Data Mining and CRM Cindy Estis Green Managing Director, The Estis Group [email protected] HITEC® 2003 Produced by Hospitality Financial and Technology Professionals
Data Mining and CRM
Cindy Estis GreenManaging Director,
The Estis [email protected]
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…
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
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
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
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
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
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 [email protected]
HITEC® 2003Produced by Hospitality Financial and Technology Professionals