Managing customer data spatially Fifth Annual GIS 2007

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Managing customer data spatially Fifth Annual GIS 2007 (Melbourne) Serena Coetzee University of Pretoria Managing customer data spatially Fifth Annual GIS 2007 (Melbourne) Serena Coetzee University of Pretoria 2 May 2007 1

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South Africa & Tshwane Afrikaans, English Isi. Zulu Isi. Xhosa Si. Swati Ndebele Southern South Africa & Tshwane Afrikaans, English Isi. Zulu Isi. Xhosa Si. Swati Ndebele Southern Sotho Northern Sotho Tsonga Se. Tswana Venda Pretoria (executive) Bloemfontein (judicial) Cape Town (legislative) 3

South Africa ∙ 45 million people ∙ 9 provinces ∙ 262 municipalities ∙ 6 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 4

University of Pretoria (Tukkies) ∙ 1906 Transvaal University College ∙ University of Pretoria ∙ 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 5

History and Research Interests Re. GIS, Autodesk World Spatial Datasets, Property. SPI NAD on History and Research Interests Re. GIS, Autodesk World Spatial Datasets, Property. SPI NAD on the grid GI Standards Can we establish a virtual NAD for South Africa in the form of a data grid? + + = 6

Overview Managing customer data spatially ∙ Why manage customer data spatially? ∙ Spatially enabling 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 7

Managing customer data spatially ∙ Why manage customer data spatially? ∙ Spatially enabling customer 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

Why manage customer data spatially? The Future of I. T. : What's on Tap Why manage customer data spatially? The Future of I. T. : What's on Tap for 2007 and Beyond 1 1. Customer Service Surges as a Top Priority for 2007 2 2. Business Intelligence Tops the Strategic Technology List Source: www. cioinsight. com 9

Why manage customer data spatially? The 30 Most Important IT Trends for 2007 Technology Why manage customer data spatially? The 30 Most Important IT Trends for 2007 Technology 1 • 2 • • 3 • 4 • 5 • 6 7 • 8 • 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 10

Why manage customer data spatially? “More than 25% of critical data used in large 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 11

Why manage customer data spatially? “Through 2007, more than 50% of data-warehousing projects will 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 12

Why manage customer data spatially? Source: www. gwsae. org 13 Why manage customer data spatially? Source: www. gwsae. org 13

Why manage customer data spatially? The insurance industry is ready for the corporate wide 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 14

Why manage customer data spatially? ∙ Data quality ∙ Address verification ∙ Return to 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. ) 15

Managing customer data spatially ∙ Why manage customer data spatially? ∙ Spatially enabling customer 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 16

Planning Challenges ∙ Buy-in on executive level ∙ Continuous long term process ∙ Customer’s Planning Challenges ∙ Buy-in on executive level ∙ Continuous long term process ∙ Customer’s perception of what his/her address should be 17

Planning One of our strongest weapons is dialogue. Nelson Mandela 18 Planning One of our strongest weapons is dialogue. Nelson Mandela 18

Planning 1 ∙ Understand articulate the benefits of spatial customer data 2 ∙ Convince Planning 1 ∙ Understand articulate the benefits of spatial customer data 2 ∙ Convince non-technical executives about the benefits of spatial address data 3 ∙ Associate the benefits to an identified risk or business event Ready to start… 19

Planning Spatial Information Strategy Contracts Software $$ Business data $$ Process People Reference Data Planning Spatial Information Strategy Contracts Software $$ Business data $$ Process People Reference Data Spatial reference data Infrastructure (hardware & networks) 20

Planning Address Structuring & Cleaning Remembering the past (databases and data warehouse) Transactions Handling Planning Address Structuring & Cleaning Remembering the past (databases and data warehouse) Transactions Handling the present (TPS) Address capturing, for delivery People & technology Data Spatial Analysis Preparing for the future (BI, data mining, DSS, EIS, MIS, OLAP) New business systems Source: Watson 21

Planning Data Management Functions Source: DM Functional Framework by DAMA 22 Planning Data Management Functions Source: DM Functional Framework by DAMA 22

Planning ∙ Who is responsible for customer address? ∙ ∙ Information (CIO) Analytics (GIS) 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 23

Planning Purpose: Address Verification 101 Koljander Avenue Newlands Pretoria Gauteng 28. 273632 -25. 792344 Planning Purpose: Address Verification 101 Koljander Avenue Newlands Pretoria Gauteng 28. 273632 -25. 792344 24

