1
Managing customer data spatially
Fifth Annual GIS 2007 (Melbourne)Serena Coetzee
University of Pretoria2 May 2007
2
3
South Africa & Tshwane
Afrikaans, EnglishIsiZuluIsiXhosaSiSwatiNdebeleSouthern
SothoNorthern
SothoTsongaSeTswanaVenda
Pretoria (executive)
Bloemfontein (judicial)
Cape Town (legislative)
4
South Africa
∙ 45 million people∙ 9 provinces∙ 262 municipalities
∙ 6 metropolitan municipalities
∙ 7 million land parcels∙ 6,3 million in (formal) urban areas
∙ 40% in Gauteng∙ 25% in the Western Cape∙ 16% in Kwa-Zulu Natal
∙ 500,000 sectional title properties∙ Largest address database: 3.5 million
5
University of Pretoria (Tukkies)
∙ 1906 Transvaal University College∙ University of Pretoria
∙ 38 000 residential students ∙ 28 000 undergraduates∙ 10 000 post-graduates∙ 47% male, 53% female
∙ 2 000 international students from 60 countries
∙ Faculties∙ Economics & Management Sciences∙ Humanities∙ Health Sciences∙ Engineering, the Built Environment
& Information Technology∙ Natural & Agricultural Sciences∙ Education∙ Law∙ Theology∙ Veterinary Sciences
6
History and Research Interests
ReGIS, Autodesk World
Spatial Datasets, PropertySPI
GI Standards
NAD on the gridCan we establish a virtual NAD for South
Africa in the form of a data grid?
+ + =
7
Overview
Managing customer data spatially∙ Why manage customer data spatially?∙ Spatially enabling customer data
∙ Planning∙ Spatial Information Strategy∙ Customer address data model∙ Master address database
∙ Implementation∙ Integrate the address data model∙ Transform customers into spatial customers∙ Coping with uncertainty
∙ Operation∙ The address data life cycle
∙ Using spatial customer data
8
Managing customer data spatially
∙ Why manage customer data spatially?∙ Spatially enabling customer data
∙ Planning∙ Spatial Information Strategy∙ Customer address data model∙ Master address database
∙ Implementation∙ Integrate the address data model∙ Transform customers into spatial customers∙ Coping with uncertainty
∙ Operation∙ The address data life cycle
∙ Using spatial customer data
9
Why manage customer data spatially?
The Future of I.T.: What's on Tap for 2007 and Beyond
1. Customer Service Surges as a Top Priority for 2007
2. Business Intelligence Tops the Strategic Technology List
Source: www.cioinsight.com
1
2
10
Why manage customer data spatially?
The 30 Most Important IT Trends for 2007
Technology
• The move to a new architecture marches on• Enterprise applications start losing their luster• Data quality demands attention• IT reluctantly embraces Web 2.0• IT innovation loses traction• Business process management services and
software will frustrate users• For business intelligence, the best is yet to come• IT organizations start going green
Source: www.cioinsight.com
1
2
3
4
5
6
7
8
11
Why manage customer data spatially?
“More than 25% of critical data used in large corporations is flawed due to
human data-entry error, customer profile changes (e.g. change of address),
poor processes and a lack of proper corporate data standards.”
The result: soiled statistics, faulty forecasting and
sagging sales
Source: Gartner Research quoted on www.cioinsight.com
12
Why manage customer data spatially?
“Through 2007,
more than 50% of data-warehousing projects will experience limited
acceptance, if not outright failure,
because they will not proactively address data-quality issues.”
Source: Gartner Research quoted on www.cioinsight.com
13
Why manage customer data spatially?
Source: www.gwsae.org
14
Why manage customer data spatially?
The insurance industry is ready for the corporate wide proliferation of geographic
information systems as insurers rely on data that has a geographic
component to determine accurate underwriting, risk analysis and claims
management.
Employ Geographic Information Systems to Manage Risk for Property and Casualty Insurers, 11 October 2006,
Stephen Forte Source: www.gartner.com
15
Why manage customer data spatially?
