Data Mining and CRM

Cindy Estis GreenManaging Director,

The Estis GroupCme25@cornell.edu

HITEC® 2003Produced by Hospitality Financial and Technology Professionals

Buzzwords through the Ages

• Guest History--1991

• Database Marketing--1994

• Data Warehousing--1997

• CRM-- 1999

• ERM– 2002--?

Single Major Change?

Shift from a focus on the tactics...(read: technology)

to the strategies(read: what are you going to do with “it”

once you have it?)

CRM DefinitionCustomer Relationship Management is a comprehensive process in which a company fundamentally improves the quality of every

interaction with its customers and prospects…

…through the use of relevant, timely and actionable information,

resulting in improved profitability.

The Path to CRMB

usin

ess

Valu

e

No Information-Based Marketing

(Asset-Centric)

Increased Database Marketing Capabilities

(Market-Centric)

Customer Relationship Management

(Customer-Centric)

Delighted Customer Experience

Information-Based Marketing Over Time

Identify best targets for specific product/servicepromotions

Evaluate programperformanceto demonstrate ROI

Product-focused executionof marketing strategies: Acquisition, retention, extension

Customer-driven organization focused on optimizing the life time value of each relationship

Coordinated communications across all channels, products and services

Customer-focused development of products & services

Organizational learning based on shared knowledge

Traditional Database Marketing

Customer Relationship Management

The Move to CRM

CRM Evolution

Target MarketingTarget Marketing

Customer KnowledgeCustomer Knowledge

Campaign ManagementCampaign Management

Interaction ManagementContact Integration

Interaction ManagementContact Integration

In the 90’s, CRM initiatives focused on gaining a better understanding of customer behavior and marketing impact

In the future, the focus will be on action.

• A Reward Program• A Quick Fix

• A Computer System• A Mailing List Generator• A Single Source Solution• A Department in Sales &

Marketing

CRM is not…

CRM Impact

• Marketing• Customer Service• Product Development• Distribution Planning• Partnering

Relationship Marketing ModelPr

efer

ence

Commitment

Potential Pitfalls

• Lack of executive sponsorship• Short-term focus• Lack of employee involvement and empowerment• Lack engagement of internal constituents• No exit strategy• Over promise or undefined promise• Non-quantitative measurement plan• Over-emphasis on technology and creative

“The Future…”• It’s not about programs, it’s about the customer• Scope is changing• Continued movement beyond miles and points • Information in real time…both ways• Channel and brand integration

Technology transforms data into information. Marketing and Business

teams make it knowledge.

Data

Information

Knowledge

Proprietary Data Warehouse

Customer CentricData Warehouse

PatternDetection

ExtractionTransformation

OperationalAnalysis

Execution

Why Use a Data Warehouse?

• Marketing Intelligence to Set Strategy

• Manage Tactical Plan

Data Warehousing Applications for the Hospitality Industry

• Prospecting for New Business

• New Business from Old Customers

• Retention of Old Customers

• Attrition Analysis / Who is Leaving?

• Guest Service

Commonly Tapped Data Sources• Property Management System• Central Reservation System• Sales and Catering System• Mailing Campaign Systems• Internet Data• Inquiry Files• Frequent User Members• Secondary Sources: Industry or Consumer Data

1. Central Warehouse Repository / Access2. Data Reporting3. Data Mining4. Data Modeling & ROI5. Customer Life Cycle

Five Stages of Data Warehousing

EXTRACT TRANSFORM LOAD

DATA DATA BASEBASE

Study & Apply Business Rules

Cleaning

Verification

Calculation

Enhancement

Building the Data Warehouse

Property PMS Specialized Software & Processing

Relational DataModels

ADDRESS CLEANING & STANDARDIZATION

UGLY!

Good:9324 Thurman Court

Wichita, KS 67212

Bad:One Bethesda Metro Center

Bethesda, MD 20814

Get Name on Arrival

XXXXXX,or

BLANK!

