (F9) Design (F8) Home
Help
C l o u d - b a s e d N E O S M I L P s o l v e r
S y n o p s i s
Solve linear and integer programs in the cloud using NEOS.
D e s c r i p t i o n
This cloud-based operator solves linear, integer, and
mixed-integer linear programs using the NEOS server. The
underlying solver engine is Gurobi.
I n p u t
Parameters
typedecision variable datatype attribute
nameconstraint name attribute
inequalityconstraint inequality attr ibute
resourceLimitconstraint l imit attr ibute
coefficientdecision variables coefficients
maximizecriterion
NEOS_MILP (Cloud-based NEOS MILP solver)
LogProblems
No problems found
Message Fixes Location
Process
Main Process
Read CSV
f i l ou t
Read CSV (2)
f i l ou t
NEOS_MILP
Dec
Con
Exa
Exa
inp res
res
res
Process
Onboard April 9
LPSolve simple
set exp 01
Prescriptive Analytics (1)
Cloud Prescriptive Analytics (1)
Linear And Integer Programming (1)
Cloud-based NEOS MILP solver
Let’s do Prescriptive Analytics Challenge #1
Objective I Maximize fun during Boston visit
Decision variables I 50 sites we could visit:XFenway ParkXWally’s Cafe (best local jazz)...X History tour with quiz at the end
Constraints I $500 budgetI Required minimum levels of:
Culture >= 12 sitesHistory >= 4 sitesEntertainment >= 9 sitesSports >= 5 sites
Define a fun level for each tourist activity
HelpViewToolsProcessEditFile
big help big process Wizard Results (F9) Design (F8) Home
Result Overview ExampleSet (Read Objective)
Row No. I tem FunLevel Type
1 Back Bay 6 bin
2 Beacon Hill 5 b in
3 Bleacher Bar 8 bin
4 Boston Children's Museum 2 bin
5 Boston Harbor Islands National Recreation Area 7 bin
6 Boston Pops 4 bin
7 Boston Public Garden 4 bin
8 Boston Public Library 1 bin
9 Symphony Hall 4 bin
1 0 Bunker Hill Monument 7 bin
1 1 Café 939 8 bin
1 2 Charles River Esplanade 6 bin
1 3 Duck Tours 7 bin
1 4 Dunkin Donuts 9 bin
1 5 Faneuil Hall Marketplace 5 bin
1 6 Fenway Park 1 0 bin
1 7 Harpoon Brewery Tour 1 0 bin
1 8 Harvard University 7 bin
1 9 Harvard Museum of Natural History 8 bin
2 0 House of Blues 8 bin
2 1 Huntington Theatre Company 6 bin
2 2 Institute of Contemporary Art 100 9 bin
2 3 Isabella Stewart Gardner Museum 4 bin
2 4 John F. Kennedy Presidential Museum & Library 5 bin
allFilter (47 / 47 examples):ExampleSet (47 examples, 1 special attribute, 2 regular attributes)
Data
Statistics
Charts
Advanced Charts
Annotation
HelpViewToolsProcessEditFile
big help big process Wizard Results (F9) Design (F8) Home
Result Overview ExampleSet (Read Objective)
Row No. I tem FunLevel Type
2 4 John F. Kennedy Presidential Museum & Library 5 bin
2 5 Massachusetts Institute of Technology 9 bin
2 6 Mapparium 5 bin
2 7 Massachusetts State House 4 bin
2 8 Museum of Fine Arts 8 bin
2 9 Museum of Science 7 bin
3 0 New England Aquarium 7 bin
3 1 Newbury Street 6 bin
3 2 Newport Mansions 2 bin
3 3 North End 8 bin
3 4 Old North Church 5 bin
3 5 Old South Meeting House 5 bin
3 6 Paul Revere House 5 bin
3 7 Regattabar Jazz Club - Charles Hotel 7 bin
3 8 Ryles Jazz Club 7 bin
3 9 Scullers Jazz Club 7 bin
4 0 SoWa Galleries 6 bin
4 1 Spirit Cruises 7 bin
4 2 Sports Museum 8 bin
4 3 Top of the Hub Restaurant & Skywalk 6 bin
4 4 USS Constitution 5 bin
4 5 Trinity Church 5 bin
4 6 Wally’s Café 1 0 bin
4 7 Whale Watch 8 bin
allFilter (47 / 47 examples):ExampleSet (47 examples, 1 special attribute, 2 regular attributes)
Data
Statistics
Charts
Advanced Charts
Annotation
Define costs and categoriesHelpViewToolsProcessEditFile
big help big process Wizard Results (F9) Design (F8) Home
Result Overview ExampleSet (Join)
Row No. I tem Back Bay Beacon Hill Duck Tours Dunkin Donuts Wally’s Café Boston Children's Museum Boston Harbor Islands ...