∙ Data quality∙ Address verification∙ Return to sender improvements
∙ Business intelligence for improved customer service∙ Routing and deliveries∙ Geo-marketing∙ Outlet planning∙ Demarcation (sales areas, etc.)
16
Managing customer data spatially
∙ Why manage customer data spatially?∙ Spatially enabling customer data
∙ Planning∙ Spatial Information Strategy∙ Customer address data model∙ Master address database
∙ Implementation∙ Integrate the address data model∙ Transform customers into spatial customers∙ Coping with uncertainty
∙ Operation∙ The address data life cycle
∙ Using spatial customer data
17
Planning
Challenges∙ Buy-in on executive level∙ Continuous long term process∙ Customer’s perception of what his/her
address should be
18
Planning
One of our strongest weapons is dialogue.
Nelson Mandela
19
Planning
∙ Understand and articulate the benefits of spatial customer data
∙ Convince non-technical executives about the benefits of spatial address data
∙ Associate the benefits to an identified risk or business event
Ready to start…
1
2
3
20
Planning
Spatial Information Strategy
Infrastructure (hardware & networks)
Spatial reference data
Software
Con
tract
s
Pro
cess
Business data $$$$
PeoplePeople
Reference Data
21
Handling the present(TPS)
Preparing for the future(BI, data mining, DSS,
EIS, MIS, OLAP)
Remembering the past(databases and data warehouse)
Planning
Source: Watson
Data
New business systems
Transactions
People People & &
technologytechnology
Address capturing
, for delivery
Spatial Analysis
Address Structuri
ng & Cleaning
22
Planning
Source: DM Functional Framework by DAMA
Data ManagementFunctions
23
Planning
∙ Who is responsible for customer address?∙ Information (CIO)∙ Analytics (GIS)∙ Development (IT)∙ Business (CRM)
∙ Decide why you need spatial customer data
∙ Design the address data model
24
Planning
Purpose: Address Verification
28.273632-
25.792344
101 Koljander AvenueNewlandsPretoriaGauteng
25
Planning
Purpose: Deliveries
26
Planning
Purpose: Customer profiling
28.270885-
25.790764
101 Koljander AvenueNewlandsPretoriaGauteng
45 Nutmeg AvenueNewlandsPretoriaGauteng
27
Managing customer data spatially
∙ Why manage customer data spatially?∙ Spatially enabling customer data
∙ Planning∙ Spatial Information Strategy∙ Customer address data model∙ Master address database
∙ Implementation∙ Integrate the address data model∙ Transform customers into spatial customers∙ Coping with uncertainty
∙ Operation∙ The address data life cycle
∙ Using spatial customer data
28
Cow of many -well milked and badly fed
Spanish proverb
29
Planning: Address data models
Geographic Information – Address standardSANS1883
Address = StreetAddress | BuildingAddress |IntersectionAddress | …
StreetAddress = StreetAddressPart, Locality
StreetAddressPart = [CompleteStreetNumber | StreetNumberRange], CompleteStreetName
Locality = PlaceName, [TownName],[MunicipalityName], [Province],[SAPOPostcode], [Country] | [CountryCode]
30
Planning: Address data models
Geographic Information – Rural and urban addressingAS/NZS 4819:2003
An urban address includes, in order, the following:∙ Sub-dwelling (flat/unit) number or identifier∙ Level number of sub-dwelling∙ Private road name (if applicable)∙ Utility name (if applicable)∙ Address site name (if applicable)∙ Single urban address number or urban address number range∙ Road name∙ Locality∙ State/territory∙ Postcode (optional)∙ Country
31
Planning: Address data models
Organization for the Advancement of Structured Information Standards (OASIS)∙ www.oasis-open.org ∙ Members
∙ Over 5,000 Members from 100+ countries of OASIS
∙ Software vendors, industry organizations, governments, universities and research centers, individuals
∙ Co-operation with other standards bodies∙ Best known for web services, e-business,
security and document format standards∙ Open and royalty-free standards
32
Planning: Address data models
OASIS Customer Quality Information TC∙ http://www.oasis-open.org/committees/ciq ∙ Chairman: Ram Kumar, Mastersoft, Australia∙ XML Specifications ∙ for defining, representing, interoperating and
managing party information ∙ name, address, party specific information
including party relationships∙ open, vendor neutral, industry and application
independent, ∙ "Global" (international)∙ Extensible Address Language (xAL) to define a
party’s address(es)
33
Planning: Address data models
Par ty Rel ated
Data
Data Quality (Data Parsing, Data Matching / de-duping, data standardization, data migration, data validation, data enhancement)
Data Quality (Data Parsing, Data Matching / de-duping, data standardization, data migration, data validation, data enhancement)
Customer Segmentation
Customer Segmentation
Party Identification/ Recognition
Party Identification/ Recognition
Postal ServicesPostal Services
Customer Profiling
Customer Profiling
Customer Relationships
Customer Relationships
Customer ViewsCustomer Views e-business/ e-commercee-business/ e-commerce
Other Specific Applications (e.g.
