predictive analytics + constrained optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015...
TRANSCRIPT
![Page 1: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/1.jpg)
Predictive Analytics + Constrained Optimization =
Efficient Marketing Paul%Maiste,%President,%Lityx%
www.lityx.com%|%[email protected]%March%18,%2015%
INFORMS%New%York%
![Page 2: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/2.jpg)
2%
Abstract
PredicGve%modeling%has%become%more%mainstream%in%the%last%5%years%as%companies%add%more%advanced%capabiliGes%to%their%analyGc%tool%sets.%%There%has%also%been%more%talk%about%incorporaGng%concepts%related%to%opGmizaGon%(constrained%or%unconstrained)%into%business%decision%making.%%There%is%no%argument%that%each%can%be%quite%powerful%in%their%own%right. In%this%presentaGon,%the%speaker%will%take%it%to%the%next%level:%combining%predicGve%models,%predicGve%opGmizaGon,%and%constrained%opGmizaGon%techniques%to%provide%tools%that%allow%marketers%to%make%opGmal%decisions%and%dramaGcally%improve%efficiency.
![Page 3: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/3.jpg)
! Background%! PredicGve%OpGmizaGon%Models%! Constrained%OpGmizaGon%! Case%Studies%
• Prospect%Offer%OpGmizaGon%• Print%Media%TargeGng%OpGmizaGon%• Retail%Direct%Mail%TargeGng%OpGmizaGon%
! Wrap%Up%
3%
Agenda
![Page 4: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/4.jpg)
! Lityx%is%an%analyGc%soluGons%and%services%firm%with%a%proprietary%cloud\based%predicGve%modeling%and%opGmizaGon%pla]orm%LityxIQ.%
Lityx Background
! We%apply%deep%experGse%in%complex%analyGc%soluGons%with%a%focus%on%applicaGons%in%markeGng%analyGcs%and%customer%relaGonship%management.%
4%
![Page 5: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/5.jpg)
5%
How%Can%%We%Make%It%%Happen?%
Business Value
Co
mp
lexi
ty
High Low
Low
High
What Happened?
Why Did It Happen?
What Will Happen?
How Can We Make It Happen?
Descriptive Analytics
Prescriptive Analytics
Predictive Analytics
Diagnostic Analytics
Gartner%framed%analyGc%progression%as%the%move%forward%from%InformaGon%to%OpGmizaGon%
Analytic Progression
![Page 6: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/6.jpg)
6%
How%Can%%We%Make%It%%Happen?%
Business Value
Co
mp
lexi
ty
High Low
Low
High
What Happened?
Why Did It Happen?
What Will Happen?
How Can We Make It Happen?
Descriptive Analytics
Prescriptive Analytics
Predictive Analytics
Diagnostic Analytics
Gartner%framed%analyGc%progression%as%the%move%forward%from%InformaGon%to%OpGmizaGon%
Analytic Progression
![Page 7: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/7.jpg)
Op#mal'Marke#ng'Strategy%
Scoring%and%ImplementaGon%
Constrained%OpGmizaGon%
PredicGve%(OpGmizaGon)%Modeling%
7%
Generic Concept
! Separate%techniques%work%together%
! We%can’t%forget%the%execuGonal%piece%of%it%(scoring/%implementaGon)%
! The%global%problem%(opGmal%markeGng)%is%solved%with%the%combinaGon%
![Page 8: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/8.jpg)
! Where%does%OpGmizaGon%appear%in%this%approach?%• PredicGve%models%are%naturally%an%opGmizaGon%process%(nothing%new%here).%
• “PredicGve%OpGmizaGon%Models”%incorporate%markeGng%tacGcs%into%the%predicGve%model%inputs.%o Offer%type%o Offer%value%o Discounts%o Messaging,%sequencing%o Channel%
• Constrained%OpGmizaGon%incorporates%hard%constraints%into%the%overall%soluGon%o Business%or%markeGng%constraints%o Contractual%constraints%o Channel%constraints%
8%
Optimization Components
![Page 9: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/9.jpg)
%%%A%couple%of%predicGve%opGmizaGon%modeling%examples%
9%
![Page 10: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/10.jpg)
10%
Offer Optimization