1 Cost 0 0 3 5 3 4 0 2 0 5 0
2 FunLevel 6 5 7 9 1 0 2 7
3 Culture 1 1 0 1 1 1 0
4 History 1 0 0 1 1 0 1
5 Sports 0 0 0 1 0 0 0
6 Entertainment 0 0 1 1 1 0 1
allFilter (6 / 6 examples):ExampleSet (6 examples, 1 special attribute, 49 regular attributes)
Data
Statistics
Charts
Advanced Charts
Annotation
Define business constraints
HelpViewToolsProcessEditFile
big help big process Wizard Results (F9) Design (F8) Home
Result Overview ExampleSet (Join)
Row No. I tem Direction Limit Back Bay Beacon Hill Duck Tours Dunkin Donuts Wally’s Café Boston Children's Museum
1 Cost < = 500 0 0 3 5 3 4 0 2 0
2 FunLevel > = 1 0 6 5 7 9 1 0 2
3 Culture > = 1 2 1 1 0 1 1 1
4 History > = 4 1 0 0 1 1 0
5 Sports > = 5 0 0 0 1 0 0
6 Entertainment > = 9 0 0 1 1 1 0
allFilter (6 / 6 examples):ExampleSet (6 examples, 1 special attribute, 49 regular attributes)
Data
Statistics
Charts
Advanced Charts
Annotation
LP Solve
Dec
Con
Exa
Exagives our optimized choices
HelpViewToolsProcessEditFile
big help big process Wizard Results (F9) Design (F8) Home
Result Overview ExampleSet (Read CSV)
Row No. decisionVar value
1 OBJECTIVE 218
2 Back Bay 1
3 Beacon Hill 1
4 Bleacher Bar 1
5 Boston Children's Museum 0
6 Boston Harbor Islands National Recreation Area 0
7 Boston Pops 0
8 Boston Public Garden 1
9 Boston Public Library 1
1 0 Symphony Hall 0
1 1 Bunker Hill Monument 1
1 2 Café 939 1
1 3 Charles River Esplanade 1
1 4 Duck Tours 1
1 5 Dunkin Donuts 1
1 6 Faneuil Hall Marketplace 0
1 7 Fenway Park 1
1 8 Harpoon Brewery Tour 1
1 9 Harvard University 1
2 0 Harvard Museum of Natural History 1
2 1 House of Blues 1
2 2 Huntington Theatre Company 0
2 3 Institute of Contemporary Art 100 1
2 4 Isabella Stewart Gardner Museum 1
allFilter (54 / 54 examples):ExampleSet (54 examples, 0 special attributes, 2 regular attributes)
Data
Statistics
Charts
Advanced Charts
Annotation
HelpViewToolsProcessEditFile
big help big process Wizard Results (F9) Design (F8) Home
Result Overview ExampleSet (Read CSV)
Row No. decisionVar value
3 1 New England Aquarium 0
3 2 Newbury Street 1
3 3 Newport Mansions 0
3 4 North End 1
3 5 Old North Church 1
3 6 Old South Meeting House 1
3 7 Paul Revere House 1
3 8 Regattabar Jazz Club - Charles Hotel 0
3 9 Ryles Jazz Club 0
4 0 Scullers Jazz Club 0
4 1 SoWa Galleries 1
4 2 Spirit Cruises 0
4 3 Sports Museum 1
4 4 Top of the Hub Restaurant & Skywalk 0
4 5 USS Constitution 1
4 6 Trinity Church 1
4 7 Wally’s Café 1
4 8 Whale Watch 0
4 9 Cost 497
5 0 FunLevel 218
5 1 Culture 1 6
5 2 History 1 9
5 3 Sports 5
5 4 Entertainment 1 1
allFilter (54 / 54 examples):ExampleSet (54 examples, 0 special attributes, 2 regular attributes)
Data
Statistics
Charts
Advanced Charts
Annotation
How well did we do?