Health, Tax, manufacturing, retail, finance, automotive,
justice, etc)
Other Specific Applications (e.g.
Health, Tax, manufacturing, retail, finance, automotive,
justice, etc)
Customer Analytics, Interaction ChannelsCustomer Analytics, Interaction Channels
34
Planning: Address data models
cd xNAL
xNAL
Record Postal label
Address
+ Address
+ Address
+ AdministrativeArea
+ Country
+ FreeTextAddress
+ Locality
+ Location Coordinates
+ PostalDeliveryPoint
+ Postcode
+ PostOffice
+ Premises
+ RuralDelivery
+ Thoroughfare
(from CIQ Data Model)
Addressee
XOR
Name
- Free Text Name Line
+ Organisation Name
+ PartyName
+ PartyNameType
+ Person Name
+ Subdivision
(from CIQ Data Model)
1..*1..*
1..*
xNAL (xNL + xAL) Model
35
Planning: Address data modelscd Address
«type»Address
- Type: - Usage: - Date Valid From: - Date Valid To: - Postal ID:
FreeTextAddress
Country
- Name: - Code:
Administrativ eArea
- Name: - Type: Locality
- Name: - Type:
Thoroughfare
- Name: - Number: - Type:
Premises
- Name: - Number: - Type:
Postcode
- Identifier:
RuralDeliv ery
- Identifier:
PostalDeliveryPoint
- Identifier: PostOffice
- Identifier:
Location Coordinates
- Identifiers:
«hierarchy»
«hierarchy»
«hierarchy»
0..1
0..1
0..1
0..1
0..1
0..1
0..1
0..1
0..10..1
0..1
xAL Model
36
Planning: Address data models
∙ Customer’s perception and preferences∙ 14 Castle Pine Crescent (English)∙ 14 Castle Pine Singel (Afrikaans)
∙ 477 Chopin Street, Glenstantia (Post Office)∙ 477 Chopin Street, Constantia Park (Surveyed)
∙ 17 Glenvista Street, Woodhill (colloquial)∙ 17 Glenvista Street (erf 672), Pretoriuspark Ext
8 (registered at the deeds office)
37
Managing customer data spatially
∙ Why manage customer data spatially?∙ Spatially enabling customer data
∙ Planning∙ Spatial Information Strategy∙ Customer address data model∙ Master address database
∙ Implementation∙ Integrate the address data model∙ Transform customers into spatial customers∙ Coping with uncertainty
∙ Operation∙ The address data life cycle
∙ Using spatial customer data
38
Planning: Master address database
∙ Source: official vs unofficial∙ Maintenance cycle∙ Coverage∙ Data model∙ Level of detail
∙ Address∙ Address Range∙ Street∙ Suburb∙ Postcode and/or post office∙ Region ∙ Country
39
Planning: Master address database
Cadastral Addresses∙ Based on cadastral boundaries∙ Street numbers sourced from relevant official bodies
∙ Link street address to property information∙ owner, price, bond information, etc.