$ OID HLF 0MP P
EEDO RD1HPBMRL
EEDO
EEDO )EEDO (
2TNDB DCOMEH AH H U
( ) +
N H MEEDO HP EEDO ( CHPBMRL
OMEHAD
OMNMPHHML
LNOMEHAD
OMNMPHHML
2TNDB DC 0RP M DOOMEH AH H U -
3 0=5 3
=O LP B HML 4HP MOU 6MU U 1D MFO N HBP
NNDLCDC 1 OID HLF 2 D DL P
OID HLF 6DSDOP$ EEDO =UND$ EEDO RD
Offer'Elas#city'Curves'
Modeled%Behaviors%
MCD DC /D SHMOP$ DPNMLPD D$ NDLC% RD
MarkeGng%Levers%
![Page 11: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/11.jpg)
11%
Actual Offer Elasticity Curves
![Page 12: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/12.jpg)
12%
Cadence Optimization
.905 .629 .528 .489
.796 .644 .593 .480
.606 .564 .469 .402
.361 .543 .515 .390
Note:%Gray%=%low%sample%size%
Not%targeted%in%last%two%months%
Targeted%two%
months%ago%
Targeted%last%month%
Targeted%both%of%last%
two%months%
Last'Two'Months'Ac#vity'
Not%targeted%3%or%4%months%ago%
Three'an
d'four'm
onths'a
go'
Targeted%4%months%ago%
Targeted%3%months%ago%
Targeted%both%3%and%4%months%ago%
Lowest%values%here%
Best%values%here%
We'can'incorporate'cadence'into'our'predic#ve'op#miza#on'model.''
'Recency'and'frequency'can'be'categorized'in'a'number'of'different'ways'
Cells'show'average'observed'response'rates'
More%“Freshness”%
More%“Freshness”%
![Page 13: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/13.jpg)
%%%
What%about%constraints?%
13%
![Page 14: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/14.jpg)
! Constraints%make%the%overall%problem%harder%to%solve,%and%(even%worse)%give%a%less%than%globally%opGmal%soluGon.%
! But%we%need%to%account%for%them.%! Number%and%complexity%of%constraints%determine%the%soluGon%approach.%
14%
Business/Marketing Constraints
Dimensions' Constraints' Solu#on' Example'
0% 1%(total%budget)% Rank%order%all% Overall%markeGng%budget%
1%(channel)% 1%(total%budget)% Rank%order%by%dim% MarkeGng%budget%by%channel%
1%(channel)% 2%(channel%budget)% Rank%order%by%dim% Channel%max%spend%and%min%spend%
2+% 2+% Harder!%%Need%tools%
Channel%max%spend%with%product%min%performance%+%more%
![Page 15: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/15.jpg)
! In%our%offer%opGmizaGon%example,%if%there%was%a%policy%to%never%offer%more%than%$100,%we%would%not%realize%global%opGmality.%
15%
Sub-optimality
![Page 16: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/16.jpg)
%%%
Prospect%Offer%OpGmizaGon%
16%
Case Study 1
![Page 17: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/17.jpg)
! We%want%to%maximize%%• Expected%profitability%OR%• Response%rate%OR%• Expected%revenue%
! We%have%predicGve%opGmizaGon%models%that%predict%• Spend%by%prospect%• Response%rate%by%prospect%
! Constraints%• Limits%on%total%amount%of%offers%• Limits%on%freebies%(e.g.,%hotel%rooms%capacity%constraints)%• Experimental%tesGng%and%cross\decile%sampling%• Segment%level%markeGng%plan%
17%
Offer Optimization Problem
![Page 18: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/18.jpg)
18%
Data View
![Page 19: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/19.jpg)
19%
Outputs Number'to'Contact'by'Offer'
Budget'by'Offer'
![Page 20: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/20.jpg)
! For%this%industry,%prospects%are%placed%into%an%offer%matrix%for%markeGng%execuGon.%
20%
Implementation
%%High'Offer'Value' Low'Offer'Value'
Offer%1% Offer%2% Offer%3% Offer%1% Offer%2% Offer%3%Predicted'Value''<$75%%
3506% 3962% 3331% 763% 2095% 1428%
Predicted'Value''
$75R$299'%%
5556% 4572% 6074% 358% 612% 641%
Predicted'Value''
$300R$749'%%%
1642% 384% 394% 38% 12% 17%
Predicted'Value''$750+%
475% 12% 14% 10% 0% 0%
![Page 21: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/21.jpg)
%%%
%Print%Media%TargeGng%OpGmizaGon%(Cadence)%
