Using Origins to support research about and engagement with

Australia’s CALD communities

Uses, Applications and Privacy Compliance

Michael DovePrincipal Consultant, OriginsInfo

IntroductionThis PowerPoint slide show highlights a range of potential uses of Origins.

It complements documentation found elsewhere in the OriginsInfo website regarding the privacy compliance of Origins data.

See http://www.originsinfo.com.au/about/privacy-and-ethics/ for our privacy policy. Please contact us directly at info@originsinfo.com.au if you need more information about why Origins data is privacy-compliant.

Additionally, the slide show serves as support for current and potential clients considering the range of potential uses for Origins.

Please note that the Office of the Privacy Commissioner (now the Office of the Australian Information Commissioner) has indicated to OriginsInfo that none of the potential uses of the Origins product is considered to contravene the Privacy Act.

1. Geographically aggregated Tabulation Mapping

2. Profiling within the Origins software product3. Research and insight services4. Unaddressed non-personalised promotions targeted at small areas identified using

Origins5. Non-personalised targeting of addresses identified using Origins6. Targeted prospect lists compiled using modelled multivariate selection criteria7. Lists compiled using Origins as a single selection8. Realtime coding for enhanced customer experience9. Customer segmentation10. Multipurpose use for research, insight, targeted communications and campaign

measurement

Potential Uses of Origins

Geog Area ID A

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HIS

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LIA

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NIA

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2370101 0.0% 16.1% 4.5% 30.7% 3.9% 3.0% 17.9% 0.6% 2.7% 0.0% 0.6% 12.5% 3.6% 3.9%2370102 0.4% 22.2% 6.4% 30.9% 6.8% 3.5% 5.8% 1.6% 5.4% 0.4% 0.2% 4.9% 4.7% 6.8%2370103 0.0% 21.7% 9.1% 26.8% 10.8% 2.6% 6.9% 1.0% 3.9% 0.4% 0.6% 7.3% 4.3% 4.5%2370104 0.0% 14.8% 7.4% 13.9% 13.4% 2.8% 23.1% 0.0% 5.1% 0.5% 0.0% 10.2% 1.9% 6.0%2370105 0.0% 27.4% 14.6% 11.9% 11.0% 3.6% 8.7% 0.7% 4.0% 1.1% 0.7% 4.5% 3.4% 8.5%2370106 0.0% 10.9% 4.3% 39.0% 11.1% 3.2% 5.0% 0.4% 6.1% 0.5% 1.3% 8.2% 3.8% 6.1%2370107 0.0% 17.3% 7.4% 32.3% 9.2% 2.1% 9.5% 0.0% 3.7% 0.0% 0.4% 10.4% 1.9% 5.8%2370108 0.2% 9.7% 6.2% 46.3% 5.4% 3.2% 4.5% 0.0% 2.7% 0.2% 0.0% 4.2% 9.7% 7.4%2370109 0.0% 18.1% 6.6% 43.6% 4.4% 4.7% 5.5% 0.0% 1.1% 1.1% 0.0% 10.1% 1.4% 3.6%2370110 0.0% 23.3% 9.7% 28.7% 6.5% 4.4% 4.9% 0.4% 3.4% 0.8% 1.1% 5.1% 3.8% 8.0%

1 Aggregated dataProfiling areas

DescriptionThis table shows a profile of name origins referenced to geographical areas, such as postcodes, SA1 areas, or similar. For each area, the percentage of names from each name origin group is identified.

UsesA profile such as this helps organisations understand the mix of name origins at detailed geographical levels. Areas can be compared and ranked so that resources are allocated in the most efficient way.

Typical Client ApplicationsThe Department of Health imports a table such as this into its mapping/GIS system to allow it to build up a detailed picture of name origins for any area (eg a health region, a hospital catchment, or a community).

Temporary O Permanent O

Requirement to append to a customer record

DescriptionThis map shows the distribution of different name origins across a geographical area – in this case Melbourne metropolitan area.

UsesA map like this helps organisations understand the city-wide mix of name origins across the urban area. Mapping is an important and effective communication device that is used to justify resource allocation in many different types of organisation.

Typical Client ApplicationsA large department store retailer can modify its product range in certain areas so that it is better aligned to the needs of particular cultural communities.