∙ Accommodates for anomalies (panhandle, skip numbers)∙ Address verification, routing, deliveries, customer profiles
2 4 8 10
12A
12B16
GORDON STREET
40
Planning: Master address database
Address Range ∙ Street numbers surveyed at street corners∙ Street numbers evenly allocated in between
∙ Includes street numbers that do not exist∙ Cannot link the street address to property information∙ Routing, deliveries, customer profiles∙ Not good enough for address verification
2 4 8 106GORDON STREET
12 14 16
GORDON STREET2 16
41
Planning: Master address database
Street
W aterkloof R idge
W aterkloof
Lynnw ood G len
Menlo P ark
W aterkloof G len
New lands
W aterkloof He ights
Hartebeestpoort 362- J r
MenlynAshlea Gardens
W aterkloof P ark
Alphen P ark
Maroe lana
Lynnw ood P arkHaze lw ood
De Beers
RA
YM
ON
D S
TR
EE
T
N1
NA
TIO
NA
L F
RE
EW
AY
GARSFONTEIN (M30) AVENUE
DELPHINUS S
TREET
ERIDANUS S
TREET GE
N L
OU
IS B
OTH
A (M
33) D
RIV
E
TAU
RU
S AVEN
UE
JEREMY STREET
KO
RA
NN
AB
ER
G R
OA
D
GRUS STREET
GARSTFONTEIN (M30) ROAD
MALDON ROAD
MARIAN ROAD
ALBERT ADAMSON STREET
HIGH STREET
TW
EN
TY
FIR
ST
ST
RE
ET
SE
LD
ER
Y A
VE
NU
E
BO
GE
Y S
TR
EE
T
BANKET ROAD
GARY AVENUE
TUCKER AVENUE
RO
AD
SADIE STREET
SNOWDROP AVENUE
LONG STREET
SIDNEY R
OADT
WE
NT
Y T
HIR
D S
TR
EE
T
TW
EN
TY
SE
CO
ND
ST
RE
ET
EIG
HT
EE
NT
H S
TR
EE
T
TW
EN
TY
FO
UR
TH
ST
RE
ET
TW
EN
TY
FIF
TH
ST
RE
ET
KAFUE STREET
JULIUS JEPPE STREET
ILKEY ROAD
TREVOR GETHING STREET
NUTMEG AVENUE
MARIGOLD AVENUE
N1
ST
RE
ET
INGERSOL ROAD
SPROKIE AVENUE
ANTHONY STREET
INNES ROAD
COGHILL R
OAD
LANDI STREETGRASKOP ROAD
DUNHILL STREET
JACQUELINE DRIVE
FOR
BES R
EEF RO
AD
OLIVIER STREET
NICOLSON STREET
KARIBA STREETALBERT ROAD
CLARK STREET
EDW ARD STREET
MILNER STREET
GOLF STREET
MAIN STREET
TEGAN STREET
OUTENIQUA AVENUE
RINA VAN ZYL STREET
ROSLYN AVENUE
PALM
PLA
CE
ST
RE
ET
IDOL ROAD
JOHN SCOTT STREETD
EL
PH
I ST
RE
ET
LOIS
AVEN
UE
ADINA ROAD
ELVERAM STREET
JAS
ON
RO
AD
LUNAR AVENUE JES
SIE
CO
LL
INS
ST
RE
ET
GLENW OOD ROAD
ANTON VAN WOUW
STREET
HE
LO
MA
ST
RE
ET
SID
NE
Y S
TR
EE
T
SERENE STREET
THE KOPPIE ROAD
TW
EN
TY
SIX
TH
ST
RE
ET
ARIES S
TREET
DELY ROAD
CHARLES (M11) STREET
ESSENHOUT STREET
CO
RO
BA
Y A
VE
NU
E
PETRONELLA STREET
KOELMAN ROAD
BUTTON PLACE
KA
ST
EE
L R
OA
D
EU
GE
NE
BR
AS
LE
R S
TR
EE
T
MONTY STREET
GLEN MANOR AVENUE
COPSE LANE
W AY
COETZEE STREET
TEGAN
WIN
DSOR ROAD
MA
TR
OO
SB
ER
G R
OA
D
FALDA ROAD
VICTORIA STREET
0 .2 .4 .6
Kilometers
LegendSuburbStreetHighwayMain Road
42
Planning: Master address database
Suburb or Region
43
Planning: Master address database
Postcode and/or post office
44
Planning: Master address database
∙ Mapping to customer address data model∙ Plan for the future
∙ Master address database independent∙ Increasing levels of detail∙ Accessibility by all departments∙ Tools
∙ Knowledge Management∙ What address information is available?∙ How do I access the address information?∙ What can I do with the address information?∙ What tools are available?∙ How is the address captured?
