Predictive Analytics + Constrained Optimization =

Efficient Marketing Paul%Maiste,%President,%Lityx%

www.lityx.com%|%Maiste@lityx.com%March%18,%2015%

INFORMS%New%York%

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.

! Background%! PredicGve%OpGmizaGon%Models%! Constrained%OpGmizaGon%! Case%Studies%

•  Prospect%Offer%OpGmizaGon%•  Print%Media%TargeGng%OpGmizaGon%•  Retail%Direct%Mail%TargeGng%OpGmizaGon%

! Wrap%Up%

3%

Agenda

! 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%

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

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

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%

! 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

%%%A%couple%of%predicGve%opGmizaGon%modeling%examples%

9%

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%

11%

Actual Offer Elasticity Curves

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”%

%%%

What%about%constraints?%

13%

! 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%

! 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

%%%

Prospect%Offer%OpGmizaGon%

16%

Case Study 1

! 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

18%

Data View

19%

Outputs Number'to'Contact'by'Offer'

Budget'by'Offer'

! 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%

%%%

%Print%Media%TargeGng%OpGmizaGon%(Cadence)%

21%

Case Study 2

! 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

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)

! 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

25%

Outputs

26%

Output

%%%

%Retail%Direct%Mail%TargeGng%OpGmizaGon%

27%

Case Study 3

! 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

29%

Outputs Mail'Qty'by'Decile'

Mail'Qty'by'Prior'Times'Targeted'

Mail'Qty'by'Store'Loca#on'

! ImplemenGng%modeling%and%constraints%has%helped%generate%incremental%sales%consistently%since%the%program%begain%mid\July%2014%

30%

Results

! Incorporate%predicGve%opGmizaGon%modeling%•  Cadence%/%prior%contacts%•  Direct%mail%package/message%

o What%is%opGmal%way%to%communicate%with%each%prospect?%

31%

Future Objectives

! 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

%%%

Paul%Maiste%maiste@lityx.com%www.lityx.com%

%

33%

Questions