HelpViewToolsProcessEditFile
big help big process Wizard Results (F9) Design (F8) Home
Result Overview ExampleSet (Read CSV)
Row No. decisionVar value
3 1 New England Aquarium 0
3 2 Newbury Street 1
3 3 Newport Mansions 0
3 4 North End 1
3 5 Old North Church 1
3 6 Old South Meeting House 1
3 7 Paul Revere House 1
3 8 Regattabar Jazz Club - Charles Hotel 0
3 9 Ryles Jazz Club 0
4 0 Scullers Jazz Club 0
4 1 SoWa Galleries 1
4 2 Spirit Cruises 0
4 3 Sports Museum 1
4 4 Top of the Hub Restaurant & Skywalk 0
4 5 USS Constitution 1
4 6 Trinity Church 1
4 7 Wally’s Café 1
4 8 Whale Watch 0
4 9 Cost 497
5 0 FunLevel 218
5 1 Culture 1 6
5 2 History 1 9
5 3 Sports 5
5 4 Entertainment 1 1
allFilter (54 / 54 examples):ExampleSet (54 examples, 0 special attributes, 2 regular attributes)
Data
Statistics
Charts
Advanced Charts
Annotation
Let’s evaluate our solution
I 1014 possible combinations of activitiesI Far too complex to check by hand
I Plan:Generate 1000 other solutions that meet all constraintsCompare against our optimal solution
We did far better than 1000 other feasible solutions
We maximized fun
within our budget
0
50
100
150
100 150 200 250
FunLevel
Exp
erim
ent c
ount
We used most of budget to maximize fun
We maximized fun
within $500 budget
100
125
150
175
200
225
$425 $450 $475 $500
Cost
Fun
Sub−optimalsolution count
5 10 15 20
Category levels well above minimum constraints
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Cost FunLevel Culture History Sports Entertainment
5% vertical jitter for clarity
Prescriptive Analytics Challenge #2:Optimize our tourist route
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Define Boston tourist optimization challenge
Objective I Minimize transportation distance
Decision variables I Ordered list of sites to visit
Constraints I Start in Back BayI Visit each site exactly onceI End in Back Bay
Huge problem: 1034 routes satisfy constraints
Define Boston tourist optimization challenge
Objective I Minimize transportation distance
Decision variables I Ordered list of sites to visit
Constraints I Start in Back BayI Visit each site exactly onceI End in Back Bay
Huge problem: 1034 routes satisfy constraints
RapidMiner + Google Maps provide geocoding
Main Process
Read CSV
f i l ou t
preprocess
in
in
ou t
ou t
Encode URLs
exa exa
ori
Enrich Data.. .
Exa Exa
Write CSV
inp th r
f i l
inp res
res
HelpViewToolsProcessEditFile
big help big process Wizard Results (F9) Design (F8) Home
Result Overview ExampleSet (//Local Repository/tmp/data/places)
Row No. I tem Street
1 4 Dunkin Donuts 127 Tremont
1 5 Faneuil Hall Marketplace ?
1 6 Fenway Park 4 Yawkey Way
1 7 Harpoon Brewery Tour 306 Northern Avenue
1 8 Harvard University ?
1 9 Harvard Museum of Natural History 26 Oxford Street
2 0 House of Blues 15 Lansdowne St
2 1 Huntington Theatre Company 264 Huntington Ave
2 2 Institute of Contemporary Art 100 Northern Ave
2 3 Isabella Stewart Gardner Museum 25 Evans Way
2 4 John F. Kennedy Presidential Museum & Library Columbia Point
2 5 Massachusetts Institute of Technology ?
2 6 Mapparium 200 Massachusetts Ave
2 7 Massachusetts State House ?
2 8 Museum of Fine Arts ?