45
Managing customer data spatially
∙ Why manage customer data spatially?∙ Spatially enabling customer data
∙ Planning∙ Spatial Information Strategy∙ Customer address data model∙ Master address database
∙ Implementation∙ Integrate the address data model∙ Transform customers into spatial customers∙ Coping with uncertainty
∙ Operation∙ The address data life cycle
∙ Using spatial customer data
46
Implementation: Integrate data model
∙ Address is not an attribute of the customer!∙ Link an address entity/object to the customer
Customer ID 198374
Name Mr Smith
AddressLine1 14 Collins Street
AddressLine2 Hatfield
AddressLine3 South Africa
Postcode 0083
47
Implementation: Integrate data model
tblAddress
PK pkAddressID
tblCustomer
PK pkCustomerID
FK1 pkAddressID
tblBranch
PK pkBranchID
FK1 pkAddressID
tblATM
PK pkATMID
FK1 pkAddressID
tblSupplier
PK pkSupplierID
FK1 pkAddressID
tblOutlet
PK pkOutletID
FK1 pkAddressID
48
Implementation: Integrate data model
Source: GINIE project
49
It is a capital mistaketo theorize
before one has data.
Sir Arthur Conan Doyle, “A Scandal in Bohemia”,
The Adventures of Sherlock Holmens 1891
50
Managing customer data spatially
∙ Why manage customer data spatially?∙ Spatially enabling customer data
∙ Planning∙ Spatial Information Strategy∙ Customer address data model∙ Master address database
∙ Implementation∙ Integrate the address data model∙ Transform customers into spatial customers∙ Coping with uncertainty
∙ Operation∙ The address data life cycle
∙ Using spatial customer data
51
Implementation: Transform customers
∙ Start with bulk transformation∙ Refine addresses further∙ Limit manual intervention∙ Decide on thresholds∙ Store the linked ID + the original address!∙ Call centre involvement
∙ Cost is a factor∙ Call centre training
∙ Whenever contact is made∙ Update customers who are in contact
52
Implementation: Transform customers
Refinement Process
Original Customer Addresses
etc.