DescriptionThis map shows the distribution of people with names that originate in Vietnam. It also contains an underlay of areas showing the highest concentration of people with Vietnamese ancestry as indicated in the ABS census data.

UsesThe distribution of people with names of a particular origin provides a more granular view than is possible with census data. It also gives different, but complementary, information compared to the Country of Birth or Ancestry tables provided in the census.

Typical Client ApplicationsA big four bank is seeking to identify ways of improving the staff mix and language skills in its branch network. This information, when overlaid with the distribution of its branches, helps them recruit the right staff for the right locations.

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Requirement to append to a customer record

1 Greater Dandenong: Origins MapDescriptionThis map shows the distribution of different name origins in the City of Greater Dandenong.

UsesA map such as this helps organisations understand the mix of name origins found in local areas. This information is used to guide resource allocation and helps improve provision of government services to ‘hard-to-reach’ communities.

Typical Client ApplicationsThe City of Greater Dandenong can promote its library services to ensure that the service is accessed by all sectors of the community.

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Requirement to append to a customer record

2 Profiling within the Origins softwareCustomer profile (Count) compared with Australia (Base)

DescriptionThis table shows part of a profile of Origins Types compared to a geographical base – in this case Australia. Profiling provides initial diagnostic research on how well people with different name origins are represented in an organisation’s customer list compared with the market from which they are drawn.

UsesProfiling is a valuable way to find out if customers with particular name origins are under or over-represented (Index >100 = over-represented; index <100 = under-represented). Profiling is normally a first step for any public or commercial sector user with a list of customers or users of a particular service.

Typical Client ApplicationsA private health insurer uses profiling to understand how its brand appeals to names of different origin. Among other things, it also looks at the profiles of new members, cancelling members, members holding particular products, and payment methods.

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Requirement to append to a customer record

3 Research to provide behavioural insightPayment preferences: Index values with row and column counts

Group Description AusPost BPAYCredit Card

Manual Receipt Write Off Total

Grand Total 19,118 32,019 19,708 2,951 6,642 80,438 ANGLO-SAXON 92 106 98 96 100 21,377 EAST/SOUTH EAST ASIAN 115 61 104 127 103 13,311 CELTIC 90 108 101 99 99 9,107 SLAVIC 147 84 97 89 94 8,387 WESTERN EUROPEAN 92 103 102 99 99 6,458 ITALIAN 150 93 104 91 91 5,550 MUSLIM 140 66 96 113 122 4,989 GREEK/GREEK CYPRIOT 180 82 103 100 85 3,791 HISPANIC 113 86 92 92 106 2,993 SOUTH ASIAN 59 83 129 150 110 2,818 NORDIC 96 105 97 100 104 689 JEWISH/ARMENIAN 96 100 120 177 96 367 OCEANIAN 79 71 57 56 146 304 AFRICAN 106 75 69 123 108 203 UNCLASSIFIED 128 83 108 124 108 60 NOT FOUND 98 52 96 79 135 34 Grand Total 19,118 32,019 19,708 2,951 6,642 80,438

> 135

120-135

< 80

Thresholds

Base = All Active Customers

DescriptionThis table shows comparative profiles of how customers prefer to pay their bills. The index values in the body of the table provide initial guidance about those preferences (>100 = Over-represented; <100 = under-represented).

UsesDifferent payment methods have different implications for customer engagement, levels of service, cross-sell opportunities, and transaction costs. Understanding variation between different customer groups is a first step in managing towards preferred outcomes.

Typical Client ApplicationsA motoring services organisation has researched payment methods and identified that people with different name origins prefer to pay their bills in different ways. Their objective is to increase the number of members who pay on-line as this represents a lower-cost channel.

