Sports Alliance works for 106 Football Clubs in 6 European countries. These clubs, with the help of Sports Alliance, have found a way to turn their Fan data into Data Driven Marketing. This Case Study will show you how this is done and what the results are.

1. “WE CONNECT THE DOTS
BETWEEN DATA AND FANS”
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2. SPORTS ALLIANCE
MARKET LEADER IN
EUROPE
Sports Alliance sets itself apart from the competition with over
100 clients in four countries, three leagues and data
management of over 12.000.000 fan profiles in Europe. With a
‘can do’ attitude and a focus on getting results, we’ve created
long lasting relationships and we actually make Fan
Relationship Management possible.
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4. FRM - MADE EASY
Fan Marketers should only be focused on maintaining a relationship
with the fans of the club. That’s only possible when the marketer has
a clear 360 degrees picture of the fan. Therefor it’s crucial to get all
the data together and that should be as easy as possible. That’s why
Sports Alliance offers a pragmatic approach that guarantees a
solution and a way to get to work quickly. That’s why we say, “we
connect the dots between data and fans”.
• Data Integration with Club Sources
• Data Management with the Sports Alliance datawarehouse /
Orangebox
• Business Intelligence via Tableau
• Contact Management with Microsoft Dynamics CRM
• Campagne Management with the Management Console

5. DATA MANAGEMENT
2. VALIDATE
Check street name against
postcode database
1. CLEAN
Standardize, capitalize, swear
words.
Deduplicate the double records
based on the Sports Alliance
deduplication algorithm. This
algorithm can be adjusted to the
client needs.
3. DEDUPLICATE
This is the hart of the GDPR
compliance process. Permissions,
or in GDPR terms consent, are
recorded and managed for all fan
data. This process also deals with
Right to be Forgotten, Right to Data
Portability and the right to object.
4. AVG/GDPR
COMPLIANCE
5. THE GOLDEN
RECORD
The final result is a database with
unique fans and their transactions that
form the basis for digital marketing.

6. DATA
MANAGEMENT
PROCES PER
CLIENT• BucketCus table contains raw
data from integration
• The address is standardized
and validated
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BucketCus
Address Formatted
Address
Processing
Candidate Raw
Candidate
Processing
Pre-Process
Functions &
Stored
Procedures
Testing
Functions &
Stored
Procedures
Candidate Test
Outcome
Applying Rules
Functions &
Stored
Procedures
Candidate
Formatted
Group / GroupInfo
Deduplication
Functions &
Stored
Procedures
Test
Definition Table
Rules
Definition Table
Grouping Criteria
Definition Table
Change
Notification
Customer Detail
PreProcess
Definition Table

7. DATA
MANAGEMENT
PROCES PER
CLIENT• Validated data is tested on
150+ attributes
• This will prepare the contact
data for deduplication
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BucketCus
Address Formatted
Address
Processing
Candidate Raw
Candidate
Processing
Pre-Process
Functions &
Stored
Procedures
Testing
Functions &
Stored
Procedures
Candidate Test
Outcome
Applying Rules
Functions &
Stored
Procedures
Candidate
Formatted
Group / GroupInfo
Deduplication
Functions &
Stored
Procedures
Test
Definition Table
Rules
Definition Table
Grouping Criteria
Definition Table
Change
Notification
Customer Detail
PreProcess
Definition Table

8. DATA
MANAGEMENT
PROCES PER
CLIENT• Data is deduplicated or
grouped based on set of rules,
these rules may differ per
client
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BucketCus
Address Formatted
Address
Processing
Candidate Raw
Candidate
Processing
Pre-Process
Functions &
Stored
Procedures
Testing
Functions &
Stored
Procedures
Candidate Test
Outcome
Applying Rules
Functions &
Stored
Procedures
Candidate
Formatted
Group / GroupInfo
Deduplication
Functions &
Stored
Procedures
Test
Definition Table
Rules
Definition Table
Grouping Criteria
Definition Table
Change
Notification
Customer Detail
PreProcess
Definition Table
OrderID GroupingDescription
1Records with same forename, surname, DOB, and at least one contact channel
2Records with the same forename, surname, and at least TWO contact channels, but one with DOB and one without
3Records with the same forename, DOB, Female and at least TWO contact channels, dealing with surname changes after marriage
4Records with DOB and at least one contact channel, with no mismatch on names and gender
5Records one with DOB and one without
6Records without DOB
7Records with Email only

9. THE CLIENT DATA
IS HELD IN THE
‘ORANGE BOX’
• Incoming integrations differ
per client
• We recognize 8 transaction
types: Tix, Acc, Mer, Mem,
Loy, Corp, Web, Email
• One shared platform for all
120 clients
• Data model, after
standardization, is the same
for every client
• Separate benchmarking
database
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10. SPORTSALLIANCE
APPS
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11. CAMPAIGN
MANGEMENTTOOL
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12. ASTON VILLA
MY VILLA MOMENTS
This campaign was personalised with
information about the Fan’s behaviour
throughout the season. It recognises the
games a fan saw and the travel kilometres a
Fan put to support his team.

13. DASHBOARDS,
REPORTING,
TABLEAU
• Tableau integrated in
the Sports Alliance
Apps applicatie
• Mix of ‘Standard’ and
‘Custom Made’
• Benchmarking
available across all
120 clients
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14. CLUB DASHBOARDS
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16. STANDARD
DASBOARD
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17. ANALYTICAL
PROJECTS
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18. CAMP NOU
PROJECT
• Nou Camp is being renewed
over a period of 8 years
• Construction requires the re-
seating of socios during
reconstruction
• To assist in planning the re-
seatting, a map of the Camp
Nou stadion based on x and y
coordinates was created
• The maps show different types
of socio seat in the stadion
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19. Go Ahead
Access
Analyse
• Data from access
control system
• How many supporters
are late
• What entry points are
botlenecks
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20. Swansea Family
Stand Project
• Swansea traditionally did not
differentiate in prices
throughout the stadium
• This meant loss of revenue &
no opportunity for a family
stand
• This anlysis looked into family
relations:
• Adjacent families
• Non adjacent families
• Adjacent cohabitor
• Adjacent shared surnames
• Other season ticket holders
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21. BENCHMARKING
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22. Purpose Benchmarking
PURPOSE
• Monitor client activity
• Monitor client progress
WHAT ARE WE LOOKING TO
MEASURE?
• Is there client activity?
• What is the client activity
ecxactly?
• If there is, how does that
compare to similar clients?
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23. InternalMarketing
Benchmark

24. Purpose Benchmarking
PURPOSE
• Provide leagues and
gouverning bodies with
overview
WHAT ARE WE LOOKING TO
MEASURE?
• Compare stats between clubs
and make them comparable
• Ticketing sales
• No show
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25. Purpose Benchmarking
PURPOSE
• Provide perspective to client
marketing achievement
WHAT ARE WE LOOKING TO
MEASURE?
• Ticket sales
• Season Ticket Saels
• Email Open Rates
• No show
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26. VitesseBenchmark

27. Questions?
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28. CONTACT
HENK-FRITS VERKERK
Directeur
T: +31 (0)6 418 412 72
E: hfverkerk@sportsalliance.com
W: www.sportsalliance.com
WOUTER VAN DIJK
Directeur
T: +31 (0)6 106 229 03
E: wvandijk@sportsalliance.com
W: www.sportsalliance.com
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