100% Correct Addresses
80% Correct Addresses
75% Correct Addresses
<100% Correct Addresses
<80% Correct Addresses
53
Implementation: Transform customers
ThresholdsOriginal customer address % Master database
2340Sekanama Street Albarnie Pretoria 84.0 7497
SELALA STREET NALEDI PRETORIA
Talitha STREET Derdepoort Pretoria 84.1 160
TSAMMA STREET DOORNPOORT PRETORIA
Atterbury ROAD Centurion Centurion 84.1 160
AMCOR ROAD
CENTURION CENTRAL CENTURION
4
Cecile ROAD Doringkloof Centurion 84.1 163
CECILE STREET DORINGKLOOF CENTURION
54
Implementation: Transform customers
Pitfalls: 100 Rubida Street, Die Wilgers
55
Implementation: Transform customers
Pitfalls:2 Protea Road,Sandown R
IVO
NIA
RO
AD
GRAYSTO
N DRIVE
SOUTH ROAD
KATHERINE STREET
WEST STREET
PR
OT
EA
RO
AD
PR
OTE
A P
LAC
E
Sandown
Chislehurston
56
Managing customer data spatially
∙ Why manage customer data spatially?∙ Spatially enabling customer data
∙ Planning∙ Spatial Information Strategy∙ Customer address data model∙ Master address database
∙ Implementation∙ Integrate the address data model∙ Transform customers into spatial customers∙ Coping with uncertainty
∙ Operation∙ The address data life cycle
∙ Using spatial customer data
57
Implementation: Coping with uncertainty
∙ Flag uncertain records∙ Type of uncertainty∙ As much information as possible∙ Contact details∙ Status history
∙ Uncertainty resolution∙ Pick ‘n Pay HomeShopping: next day∙ eBucks: next week∙ Invoices: end of the month
∙ Business value∙ Evaluate the cost benefit∙ Does improved accuracy add to customer service?∙ Does improved quality add to customer service?
58
Implementation: Coping with uncertainty
59
Implementation: Coping with uncertainty
5
60
Implementation: Coping with uncertainty
1A
61
Managing customer data spatially
∙ Why manage customer data spatially?∙ Spatially enabling customer data
∙ Planning∙ Spatial Information Strategy∙ Customer address data model∙ Master address database
∙ Implementation∙ Integrate the address data model∙ Transform customers into spatial customers∙ Coping with uncertainty
∙ Operation∙ The address data life cycle
∙ Using spatial customer data
62
Cleaning
Operation: The address data life cycle
Capturing
Matching & Verification
Delivery & Analysis
63
Cleaning
Capturing
Matching & Verification
Delivery & Analysis
Operation: The address data life cycle
PO Box
Private Bag 15
Postnet
Building
StrNo
Str Name
Suburb Die Wilgers
City Pretoria
Code 0041
Province Gauteng
Type PrivateBag
64
Operation: The address data life cycle
AddressCapturing
AddressCleaning
AddressGeocoding &Verification
AddressDelivery &Analysis
65
Get it right the first time∙ Search facility∙ Consistent address capturing∙ Capture verified/valid addresses
∙ Add coordinate while capturing∙ Comply to postal delivery requirements
while capturing
∙ List ∙ old & new names∙ language alternatives
AddressCapturing
AddressCleaning
AddressGeocoding &Verification
AddressDelivery &Analysis
66
AddressCapturing
AddressCleaning
AddressGeocoding &Verification
AddressDelivery &Analysis
67
Get it right the first time∙ Address data model∙ Automate as much as possible∙ Check for alternative, old & new
names∙ Complete partial addresses (e.g.
province)∙ Split address types
AddressCapturing
AddressCleaning
AddressGeocoding &Verification
AddressDelivery &Analysis
68
AddressCapturing
AddressCleaning
AddressGeocoding &Verification
AddressDelivery &Analysis
Line 1 Celtis Plaza
Line 2 1085 Schoeman St
Suburb Hatfield
City Pretoria
Code 0083
POBox
Private Bag
Postnet
Building Celtis Plaza
StrNo 1085
Str Name Schoeman Street
Suburb Hatfield
City Pretoria
Code 0083
Province Gauteng
Type Building
69
AddressCapturing
AddressCleaning
AddressGeocoding &Verification
AddressDelivery &Analysis
Line 1
Line 2 P/Box 14134
Suburb
City Hatfield
Code 0028
PO Box 14134
Private Bag
Postnet
Building
StrNo
Str Name
Suburb Hatfield
City Pretoria
Code 0028
Province Gauteng
Type POBox
70
Get it right the first time∙ Automate as much as possible∙ Accuracy required?∙ Coordinate reference system to be
used∙ Use as many datasets as possible
AddressCapturing
AddressCleaning
AddressGeocoding &Verification
AddressDelivery &Analysis
71
AddressCapturing
AddressCleaning
AddressGeocoding &Verification
AddressDelivery &Analysis
Address verificationPOBox
Private Bag
Postnet
Building
StrNo 101
Str Name Koljander Avenue
Suburb Newlands
City Pretoria
Code
Province Gauteng
Type Street
28.273632-
25.792344
72
AddressCapturing
AddressCleaning
AddressGeocoding &Verification
AddressDelivery &Analysis
Deliveries
73
AddressCapturing
AddressCleaning
AddressGeocoding &Verification
AddressDelivery &Analysis
Suburb or RegionPOBox
Private Bag
Postnet
Building
StrNo 101
Str Name Koljander Avenue
Suburb Newlands
City Pretoria
Code
Province Gauteng
Type Street
28.270885-
25.790764
74
Get it right the first time∙ Understand source of addresses∙ Understand business challenges∙ Overlay with other datasets:
∙ Other businesses, competitors∙ Public transport & road network∙ Demographics: Census, LSM, etc.