2 9 Museum of Science 1 Science Park
3 0 New England Aquarium Central Wharf
3 1 Newbury Street 1 Newbury St
3 2 Newport Mansions 424 Bellevue Ave Newport RI
3 3 North End 1 Prince St
3 4 Old North Church 193 Salem Street
3 5 Old South Meeting House 310 Washington Street
3 6 Paul Revere House 19 North Square
3 7 Regattabar Jazz Club - Charles Hotel 1 Bennett Street
3 8 Ryles Jazz Club 212 Hampshire Street
3 9 Scullers Jazz Club 400 Soldiers Field Road
4 0 SoWa Galleries 450 Harrison Ave
4 1 Spirit Cruises 200 Seaport Boulevard
4 2 Sports Museum 100 Legends Way
4 3 Top of the Hub Restaurant & Skywalk 800 Boylston Street
4 4 USS Constitution ?
4 5 Trinity Church 206 Clarendon St
4 6 Wally’s Café 427 Massachusetts Ave
4 7 Whale Watch Central Wharf
allFilter (47 / 47 examples):ExampleSet (47 examples, 0 special attributes, 2 regular attributes)
Data
Statistics
Charts
Advanced Charts
Annotation
HelpViewToolsProcessEditFile
big help big process Wizard Results (F9) Design (F8) Home
ExampleSet (//Local Repository/tmp/data/placesGeocoded) ExampleSet (//Local Repository/tmp/data/places)
Result Overview ExampleSet (//Local Repository/tmp/data/placesGeocoded)
Row No. I tem address longitude lati tude
1 4 Dunkin Donuts 127 Tremont Street, Boston, MA 02108, USA-71.062 42.356
1 5 Faneuil Hall Marketplace Faneuil Hall, Faneuil Hall Marketplace, 1 Faneuil Hall Square, Boston, MA 02109, USA-71.056 42.360
1 6 Fenway Park 4 Yawkey Way, Boston, MA 02215, USA -71.098 42.346
1 7 Harpoon Brewery Tour 306 Northern Avenue, Boston, MA 02210, USA-71.034 42.347
1 8 Harvard University Harvard University, Cambridge, MA 02138, USA-71.117 42.377
1 9 Harvard Museum of Natural History26 Oxford Street, Harvard University, Cambridge, MA 02138, USA-71.115 42.379
2 0 House of Blues 15 Lansdowne Street, Boston, MA 02215, USA-71.096 42.347
2 1 Huntington Theatre Company 264 Huntington Avenue, Boston, MA 02115, USA-71.086 42.342
2 2 Institute of Contemporary Art 100Northern Avenue, Boston, MA, USA -71.042 42.350
2 3 Isabella Stewart Gardner Museum25 Evans Way, Boston, MA 02115, USA -71.098 42.338
2 4 John F. Kennedy Presidential Museum & LibraryColumbia Point, University of Massachusetts Boston, Boston, MA 02125, USA-71.035 42.315
2 5 Massachusetts Institute of TechnologyMassachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA-71.094 42.360
2 6 Mapparium 200 Massachusetts Avenue, Boston, MA 02115, USA-71.086 42.345
2 7 Massachusetts State House Massachusetts State House, Boston, MA 01233, USA-71.064 42.359
2 8 Museum of Fine Arts Museum of Fine Arts, Boston, 465 Huntington Avenue, Boston, MA 02115, USA-71.094 42.339
2 9 Museum of Science Museum Of Science Driveway, Boston, MA 02114, USA-71.071 42.368
3 0 New England Aquarium Central Wharf, Boston, MA 02110, USA -71.050 42.359
3 1 Newbury Street 1 Newbury Street, Boston, MA 02116, USA -71.071 42.353
3 2 Newport Mansions 424 Bellevue Avenue, Newport, RI 02840, USA-71.309 41.482
3 3 North End 1 Prince Street, Boston, MA 02113, USA -71.053 42.364
3 4 Old North Church 193 Salem Street, Boston, MA 02113, USA -71.055 42.366
3 5 Old South Meeting House 310 Washington Street, Boston, MA 02108, USA-71.059 42.357
3 6 Paul Revere House 19 North Square, Boston, MA 02113, USA -71.054 42.364
3 7 Regattabar Jazz Club - Charles Hotel1 Bennett Street, Cambridge, MA 02138, USA-71.123 42.372
3 8 Ryles Jazz Club 212 Hampshire Street, Cambridge, MA 02139, USA-71.100 42.373
3 9 Scullers Jazz Club 400 Soldiers Field Road, Allston, MA 02134, USA-71.118 42.360
4 0 SoWa Galleries 450 Harrison Avenue, Boston, MA 02118, USA-71.065 42.343
4 1 Spirit Cruises 200 Seaport Boulevard, Boston, MA 02210, USA-71.040 42.351
4 2 Sports Museum 100 Legends Way, Boston, MA 02114, USA -71.062 42.366
4 3 Top of the Hub Restaurant & Skywalk800 Boylston Street, The Shops at the Prudential Center, Boston, MA 02199, USA-71.083 42.348
4 4 USS Constitution USS Constitution Museum, Charlestown, MA 02129, USA-71.055 42.374
4 5 Trinity Church 206 Clarendon Street, Boston, MA 02116, USA-71.075 42.350
4 6 Wally’s Café 427 Massachusetts Avenue, Boston, MA 02118, USA-71.082 42.341
4 7 Whale Watch Central Wharf, Boston, MA 02110, USA -71.050 42.359
allFilter (47 / 47 examples):ExampleSet (47 examples, 0 special attributes, 4 regular attributes)
Data
Statistics
Charts
Advanced Charts
Annotation
Optimize route with Traveling-Salesperson solver
Main Process
Read Objec.. .
