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

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Presentation on theme: "1 Managing customer data spatially Fifth Annual GIS 2007 (Melbourne) Serena Coetzee University of Pretoria 2 May 2007."— Presentation transcript:

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

2 2

3 3 South Africa & Tshwane Afrikaans, English IsiZulu IsiXhosa SiSwati Ndebele Southern Sotho Northern Sotho Tsonga SeTswana Venda Pretoria (executive) Bloemfontein (judicial) Cape Town (legislative)

4 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 5 University of Pretoria (Tukkies) ∙1906 Transvaal University College ∙University of Pretoria ∙ residential students ∙ undergraduates ∙ 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 6 History and Research Interests ReGIS, Autodesk World Spatial Datasets, PropertySPI GI Standards NAD on the grid Can we establish a virtual NAD for South Africa in the form of a data grid? + + =

7 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 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 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 Business Intelligence Tops the Strategic Technology List Source: 1 2

10 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:

11 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

12 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

13 13 Why manage customer data spatially? Source:

14 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:

15 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 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 17 Planning Challenges ∙Buy-in on executive level ∙Continuous long term process ∙Customer’s perception of what his/her address should be

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

19 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 20 Planning Spatial Information Strategy Infrastructure (hardware & networks) Spatial reference data Software ContractsProcess Business data $$ People Reference Data

21 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&technology Address capturing, for delivery Spatial Analysis Address Structurin g & Cleaning

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

23 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 24 Planning Purpose: Address Verification Koljander Avenue Newlands Pretoria Gauteng

25 25 Planning Purpose: Deliveries

26 26 Planning Purpose: Customer profiling Koljander Avenue Newlands Pretoria Gauteng 45 Nutmeg Avenue Newlands Pretoria Gauteng

27 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 28 Cow of many - well milked and badly fed Spanish proverb

29 29 Planning: Address data models Geographic Information – Address standard SANS1883 Address = StreetAddress | BuildingAddress | IntersectionAddress | … StreetAddress = StreetAddressPart, Locality StreetAddressPart = [CompleteStreetNumber | StreetNumberRange], CompleteStreetName Locality = PlaceName, [TownName], [MunicipalityName], [Province], [SAPOPostcode], [Country] | [CountryCode]

30 30 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

31 31 Planning: Address data models Organization for the Advancement of Structured Information Standards (OASIS) ∙www.oasis-open.orgwww.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 32 Planning: Address data models OASIS Customer Quality Information TC ∙http://www.oasis-open.org/committees/ciqhttp://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 33 Planning: Address data models

34 34 Planning: Address data models xNAL (xNL + xAL) Model

35 35 Planning: Address data models xAL Model

36 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 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 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 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 A 12B 16 GORDON STREET

40 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 GORDON STREET GORDON STREET 216

41 41 Planning: Master address database Street

42 42 Planning: Master address database Suburb or Region

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

44 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 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 46 Implementation: Integrate data model ∙Address is not an attribute of the customer! ∙Link an address entity/object to the customer Customer ID NameMr Smith AddressLine114 Collins Street AddressLine2Hatfield AddressLine3South Africa Postcode0083

47 47 Implementation: Integrate data model

48 48 Implementation: Integrate data model Source: GINIE project

49 49 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

50 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 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 52 Implementation: Transform customers Refinement Process etc.

53 53 Implementation: Transform customers Thresholds Original customer address%Master database 2340 Sekanama StreetAlbarniePretoria SELALA STREETNALEDIPRETORIA Talitha STREETDerdepoortPretoria TSAMMA STREETDOORNPOORTPRETORIA Atterbury ROADCenturion AMCOR ROAD CENTURION CENTRALCENTURION 4 Cecile ROADDoringkloofCenturion CECILE STREETDORINGKLOOFCENTURION

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

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

56 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 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 58 Implementation: Coping with uncertainty

59 59 Implementation: Coping with uncertainty 5

60 60 Implementation: Coping with uncertainty 1A

61 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 62 Operation: The address data life cycle

63 63 Operation: The address data life cycle PO Box Private Bag15 Postnet Building StrNo Str Name SuburbDie Wilgers CityPretoria Code0041 ProvinceGauteng TypePrivateBag

64 64 Operation: The address data life cycle

65 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

66 66

67 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

68 68 Line 1Celtis Plaza Line Schoeman St SuburbHatfield CityPretoria Code0083 POBox Private Bag Postnet BuildingCeltis Plaza StrNo1085 Str NameSchoeman Street SuburbHatfield CityPretoria Code0083 ProvinceGauteng TypeBuilding

69 69 Line 1 Line 2P/Box Suburb CityHatfield Code0028 PO Box14134 Private Bag Postnet Building StrNo Str Name SuburbHatfield CityPretoria Code0028 ProvinceGauteng TypePOBox

70 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

71 71 Address verification POBox Private Bag Postnet Building StrNo101 Str NameKoljander Avenue SuburbNewlands CityPretoria Code ProvinceGauteng TypeStreet

72 72 Deliveries

73 73 Suburb or Region POBox Private Bag Postnet Building StrNo101 Str NameKoljander Avenue SuburbNewlands CityPretoria Code ProvinceGauteng TypeStreet

74 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.

75 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 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 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: T0JR Building: Glen Hills No 6 Glenvista Street Woodhill Pretoria Gauteng

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

79 79 Delivery Pick ‘n Pay HomeShopping

80 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.zawww.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 81

82 82

83 83

84 84

85 85 Delivery Cambio process

86 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 87 Customer’s perception…

88 88 Customer’s perception…

89 89 Mail eBucks

90 90 Mail ∙eBucks ∙Rewards program ∙eBucks are earned for shopping and paying bills ∙No membership fee (free) ∙Ten eBucks equals one Rand: eB10 = R1 ∙www.ebucks.comwww.ebucks.com

91 91 Mail ∙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 92

93 93

94 94

95 95 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

96 96 Geo-marketing MultiChoice

97 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 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 99 Geo-marketing

100 100 Geo-marketing Background: market potentialDots: market penetration

101 101 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

102 102 Outlet planning Daily Sun

103 103 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 ∙ sales in Gauteng, Limpopo, Mpumalanga, Northwest Province ∙Also KwaZulu-Natal, Free State and Eastern Cape

104 104 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

105 105 Outlet planning

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

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

108 108 More interesting reading… Address Markup Languages, AfriGIS, CIO Insight, The Data Management Organization, Geographic Information Network in Europe (GINIE), Ireland’s GeoDirectory, OASIS, PSMA Australia, Richard T.Watson, Data Management Databases and Organizations, John Wiley & Sons, Inc, Fifth Edition, 2006 University of Pretoria,

109 109 Serena Coetzee University of Pretoria


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