AddressCapturing
AddressCleaning
AddressGeocoding &Verification
AddressDelivery &Analysis
75
Managing customer data spatially
∙ Why manage customer data spatially?∙ Spatially enabling customer data
∙ Planning∙ Spatial Information Strategy∙ Customer address data model∙ Master address database
∙ Implementation∙ Integrate the address data model∙ Transform customers into spatial customers∙ Coping with uncertainty
∙ Operation∙ The address data life cycle
∙ Using spatial customer data
76
Effective information management must begin by thinking about how people use information –
not with how people use machines.
Thomas Davenport, Harvard Business Review,
1994
77
Address data in South Africa
Street Address:
9 Glenvista Street
Woodhill
Pretoria
(City of Tshwane)
Gauteng
PO Box Address:
PO Box 153
Woodhill
(Kromdraai)
0081
Postal Street Address:
9 Glenvista Street
Woodhill
(Kromdraai)
0081
Deeds Office ERF Description:
Proclaimed Town: Pretorius Park Ext 8
Erf: 676,0
Deeds Office: Pretoria (T)
Surveyor General ERF Description:
Minor Region: PRETORIUS PARK EXT 8
Major Region: JR
Erf: 676
Portion: 0
SG Code: T0JR02050000067600000Building:
Glen Hills No 6
Glenvista Street
Woodhill
Pretoria
Gauteng
78
Using spatial customer data
∙ Delivery∙ Mail∙ Geo-marketing∙ Outlet Planning
79
DeliveryPick ‘n Pay HomeShopping
80
Delivery
∙ Pick ‘n Pay∙ Largest retailer in South Africa∙ Groceries, toiletries, clothing, electrical
appliances, and more∙ Started an Internet shopping company in 2002∙ www.picknpay.co.za
∙ Challenge∙ Integration with existing online shopping site∙ Integration with new logistics software for
deliveries∙ Not-found addresses: 72 hour turnaround time∙ Conversion of existing customers∙ Client provides logistics software to Pick ‘n Pay
81
82
83
84
0 6 12 18
Kilometers
LegendMunicipalitySuburbHighwayMain RoadSilver Lakes
City of Tshwane Metropolitan Municipality
Moretele Local Municipality
Nokeng tsa TaemaneLocal Municipality
City of Johannesburg Metropolitan Municipality
Local Municipalityof Madibeng
KungwiniLocal Municipality
85
Delivery
Cambio process
86
Delivery
Reflection∙ Working with the public…∙ Customer’s perception vs master
database∙ Do not rely on the address ID only∙ Coping with uncertainty
∙ Simplify the process∙ Not-found customers∙ Customer notifications∙ Cost∙ Training
87
Customer’s perception…
88
Customer’s perception…
89
MaileBucks
90
∙ eBucks∙ Rewards program∙ eBucks are earned for shopping and paying
bills ∙ No membership fee (free)∙ Ten eBucks equals one Rand: eB10 = R1 ∙ www.ebucks.com
91
∙ Challenge∙ Integrate an address capturing interface into
existing FNB Online and eBucks website∙ Object-oriented database without SQL
interface∙ UNIX environment∙ No changes to the address data model allowed∙ Call centre training∙ Client in Information Management
92
93
94
95
∙ Reflection∙ Many address types (result is free format)∙ Building names (work address)∙ Process refinement
∙ which addresses are really important?∙ Website integration
∙ Developer training∙ IT personnel – high turnover
∙ Moving target∙ Initial data cleaning∙ Then door-to-door delivery of “Welcome package”∙ Then postal rebates
∙ “Trip into space” - competition
96
Geo-marketingMultiChoice
97
Geo-marketing
∙ MultiChoice∙ Entertainment Television (mainly satellite)∙ DStv, DStv Indian and DStv Portuguesa ∙ Contract channels from various broadcasters,
sell them to the public (subscribers)∙ More than 1.3 million subscribers∙ Series Channel, Movies, History, National
Geographic, Discovery, Sport, CNN, BBC, Cartoon Network, Boomerang, M-TV, etc.