f i l ou t
prep Constr. . .
in ou t
ou t
LP Solve
Dec
Con
Exa
Exa
prep route
in
in
in
ou t
ou t
ou t
ou t
Write TSP
inp th r
f i l
Traveling-s.. .
Exa Exa
inp
res
res
res
res
Traveling-s.. .
Exa Exa optimized our itineraryHelpViewToolsProcessEditFile
big help big process Wizard Results (F9) Design (F8) Home
Result Overview ExampleSet (LP Solve) ExampleSet (Multiply)ExampleSet (Traveling-salesperson solver (simple 2d))
Row No. f rom t o distance
1 Back Bay Wally’s Café 8 8
2 Wally’s Café Mapparium 6 0
3 Mapparium Museum of Fine Arts 9 6
4 Museum of Fine Arts Isabella Stewart Gardner Museum 4 0
5 Isabella Stewart Gardner Museum Fenway Park 8 4
6 Fenway Park Bleacher Bar 1 7
7 Bleacher Bar House of Blues 1 0
8 House of Blues Harvard University 363
9 Harvard University Harvard Museum of Natural History 2 4
1 0 Harvard Museum of Natural History Massachusetts Institute of Technology 283
1 1 Massachusetts Institute of Technology Charles River Esplanade 9 6
1 2 Charles River Esplanade Café 939 5 4
1 3 Café 939 Duck Tours 2 8
1 4 Duck Tours Boston Public Library 5 5
1 5 Boston Public Library Trinity Church 2 3
1 6 Trinity Church Newbury Street 4 9
1 7 Newbury Street Boston Public Garden 1 8
1 8 Boston Public Garden Massachusetts State House 7 7
1 9 Massachusetts State House Dunkin Donuts 3 2
2 0 Dunkin Donuts Old South Meeting House 3 2
2 1 Old South Meeting House Beacon Hill 2 4
2 2 Beacon Hill Sports Museum 7 8
2 3 Sports Museum Museum of Science 8 8
2 4 Museum of Science Bunker Hill Monument 136
allFilter (33 / 33 examples):ExampleSet (33 examples, 0 special attributes, 3 regular attributes)
Data
Statistics
Charts
Advanced Charts
Annotation
Here’s our optimized route
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Our optimized route was shorter than 10,000 others
We did better
0
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0.00 0.25 0.50 0.75 1.00 1.25
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Prescriptive Analytics Challenge #3:Optimize churn customer retention campaign
HelpViewToolsProcessEditFile
big help big process Wizard Results (F9) Design (F8) Home
Application Wizard
Churn Data
To build a predict ive model of churn, you start with a table of historical data about your customers. This table has one row per customer, and columns describing the customer. Typical columns for churn prediction include
length of the business relationshipfrequency of customer transactionstypes of products purchasedaverage purchase sizessurvey resultscomplaint frequency.
Customer demographics can also improve predictive accuracy, and you can include attributes such as
agegendersegmentjob typegeographic or postal location.