21%
Case Study 2
![Page 22: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/22.jpg)
! MulGple%Channels%• Newspaper%inserts%• Shared%mail%(mulGple%programs)%
! Many%choices%• 30,000+%zip%codes%• 60,000+%targeGng%zones%
! Many%constraints%! PredicGve%opGmizaGon%models%predict%zip\level%response%rate%given%prior%history,%cadence,%demographics%
! Decision%• On%a%monthly%basis,%how%do%we%best%allocate%our%fixed%budget?%%(i.e.,%what%zips/zones%should%be%targeted)%
22%
Print Media Targeting Problem
![Page 23: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/23.jpg)
23%
It’s a complex process
Aggregation of program/zip level actual historical response rates
Zip/zone targeting history (cadence)
Historical response rates
Media Data Business Rules and Constraints
Marketing constraints
Mathematical Optimization
Model
• Minimum insert restrictions
• Fixed setup costs • Target/Do not target
rules
• Cross-channel budget min/max percent
• Max papers • Restricted papers
Ranked Zone List - Ordered by Decreasing
Expected CPO
Final Optimization Result
• Zip/zone maps • CPM • Circulation
Predictive Model of Newspaper Zone Response Rates
Predictive Model of Shared Mail Zone Response Rates Dynamic Data
(Processed Monthly)
Prediction of Response Rates at Zone Level
Static Models (updated approx yearly)
Census and Geo-demographic data at
Zip level
23%
Scoring
Somewhat Static Data (yearly)
![Page 24: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/24.jpg)
! The%constraint%list%covers%a%wide%variety%of%metrics,%dimensions,%and%business%rules.%
! Without%these%accounted%for,%the%problem%could%not%be%solved%to%the%saGsfacGon%of%the%business.%
24%
Constraints
![Page 25: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/25.jpg)
25%
Outputs
![Page 26: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/26.jpg)
26%
Output
![Page 27: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/27.jpg)
%%%
%Retail%Direct%Mail%TargeGng%OpGmizaGon%
27%
Case Study 3
![Page 28: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/28.jpg)
! Maximize%overall%response%rate%! Each%prospect%has%a%predicGve%model%score%based%on:%
• Demographics%(age,%income,%educaGon)%• LocaGon%(distance,%driveGme%to%closest%store)%• Appended%behaviors%(credit%card%usage,%internet%usage)%
! Constraints%• Meet%store%minimum%quanGty%needs%• Cross\decile%tesGng%and%metrics%• Prior%Gmes%targeted%
o Cadence%is%(currently)%controlled%in%this%case%study%through%constraints%–%not%incorporated%yet%into%a%modeling%paradigm.%
28%
Retail Direct Mail Problem
![Page 29: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/29.jpg)
29%
Outputs Mail'Qty'by'Decile'
Mail'Qty'by'Prior'Times'Targeted'
Mail'Qty'by'Store'Loca#on'
![Page 30: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/30.jpg)
! ImplemenGng%modeling%and%constraints%has%helped%generate%incremental%sales%consistently%since%the%program%begain%mid\July%2014%
30%
Results
![Page 31: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/31.jpg)
! Incorporate%predicGve%opGmizaGon%modeling%• Cadence%/%prior%contacts%• Direct%mail%package/message%
o What%is%opGmal%way%to%communicate%with%each%prospect?%
31%
Future Objectives
![Page 32: Predictive Analytics + Constrained Optimization ...nymetro.chapter.informs.org/prac_cor_pubs/03-2015 Paul Maiste Modeling and Optimization...Predictive Analytics + Constrained Optimization](https://reader030.vdocuments.site/reader030/viewer/2022040907/5e7d69f58a6e2c76761b6937/html5/thumbnails/32.jpg)
! Purng%it%all%together%raises%overall%complexity%! But%the%reward%can%be%significant%
• Improve%key%business%metrics%• Ease%of%markeGng%execuGon%
! Tools%can%help%
32%
Summary