Planning Purpose: Deliveries 25 Planning Purpose: Deliveries 25

Planning Purpose: Customer profiling 101 Koljander Avenue Newlands Pretoria Gauteng 45 Nutmeg Avenue Newlands Planning Purpose: Customer profiling 101 Koljander Avenue Newlands Pretoria Gauteng 45 Nutmeg Avenue Newlands Pretoria Gauteng 28. 270885 -25. 790764 26

Managing customer data spatially ∙ Why manage customer data spatially? ∙ Spatially enabling customer 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 27

Cow of many - well milked and badly fed Spanish proverb 28 Cow of many - well milked and badly fed Spanish proverb 28

Planning: Address data models Geographic Information – Address standard SANS 1883 Address = Street. Planning: Address data models Geographic Information – Address standard SANS 1883 Address = Street. Address | Building. Address | Intersection. Address | … Street. Address = Street. Address. Part, Locality Street. Address. Part = [Complete. Street. Number | Street. Number. Range], Complete. Street. Name Locality = Place. Name, [Town. Name], [Municipality. Name], [Province], [SAPOPostcode], [Country] | [Country. Code] 29

Planning: Address data models Geographic Information – Rural and urban addressing AS/NZS 4819: 2003 Planning: Address data models Geographic Information – Rural and urban addressing AS/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 30

Planning: Address data models Organization for the Advancement of Structured Information Standards (OASIS) ∙ 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 31

Planning: Address data models OASIS Customer Quality Information TC ∙ ∙ http: //www. oasis-open. 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 (x. AL) to define a party’s address(es) 32

Planning: Address data models 33 Planning: Address data models 33

Planning: Address data models x. NAL (x. NL + x. AL) Model 34 Planning: Address data models x. NAL (x. NL + x. AL) Model 34

Planning: Address data models x. AL Model 35 Planning: Address data models x. AL Model 35

Planning: Address data models ∙ Customer’s perception and preferences ∙ 14 Castle Pine Crescent 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) 36

Managing customer data spatially ∙ Why manage customer data spatially? ∙ Spatially enabling customer 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 37

Planning: Master address database ∙ ∙ ∙ Source: official vs unofficial Maintenance cycle Coverage Planning: Master address database ∙ ∙ ∙ Source: official vs unofficial Maintenance cycle Coverage Data model Level of detail ∙ ∙ ∙ ∙ Address Range Street Suburb Postcode and/or post office Region Country 38

Planning: Master address database Cadastral Addresses ∙ Based on cadastral boundaries ∙ Street numbers Planning: Master address database Cadastral Addresses ∙ Based on cadastral boundaries ∙ Street numbers sourced from relevant official bodies 2 4 8 10 12 B 16 12 A GORDON STREET ∙ Link street address to property information ∙ owner, price, bond information, etc. ∙ Accommodates for anomalies (panhandle, skip numbers) ∙ Address verification, routing, deliveries, customer profiles 39

Planning: Master address database Address Range ∙ Street numbers surveyed at street corners ∙ Planning: Master address database Address Range ∙ Street numbers surveyed at street corners ∙ Street numbers evenly allocated in between 2 ∙ ∙ 2 4 6 8 10 12 GORDON STREET 14 16 16 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 40

Planning: Master address database Street 41 Planning: Master address database Street 41

Planning: Master address database Suburb or Region 42 Planning: Master address database Suburb or Region 42

Planning: Master address database Postcode and/or post office 43 Planning: Master address database Postcode and/or post office 43

Planning: Master address database ∙ Mapping to customer address data model ∙ Plan for 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? 44

Managing customer data spatially ∙ Why manage customer data spatially? ∙ Spatially enabling customer 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 45

Implementation: Integrate data model Customer ID 198374 Name Mr Smith Address. Line 1 14 Implementation: Integrate data model Customer ID 198374 Name Mr Smith Address. Line 1 14 Collins Street Address. Line 2 Hatfield Address. Line 3 South Africa Postcode 0083 ∙ Address is not an attribute of the customer! ∙ Link an address entity/object to the customer 46

Implementation: Integrate data model 47 Implementation: Integrate data model 47

Implementation: Integrate data model Source: GINIE project 48 Implementation: Integrate data model Source: GINIE project 48

It is a capital mistake to theorize before one has data. Sir Arthur Conan It is a capital mistake to theorize before one has data. Sir Arthur Conan Doyle, “A Scandal in Bohemia”, The Adventures of Sherlock Holmens 1891 49

Managing customer data spatially ∙ Why manage customer data spatially? ∙ Spatially enabling customer 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 50

Implementation: Transform customers ∙ ∙ ∙ Start with bulk transformation Refine addresses further Limit 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 51

