NCDM Datamining Case Study 2010

NBA TEAM SCORES WITH DATA MINING:

A CASE STUDY IN MODELING AND PROFILING

Presented by:

James R. Stafford

What is modeling & profiling?

Who uses modeling and profiling?

Common approaches

7 steps to success

Case study - NBA team upsell study

Game Plan

Modeling & Profiling

Who will respond?

Identify cross-sell opportunities

Who is likely to lapse/churn?

What do my best customers look like and how can I get more?

Who should receive what message?

Increase revenues, profit, and maximize ROI on marketing $

What is Predictive Modeling?

Predicting outcomes and future events based on historical data relating to:

- past response- transactions/purchase history- geo-demographic- lifestyle, and other attributes

What is Customer Profiling?

Profiling is a data discovery procedure that uses standard queries and statistical analysis

to segment customers and prospects based on important

traits like R,F,M, transaction/purchase behavior,

and demographics.

Who uses PredictiveModeling?

Response Cross-Sell Lapse/Churn Reactivate Lifetime Value Most Profitable

Banks & Financial Services

Publications Retail Catalogers Telco’s High-Tech Hospitality & Gaming

The Industry The Problem

Which approach should be used?

RFM CHAID Linear regression Logistic regression Neural nets

Linear regression CHAID Neural nets

limited number of answers

If the business problem has a...wide range of answers

7 steps to successful modeling and implementation

Identify the business problem Data audit -- what’s available and

relevant? Create training and validation files Use best modeling approach and

appraise results Does the model make sense? Validate the model Test campaign

CASE STUDY IN MODELING

& PROFILING

The Business Problem

National Basketball Association Team Declining attendance Expanding to new stadium with more seats

Marketing Objectives Up-sell: Mini-plan to Season ticket holders Prospecting: identify Season ticket plan prospects

Applicability to you...

Retention and up-sell -- NBA franchise has products/services and desires repeat buyers

Desire to differentiate customers with different purchasing behavior

Desire to acquire new & profitable customers

Create marketing efficiency & cut promotion costs

Data audit - customer data

Street address

# of Seats

7 game mini-plans & 14 game combos

7A = “World’s Best” -- Dream Team players

7B = “Weekend Fest” -- Fri., Sat., Sun. games

7C = “Wild West” -- Western conference teams & Chicago Bulls

21 game mini-plans

Full season ticket holders

Data preprocessing & overlay

Correct and standardize addresses

Geo-code addresses to census neighborhoods

Append updated area-level demographics

Append PRIZM lifestyle cluster types

Create training and validation files

Training file - 1884 records (75% of file)

Validation file - 651 records (25% of file)

Must always use random sampling!

Use best modeling approach

CHAID Linear regression Logistic regression Neural net

Use best modeling approach

Appraise results – Gains chart for our best model

Let’s just mail to the 50% most

likely to respond, and we’ll get 70%

of the likely responders

_______Highly targeted

and saves money

Appraise results - Gains chart for our best logistic regression model

Appraise results - Gains chart for our best linear regression model

Does the model validate?Does the model validate?

Training Data Validation Data

Does the model make sense -- what do my customers look like?

PRIZM Cluster Groups

T1: Landed Gentry C1: 2ndCitySociety

S1: EliteSuburbs

U1: UrbanUptown

R1:CountryFamilies

R2:Heart-landers

R3:RusticLiving

T2:ExurbanBlues

T3:WorkingTowns

C2: 2ndCityCenters

C3: 2nd City Blues

S2: TheAffluentials

S3: InnerSuburbs

U2: UrbanUpscale

U3: UrbanCores

Urbanization

PRIZM cluster composition for segments

Modeled C1 C2 S1 S2 S3 U1 U3Segment 1 1.6 2.4 31.2 4.0 12.8 12.0 34.4 2 2.4 16.3 56.5 1.6 0.8 13.7 4.0