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Requirement to append to a customer record

3 Research to provide behavioural insightConsumer satisfaction: Index values with row and column counts

Origins Group No

t A

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TotalANGLO-SAXON 94 92 85 104 3475CELTIC 79 101 98 101 1625WESTERN EUROPEAN 148 49 92 102 513ITALIAN 134 180 122 90 345SLAVIC 74 141 164 84 239EAST/SOUTH EAST ASIAN 44 95 208 74 132GREEK/GREEK CYPRIOT 310 127 149 82 132MUSLIM 217 78 174 76 105NORDIC 83 119 37 113 69HISPANIC - - 166 84 63SOUTH ASIAN - 427 199 70 40JEWISH/ARMENIAN 249 178 133 78 22AFRICAN - 475 295 44 9NOT FOUND - 712 265 44 6OCEANIAN - - 212 80 5UNCLASSIFIED - - 531 - 1Total 116 162 1305 5198 6781

> 135

120-135

< 80

Thresholds

Base = All Consumers

DescriptionThis table shows how responses from different name origins groups can be linked to responses on a customer satisfaction survey. Index values show the relative representation of different name groupings according to four different levels of satisfaction.

UsesHelps organisations understand the cultural dimensions of customer satisfaction.

Typical Client ApplicationsA government department operates a citizen satisfaction survey. Origins provides the department with valuable insight into the cultural dimensions of consumer satisfaction.

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Requirement to append to a customer record

3 Research to provide behavioural insightEmployee diversity

> 135

120-135

< 80

Thresholds

Base = Melbourne Adults

DescriptionThis table shows how name origins can be used to measure the extent of cultural diversity in the workplace. This type of analysis is used to identify how well the employee mix reflects the labour market or, indeed, the customer mix. Index values show the relative representation of different name groupings.

UsesHelps an organisation measure how well it is practising equal opportunity in the workplace.

Typical Client ApplicationsA government department in Melbourne wanted to understand if certain segments of the community were under-represented. This research reveals a strong Anglo-Celtic bias in the current workforce, providing a useful benchmark for measuring future change.

Origins Profile for Government Department

Origins Types

Sorted by Volume of StaffEmployees Employees %

Melbourne Adults % Index

Total 3,585 100 100 100ANGLO-SAXON 1,552 43.291 39.028 111CELTIC 820 22.873 18.053 127ITALIAN 229 6.388 8.679 74GERMAN 135 3.766 3.375 112GREEK 121 3.375 4.561 74EAST ASIAN 107 2.985 4.089 73SOUTH ASIAN 81 2.259 2.812 80CENTRAL EUROPEAN 74 2.064 2.377 87HISPANIC 70 1.953 2.123 92DUTCH 56 1.562 1.581 99FRANCO-BELGIAN 52 1.450 1.394 104SOUTH EAST ASIAN 51 1.423 2.702 53EASTERN EUROPEAN 41 1.144 0.923 124BALKAN 39 1.088 2.050 53ASIAN MUSLIM 33 0.921 1.015 91NORDIC 31 0.865 0.971 89AFRO-ARABIC MUSLIM 23 0.642 1.434 45JEWISH/ARMENIAN 19 0.530 0.616 86TURKISH 18 0.502 0.903 56BALTIC 10 0.279 0.187 150NOT FOUND 7 0.195 0.105 187SOUTH EASTERN EUROPEAN 7 0.195 0.208 94PACIFIC ISLANDS 6 0.167 0.064 263OCEANIAN 2 0.056 0.109 51EAST/WEST AFRICAN 1 0.028 0.123 23CENTRAL/SOUTHERN AFRICAN 0 0.000 0.037 0UNCLASSIFIED 0 0.000 0.482 0

Temporary P Permanent O

Requirement to append to a customer record

4 Unaddressed non-personalised promotions targeted at small areas identified using Origins

DescriptionMany organisations distribute unaddressed information to small areas based on the characteristics of people living in those areas. The material may or may not be enclosed in an envelope but in all cases, there is no name on the material. All houses within the area will receive the same material, unless the letterbox indicates a preference not to accept unaddressed material.

UsesThis can be a very cost-effective way for government and commercial organisations to distribute information. Targeting increases the efficiency of reaching the right audience whilst keeping wastage to a minimum.

Typical Client ApplicationsAfter conducting research on current subscribers, a pay-TV company targeted areas containing a high proportion of people with names of Greek and Chinese origin.

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Requirement to append to a customer record

DescriptionSome organisations find it cost-effective to recruit customers by door-knocking in areas containing a high proportion of potentially attractive customers.

UsesOrigins identifies addresses most likely to contain people with name origins that belong to the desired audience. This increases efficiency and effectiveness of door-knocking, particularly in low-density areas where the distance travelled between the front doors of houses would otherwise make such an approach inefficient and impractical.