Implementation: Transform customers Refinement Process etc. 52 Implementation: Transform customers Refinement Process etc. 52

Implementation: Transform customers Thresholds Original customer address 2340 Sekanama Street Talitha STREET Atterbury ROAD Implementation: Transform customers Thresholds Original customer address 2340 Sekanama Street Talitha STREET Atterbury ROAD 4 Cecile ROAD Albarnie Derdepoort Centurion Doringkloof Pretoria Centurion % 84. 0 84. 1 Master database 7497 SELALA STREET NALEDI PRETORIA 160 TSAMMA STREET DOORNPOORT PRETORIA 160 AMCOR ROAD CENTURION CENTRAL CENTURION 163 CECILE STREET DORINGKLOOF CENTURION 53

Implementation: Transform customers Pitfalls: 100 Rubida Street, Die Wilgers 54 Implementation: Transform customers Pitfalls: 100 Rubida Street, Die Wilgers 54

Implementation: Transform customers Pitfalls: 2 Protea Road, Sandown 55 Implementation: Transform customers Pitfalls: 2 Protea Road, Sandown 55

Managing customer data spatially ∙ Why manage customer data spatially? ∙ Spatially enabling customer 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 56

Implementation: Coping with uncertainty ∙ Flag uncertain records ∙ ∙ Type of uncertainty As Implementation: Coping with uncertainty ∙ Flag uncertain records ∙ ∙ Type of uncertainty As much information as possible Contact details Status history ∙ Uncertainty resolution ∙ Pick ‘n Pay Home. Shopping: next day ∙ e. Bucks: 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? 57

Implementation: Coping with uncertainty 58 Implementation: Coping with uncertainty 58

Implementation: Coping with uncertainty 5 59 Implementation: Coping with uncertainty 5 59

Implementation: Coping with uncertainty 1 A 60 Implementation: Coping with uncertainty 1 A 60

Managing customer data spatially ∙ Why manage customer data spatially? ∙ Spatially enabling customer 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 61

Operation: The address data life cycle 62 Operation: The address data life cycle 62

Operation: The address data life cycle PO Box Private Bag 15 Postnet Building Str. Operation: The address data life cycle PO Box Private Bag 15 Postnet Building Str. No Str Name Suburb Die Wilgers City Pretoria Code 0041 Province Gauteng Type Private. Bag 63

Operation: The address data life cycle 64 Operation: The address data life cycle 64

Get it right the first time ∙ Search facility ∙ Consistent address capturing ∙ 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 65

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Get it right the first time ∙ Address data model ∙ Automate as much 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 67

POBox Line 1 Celtis Plaza Line 2 1085 Schoeman St Suburb Hatfield City Pretoria POBox Line 1 Celtis Plaza Line 2 1085 Schoeman St Suburb Hatfield City Pretoria Code 0083 Private Bag Postnet Building Celtis Plaza Str. No 1085 Str Name Schoeman Street Suburb Hatfield City Pretoria Code 0083 Province Gauteng Type Building 68

PO Box Private Bag Line 1 Line 2 14134 P/Box 14134 Postnet Building Suburb PO Box Private Bag Line 1 Line 2 14134 P/Box 14134 Postnet Building Suburb City Hatfield Code 0028 Str. No Str Name Suburb Hatfield City Pretoria Code 0028 Province Gauteng Type POBox 69

Get it right the first time ∙ Automate as much as possible ∙ Accuracy 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 70

Address verification POBox Private Bag Postnet 28. 273632 -25. 792344 Building Str. No 101 Address verification POBox Private Bag Postnet 28. 273632 -25. 792344 Building Str. No 101 Str Name Koljander Avenue Suburb Newlands City Pretoria Code Province Gauteng Type Street 71

Deliveries 72 Deliveries 72

Suburb or Region POBox Private Bag Postnet Building Str. No 101 Str Name Koljander Suburb or Region POBox Private Bag Postnet Building Str. No 101 Str Name Koljander Avenue Suburb Newlands City Pretoria 28. 270885 -25. 790764 Code Province Gauteng Type Street 73

Get it right the first time ∙ Understand source of addresses ∙ Understand business 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. 74

Managing customer data spatially ∙ Why manage customer data spatially? ∙ Spatially enabling customer 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 75

Effective information management must begin by thinking about how people use information – not Effective information management must begin by thinking about how people use information – not with how people use machines. Thomas Davenport, Harvard Business Review, 1994 76