10 5.5 28.4 11.0 5.5 18.1 1.6 4.7

19 2.8 2.8 10.1 32.1 4.6 2.8 0.020 5.5 0.9 18.4 22.9 2.8 0.0 0.0

TOTAL 6.0 9.5 24.0 14.9 11.0 6.1 4.9

EliteSuburbs

UrbanCores

Top demi-decile, i.e., those most likely to become

season ticket holders

Education

4 + Years of College

1-3 Years College HS Graduate < 12 Years

Household income

< $15,000 $15,000 - $34,999 $35,000 - $74,000 >= $75,000

Occupation

Professional/Mgr Other W/Collar Blue collar Service Farm/Ranch/Mine

Household size

1 Person 4 + Persons HH w/Child

Summary profile of “the best” segments

Wealthy whites, Asians and Arabic

High spending levels Highest income High education High investment

Multi-racial Multi-lingual Dense/urban Home & apartment renters High % of singles High % of single parents High unemployment Lowest income group

U3 - Urban CoresS1 - Elite Suburbs

Mostlikelytoo...

S1 U3Lifestyle

Country club Football games Contrib $50+ to PBS 15 + Lottery ticket/mo Sail Play pool Housekeeper Pro basketball Classical music Smoke Contract home improve Fast Mexican food

Mostlikelytoo...

Media S1 U3 News/talk radio Rock radio Murphy Brown The Simpsons Masterpiece Theater Rescue 911 Jazz radio Jazz radio Masters Golf Tourn Listen/football Travel & Leisure Car & Driver Fortune Ebony National Geographic Consumer's Digest Business section Classified section

Mostlikelytoo...

Buy S1 U3Cappuccino Malt liquorImported wine Domestic beerPita bread Fruit LoopsMontblanc pen Adidas shoesComputer Pepsi Saks K-Mart

How Can You Use This Information ?

Develop different messages Use different media/marketing approaches to

reach them Buy prospect lists based on best segment

profiles Develop retention and prospecting plans with

customized offers (e.g., free CD’s based on their particular tastes in music)

For each major customer segment, you can...

===>> improved customer up-sell and retention and better prospecting!

Potential marketing plans

S1 U3Giveaways

1,000 FF miles Mini-music systemCD - Classical/Jazz CD - Jazz/RockFree WSJ sub Free Consumer Report sub

Contests1 trip to the Master's, or… 1 trip to Super Bowl, or… the NBA finals the NBA finals50 Montblanc pens 50 pairs Adidas/Nike

AdvertiseJazz stations Jazz stationsClassical stations Rock stationsLocal Business Section Local Classified section

S1 U3Giveaways

Potential marketing plans

S1 U3Giveaways

AdvertiseJazz stations Jazz stationsClassical stations Rock stationsLocal Business sections Local Classified section

Summary - why model & profile?

To identify those customers most likely to behave in certain ways (respond, cancel, etc.)

To see what those customers are like (high income, infrequent purchasers, etc.)

To identify what motivates our customers (price, frequency of contact, etc.)

To create mass personalizations

Expected results

Increased ROI on marketing dollars - e.g., only mail to those most likely to respond

Increased customer loyalty Decreased attrition rates Higher actual lifetime value

Maximize each customer relationship

NBA TEAM SCORES WITH DATA MINING:

A CASE STUDY IN MODELING AND PROFILING

Presented by:

Jim Stafford

NCDM Datamining Case Study 2010

Documents

DataMining Andahuaylas

Datamining 3rd naivebayes

Curso datamining

CrispDM1.0: dataMINING

Datamining Presmmmmmmmmmmmmmmmmm

Datamining Glossary

Model Datamining

Datamining intro

DataMining Overview

NCDM Datamining Case Study 2010

DATAmining (1)

Datamining r 4th

DataMining Python

Diktat Datamining

Datamining Lab

Datamining @ ARTreat

Introduzione al datamining

Mis Datamining

DataMining Kyoto

Why Datamining?