In general, it is good to include columns based on your business expertise and intuition.Customer relationships change over time and this evolution can help predict departures. To represent these changes, you can include columns that describe t rends . For example, you can include year-over-year changes in sales for the past five years.The data table must include a column indicating churn . In the demo data provided here, this column is named churn . For customers whose churn status you know, put a yes or n o in the churn column. For
4 Run Analysis
Take m e t o t h e results
3 Churn Column
yesPositive class:
ChurnSelect column:
Please select a column containing the information whether or not a customer churned.
I D Churn Gender A g e Region code Transaction count Avg balance Total accounts
113704 no f 5 2 1100 3 2 145490 4
622299 ? f 5 7 8715 1 242542 1
609274 no m 4 4 5145 2 8 79100 5
623378 ? f 5 7 2857 1 6 1 1
860912 yes f 4 7 3368 4 4 63939 1
Clear dataYour Data
ChurnResultsD a t aApplicationR
ep
osi
tori
es
Traditional marketing churn prediction uses lift chart
HelpViewToolsProcessEditFile
WizardResults (F9)Design (F8)Home
Application Wizard
Export Result Dashboard
Show the process
Churn Results
Congratulations, you have successfully built a churn prediction model. This dashboard shows the results of building and evaluating the quality of this model.
Lift ChartThe first result is a lift chart, which relates prediction confidence to the actual number of customers who churn. The leftmost bar represents the highest confidence predictions, and its height represents the number of churned customers predicted. In the ideal case, the high confidence predictions correctly predict the majority of churned customers.
Top Churn CandidatesThe next panel shows the customers with the highest prediction of churning. You can directly target these customers for retention.
Decision TreeRapidMiner built a decision tree to predict customer churn. You can examine this tree to understand why customers churn, and directly use this information in your marketing strategy. To see all parts of the tree, you can zoom the tree with the mouse wheel, and drag the tree by holding the left mouse button and moving the mouse.
6 Analytical Results
Count for Churn = yes Cumulative (Percent)
[0 .8 - ] [0.6 - 0.8] [0.2 - 0.4] [ - - 0.2]
Confidence for yes
0
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4
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ou
nt
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10%
20%
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Percen
t of yes
18 / 20
3 / 44 / 6
10 / 42
For the model's strongest predictions, the lift chart examines how likely these customers are to churn.
Lift chart
> 1.500
> 4245
> 1.500 1 .500
> 6432.500
> 127912.500 127912.500
6432.500
4 2 4 5
> 1921.500 1921.500
1 .500
Transaction count
Region code
Total accounts
noRegion code
Avg balance
yes no
no
Region code
yes no
yes
The decision tree models customer churn.
Decision Tree
ChurnResultsDataApplication
Rep
osito
ries
Real-world marketing has complex constraints
Objective I Optimize campaign forlargest total account balance
Decision variables I RapidMiner predicted 50k churnersDecide best customers to target≈ 1015,000 possible solutions
Constraints I Budget to target 5k customersI Target average > 30 transactions/yearI Max of 50 targets in regions 2 – 4I Balance gender (1/3 – 2/3 female)
Cloud computing solves big data optimization
SYMPHONYMOSEK
XpressMP MINTOMinto
scip
Main Process
prep Input
in
in
out
out
Multiply
inp out
out
out
out
prep Object...
in
in
out
out
prep Constr...
in
in
out
out
associate c...
in
in
in
out
out
Cloud-based NEOS MILP solver
Dec
Con
Exa
Exa
inp
inp res
res
res
Gurobi
Success: Cloud optimized our retention campaign
Constraints:I Budget ≤ 5000
I Avg trans/year ≥ 30
I ≤ 50 in regions 2 – 4
points jittered
Here’s the Prescriptive Analytics Roadmap
I Integrate multiple LP and MILP solversI Open source solvers with appropriate licensingI Commercial solvers
I Integrate cloud-based solvers
I Integrate heuristic bin packing methods
I Ship to select customers in 2015 Q1
I Provide domain-specific RapidMiner Wizards
I Provide simulation
Let’s summarize Prescriptive Analytics
I Prescriptive analytics produces large financial gains
I RapidMiner fills a huge product gap
Limited tools Operations Researcher tools
I Cloud computing solves large optimization challenges