Examples of Data Transformation

Examples of Data Transformation

CLEANING & STANDARDIZATION

1234 High Street PLANO-TEXAS 75234 1234 HIGH STR. PLANO, TX 75234Cherry Avne No555, Lng Beach California 90222 555 CHERRY AVE. LONG BEACH, CA 90222

VERIFICATION

1234 HIGH STR.555 CHERRY AVE

NCOA

CASS

1234 HIGH STR. = MAILABLE555 CHERRY AVE = UNMAILABLE

In DatabaseFrom PMS

HOUSEHOLDING

Bill Smith 1234 East First Street Denver CO 82003William Smith 1234 E 1st St. Denver 82003Mr Smith 1234 East First Street Denver CO 80023B Smyth 1234 East 1st St. Denver 82003

WILLIAM SMITH 1234 E FIRST ST. DENVER CO 80023-2567

Examples of Data Transformation

Examples of Data Transformation

CALCULATIONS & ENHANCEMENTS

APPENDED INFORMATION

Arrival 3/2/98Departure 3/5/98Reservation 1/15/98

Length of Stay = 3Arrival Day = TuesdayDeparture Day = FridayLead Time = 49 daysStay Weekpart = MidweekYear = 1998Month = MarchSeason = High & Spring

From PMS In Database

Joe Smith123 Main StreetChicago IL 65348

Joe SmithCooke County IL Married Age Group 35-49Chicago MSA 2 Children GolferLatitide 1675.3 Income $75 K Snow SkierLongitude 2345.7 Vehicle Value $25 K

It’s a Two Part Communication

Guest Hotels

Where do I want to go?What do I want to do?What deal can I get?

Guest ProfileCommunicationSpecial OffersProspecting

Off Property

F&BRoom Type

Room LocationNewspaperMagazines

Guest Preferences

On Property

CommunicateCommunicate

DifferentiateDifferentiate

IdentifyIdentify

CustomizeCustomize

Steps to CRM

UsageUsage•Products Purchased

•Visit Frequency•Lead Time•Arrival Patterns

GeographyGeography•Where Guest Lives•Company Office•Booker Location

SourceSource•Travel Agents•Wholesalers•Groups•Internet

DemographicsDemographics•Psychographics•Household Income•Age•Presence ofChildren

“Slicing”

“Dicing”“Drilling

Down”

Guest Profile Alternatives

Birds of a feather flock together!

Case Study: Downtown Hotel

Corporate Property

Downtown NYC-Broadway

Example: Needs Leisure Weekend Guests

Goal: Develop social weekend package business

Demographics within Database:

Demographics: 40-49, $100K+, married, children in the home

Hotel use: 1.7 days, Saturday-Sunday

Geography-- Top 5 Counties: Nassau, Suffolk, Hartford, New Haven, Westchester

Goal: Leisure Weekend Guests

Combining their demographic profile with the primary feeder counties, they were able to find REPLICATES of their weekend package guest…something called...

Leisure Weekend Guests

Leisure Weekend Guests

Nassau County - #1 Target County

Ranking of Top Target Zip Codes

1. Manhasset 11030 GOLD

2. Roslyn 11576 RED

3. Woodmere 11598 GOLD

4. Roslyn Heights11577 GOLD

5. Woodbury 11797 RED

Leisure Weekend Guests

Case Study: Resort Hotel

Example: The Millennium Celebration -New Year’s Eve 1999

5 Diamond Resort

40,000 Leisure, Mailable Past Guest Households

$5,000 Package, 3 Night Minimum

Competing for the same guests that every other 5 diamond/star hotel was going after

Sought to Couple Direct Mail with Advertising and Direct Sales to Travel Agents

GOAL: Sell 350 - 450 packages for the event

Example: The Millennium Celebration -New Year’s Eve 1999

Data Mining:

Communicated to only the highest echelon of past guests

Profiled those past guests to find new prospects

Considered travel patterns and distance of all past guests and prospects

Found best travel agents - small focused group

Example: The Millennium Celebration -New Year’s Eve 1999

Data Mining:

TA MAILING 1 Dec-982000 Brochure 819 Agencies

Top producing TAs in primary feeder areas which are 1.Boulder 24 Agenciesbeing targeted by promotion. 2. Denver 128 Agencies

3. Col Springs 76 Agencies4. Ft. Collins 33 Agencies5. Chicago 145 Agencies6. OK City 73 Agencies7. Dallas 104 Agencies8. Ft. Worth 39 Agencies9. Houston 95 Agencies10. LA 102 Agencies