∙ Part of the MIH group (Naspers)∙ Africa, Mediterranean & Asia (50 countries)∙ Internet & television subscribers
98
Geo-marketing
∙ Challenge∙ Annual study to find
∙ ‘gaps’ in the MultiChoice footprint∙ areas that should be targeted with marketing
campaigns to get subscribers∙ Client in Agency Management
99
Geo-marketing
Woodhill
Moreletapark
WOODHILL
OLYMPUS AH
MOOIKLOOF
GarsfonteinOlympus AH
Mooikloof
OPEN SPACE
GARSFONTEIN
ZWAVELPOORT
MORELETA PARK
0 .4 .8 1.2
Kilometers
LegendStatistics SALiving Standards (LSM)StreetWoodhill (Statistics SA)Woodhill (LSM)
100
Geo-marketing
Background: market potential Dots: market penetration
101
Geo-marketing
∙ Reflection∙ Addresses had to be structured, cleaned and
verified every year∙ Slow turnaround∙ Address capturing process is now being updated
and integrated (faster results)
∙ Aligning demographical & customer address data
102
Outlet planningDaily Sun
103
Outlet planning
Northern Cape
Western Cape
Eastern Cape
Free State KwaZulu-Natal
Limpopo
North W est
Mpumalanga
Gauteng
∙ Daily Sun∙ Biggest daily newspaper in South Africa∙ Target market
∙ predominantly black∙ English literate ∙ Minimum high school education∙ working - the economic core of South Africa
∙ 400 000+ sales in Gauteng,Limpopo, Mpumalanga,Northwest Province
∙ Also KwaZulu-Natal, Free State and Eastern Cape
104
Outlet planning
∙ Challenges∙ Identify the gaps in the Daily Sun footprint∙ Compare street to outlet sales∙ Compare sales volumes to e.g. traffic data∙ Client in Distribution Management
105
Outlet planning
106
Outlet planning
∙ Reflection∙ Outlets in rural areas with descriptive
addresses∙ Outlets are moving around∙ Map reading skills∙ Address capturing process now being
integrated
107
Acknowledgements
AfriGIS for use of their data and case studies
The Computer Science department at the University of Pretoria for their support
108
More interesting reading…
• Address Markup Languages, http://xml.coverpages.org/namesAndAddresses.html
• AfriGIS, www.afrigis.co.za• CIO Insight, www.cioinsight.com • The Data Management Organization, www.dama.org • Geographic Information Network in Europe (GINIE),
www.ec-gis.org/ginie/ • Ireland’s GeoDirectory, www.geodirectory.ie • OASIS, www.oasis-open.org • PSMA Australia, www.psma.com.au• Richard T.Watson, Data Management Databases and
Organizations, John Wiley & Sons, Inc, Fifth Edition, 2006
• University of Pretoria, www.cs.up.ac.za