Address data in South Africa Street Address: 9 Glenvista Street Woodhill Surveyor General ERF Address data in South Africa Street Address: 9 Glenvista Street Woodhill Surveyor General ERF Description: Pretoria Minor Region: PRETORIUS PARK EXT 8 (City of Tshwane) Major Region: JR Gauteng Erf: 676 Portion: 0 SG Code: T 0 JR 02050000067600000 Building: Glen Hills No 6 Glenvista Street Woodhill Pretoria Deeds Office ERF Description: Gauteng Proclaimed Town: Pretorius Park Ext 8 Erf: 676, 0 PO Box Address: Postal Street Address: PO Box 153 9 Glenvista Street Woodhill (Kromdraai) 0081 Deeds Office: Pretoria (T) 77

Using spatial customer data ∙ ∙ Delivery Mail Geo-marketing Outlet Planning 78 Using spatial customer data ∙ ∙ Delivery Mail Geo-marketing Outlet Planning 78

Delivery Pick ‘n Pay Home. Shopping 79 Delivery Pick ‘n Pay Home. Shopping 79

Delivery ∙ Pick ‘n Pay ∙ Largest retailer in South Africa ∙ Groceries, toiletries, 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 80

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Delivery Cambio process 85 Delivery Cambio process 85

Delivery Reflection ∙ Working with the public… ∙ Customer’s perception vs master database ∙ 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 86

Customer’s perception… 87 Customer’s perception… 87

Customer’s perception… 88 Customer’s perception… 88

Mail e. Bucks 89 Mail e. Bucks 89

Mail ∙ e. Bucks ∙ ∙ ∙ Rewards program e. Bucks are earned for Mail ∙ e. Bucks ∙ ∙ ∙ Rewards program e. Bucks are earned for shopping and paying bills No membership fee (free) Ten e. Bucks equals one Rand: e. B 10 = R 1 www. ebucks. com 90

Mail ∙ Challenge ∙ Integrate an address capturing interface into existing FNB Online and Mail ∙ Challenge ∙ Integrate an address capturing interface into existing FNB Online and e. Bucks website ∙ Object-oriented database without SQL interface ∙ UNIX environment ∙ No changes to the address data model allowed ∙ Call centre training ∙ Client in Information Management 91

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Mail ∙ Reflection ∙ Many address types (result is free format) ∙ Building names Mail ∙ 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 95

Geo-marketing Multi. Choice 96 Geo-marketing Multi. Choice 96

Geo-marketing ∙ Multi. Choice ∙ Entertainment Television (mainly satellite) ∙ DStv, DStv Indian and Geo-marketing ∙ Multi. Choice ∙ 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 97

Geo-marketing ∙ Challenge ∙ Annual study to find ∙ ‘gaps’ in the Multi. Choice Geo-marketing ∙ Challenge ∙ Annual study to find ∙ ‘gaps’ in the Multi. Choice footprint ∙ areas that should be targeted with marketing campaigns to get subscribers ∙ Client in Agency Management 98

Geo-marketing 99 Geo-marketing 99

Geo-marketing Background: market potential Dots: market penetration 100 Geo-marketing Background: market potential Dots: market penetration 100

Geo-marketing ∙ Reflection ∙ Addresses had to be structured, cleaned and verified every year 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 101

Outlet planning Daily Sun 102 Outlet planning Daily Sun 102

Outlet planning ∙ Daily Sun ∙ Biggest daily newspaper in South Africa ∙ Target Outlet planning ∙ 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 Kwa. Zulu-Natal, Free State and Eastern Cape 103

Outlet planning ∙ Challenges ∙ ∙ Identify the gaps in the Daily Sun footprint 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 104

Outlet planning 105 Outlet planning 105

Outlet planning ∙ Reflection ∙ ∙ Outlets in rural areas with descriptive addresses Outlets Outlet planning ∙ Reflection ∙ ∙ Outlets in rural areas with descriptive addresses Outlets are moving around Map reading skills Address capturing process now being integrated 106

Acknowledgements Afri. GIS for use of their data and case studies The Computer Science Acknowledgements Afri. GIS for use of their data and case studies The Computer Science department at the University of Pretoria for their support 107

More interesting reading… • Address Markup Languages, http: //xml. coverpages. org/names. And. Addresses. html More interesting reading… • Address Markup Languages, http: //xml. coverpages. org/names. And. Addresses. html • Afri. GIS, 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 Geo. Directory, 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 108

Serena Coetzee University of Pretoria scoetzee@cs. up. ac. za +27 82 464 4294 109 Serena Coetzee University of Pretoria [email protected] up. ac. za +27 82 464 4294 109




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