Example: The Millennium Celebration -New Year’s Eve 1999

Data Mining:Millennium New Year's Eve CelebrationCommunication Status and ReviewPROJECT DATE MEDIA SOURCE CRITERIA/PROFILE TOTAL

HOUSEHOLD MAILING 1 Dec-982000 Brochure

Detailed Below Detailed Below 40,081 HHLDS

Past Guests High Repeat 6,709 HHLDSThe highest echelon of mailable High LT Revenuenon-group guests. High Avg Spend Per StayProspects 33,372 HHLDSFeeder areas were chosen through 1.Dallas $125K+, Married 4,796 HHLDSthe "golden nugget" (2X List) 40-55 Ageprocess: zips w/some penetration 2. OK City $125K+, Married 1,154 HHLDSbut much potential. 45-65 Age

3. Boulder $125K+, Married 952 HHLDS45-65 Age

Additionally, travel patterns, 4. Chicago $145K+, Married 11,129 HHLDSmedia exposure opportunities, 45-65 Agehigh average spending MSAs, No childrenand other criteria were 5. Ft. Worth $145K+, Married 1,123 HHLDSconsidered when choosing 45-65 Agethese areas. No children

6. Houston $145K+, Married 8,505 HHLDS45-65 AgeNo children

7. LA $145K+, Married 5,713 HHLDS45-65 AgeNo children

HOUSEHOLD MAILING 2Apr-99 May-99

Letter/ Brochure 34,480 HHLD

Past Guests Letter 6,694 HHLDSSame group that received mailing 1deduped against future resv. Prospects 27,786 HHLDSSimilar to mailing 1, with new 2000 Broch. 1.Denver/ $100K+, Married 4,030 HHLDSfeeder area (KC). Boulder 40-65 Age

2. Dallas $100K+, Married 8,581 HHLDSIncome level slightly decreased, 40-65 Ageage category was extended. 3. Chicago $125K+, Married 7,439 HHLDSLayer media placement with 40-65 Agedirect mail (Gourmet). 4. KC $100K+, Married 3,753 HHLDS

40 65 Age

Example: The Millennium Celebration -New Year’s Eve 1999

FINAL OUTCOME

475 Packages Sold

$2.3 Million in Revenues

75,000+ Unique Households Received Promotion Information / Potential Residual Conversion

819 Travel Agencies touched/ Potential Future Bookings

Four Things To Remember

It’s not easy...It’s not fast...

It’s not cheap...It’s not an optionoption!!

Recognition vs. Reward

Two publications published by HSMAI Summer, 2002 to address

research done on this issue.

HSMAI Publication: Recognition vs. Reward

• Examined eight major hotel chains• Interviewed sixteen individual hotels

worldwide• Researched twenty non-hotel

companies• Evaluated use of recognition vs.

rewards

Hotel Chains-Overview• Most large chains depend primarily on

reward schemes

• Smaller, regional chains level the playing field with guest recognition

• Guest recognition, when most effective, is an element of a CRM strategy

• Supporting infrastructure built in IT, Organization, Training

Individual Hotels-Overview• Most hotels have VIP systems to designate

special recognition

• Repeat usage and frequent guest preferences are not tracked consistently

• Generally guests staying 2+ times are “frequent guests”

• Typical treatment includes pre-registration, fruit/water, call

Other Industries-Overview• Retail sector

– Segmentation based on purchase patterns– Main focus is promotional offers

• Casinos and Airlines – Some personalized guest service – Reliance on promotions and rewards

• Financial Services – Segmentation based on purchases, demographics,

spending in other industries– Personalized customer service– Highly personalized promotional offers

Conclusions?

• There is a trend toward recognition…the sophisticated traveler demands a higher level of personal service.

• This is more of a cultural challenge to execute than a technical one.

• Success requires integration of customer service, training, technology and DETERMINATION.

Summary

• CRM is a way of life in an organization• Technology provides tools to make this

happen• Promotional campaigns can be built around

the intelligence revealed by CRM• Consider the issue of recognition vs. reward

in your CRM strategies

Data Mining and CRM

Cindy Estis GreenManaging Director,

The Estis GroupCme25@cornell.edu

HITEC® 2003Produced by Hospitality Financial and Technology Professionals