Typical Client ApplicationsFor the electorate of Bennelong in the 2007 federal election, the ALP targeted people with names of Chinese origin because it was felt that securing their vote could be decisive for the electorate as a whole. Culturally ‘compatible’ door-knockers were also part of the ALP’s successful strategy to pro-actively engage with that community and win the seat for Mary Delahuntly against John Howard.

5 Non-personalised targeting of addresses identified using Origins

Example

A metropolitan city council identified that the Chinese community was under-represented in the use of the city’s child care services. In an attempt to improve access to its services to all members of the community, the city council decided to promote its services to people with names of Chinese origin.

Direct engagement was known to be the most effective way of informing community members of the services.

Addresses with a high likelihood of Chinese residents were identified for a targeted door-knock campaign aimed at raising awareness of the council’s services.

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Requirement to append to a customer record

6 Targeted prospect lists compiled usingmodelled multivariate selection criteriaExample

To support a promotion for housing investment loans (product X) targeted at the most appropriate existing customers

The selection of customers was based on the following characteristics of customers who, historically, have had a good take-up rate for this product:

• Not having product X• Being a customer for less than five years• Maintaining a positive credit history• Origins codes – Customers with names originating in

Southern Europe• Likely to be aged under 45• 75% more likely to be male• Lifestyle Groups A, B, D, G

DescriptionThe most sophisticated targeting occurs within large organisations, such as those in the finance, telecommunications, and insurance sectors. Often with the aid of an external solutions partner, each customer is evaluated across a range of behavioural and demographic indicators, one of which may be Origins.

UsesPredictive models are an essential part of efficient and effective customer management and the delivery of customer service.

Typical Client ApplicationsAn energy retailer wanted to identify those customers most at risk of defecting to a lower price competitor. It built predictive models identifying those most likely to ‘churn’ to the competitor. Name origin was identified as a key predictive indicator. A statistical model was built to allocate a score to all customers and flag those customers who were most at risk of churn. The model allows them to target those customers with information and offers that are designed to promote their loyalty.

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Requirement to append to a customer record

7 Lists compiled using Originsas a single selection

Record ID Personal Name Family Name Name Origin608 ROBERT CHEUNG CHINESE CANTONESE

1313 CHE WEN LIU CHINESE CANTONESE1455 TED WONG CHINESE CANTONESE2196 WEI-TING CHENG CHINESE CANTONESE3304 HONG LIU CHINESE CANTONESE3558 PUN WONG CHINESE CANTONESE3612 HONG CHOU CHINESE CANTONESE6403 CAMILLA KWONG CHINESE CANTONESE6538 CLARE KWAN CHINESE CANTONESE7202 CHI LO CHINESE CANTONESE7428 PO YING TINA LEUNG CHINESE CANTONESE8573 RAYLENE WONG CHINESE CANTONESE8770 MATTHEW KWA CHINESE CANTONESE8907 TAK CHAN CHINESE CANTONESE

11069 TERRY WONG CHINESE CANTONESE11101 ERIC WONG CHINESE CANTONESE12074 WAI NG CHINESE CANTONESE12182 CHUN CHOY CHINESE CANTONESE12200 SUNGOK YIM CHINESE CANTONESE12265 FREDDY IP CHINESE CANTONESE12838 WEN LIU CHINESE CANTONESE13252 KUI-KWONG SHUM CHINESE CANTONESE13566 ALBERT CHUANG CHINESE CANTONESE14134 WAI SHEUNG POON CHINESE CANTONESE14252 EDDIE CHAN CHINESE CANTONESE14558 EDDIE Y T SIU CHINESE CANTONESE15036 REBECCA CHAN CHINESE CANTONESE15426 IAN VONG CHINESE CANTONESE16592 HELEN CHAN CHINESE CANTONESE

DescriptionThis table shows a list of names that are most likely to be of Chinese Cantonese origin. Lists can be compiled to reflect the most likely backgrounds for any broadly defined community.

UsesTargeting a group with a particular name origin can generate many positive outcomes for businesses and customers. The communication can be crafted to be culturally-relevant and appropriate, reinforcing a customer’s sense of identity. Customer survey panels can be constructed to be representative across the range of name origin groups.

Typical Client ApplicationsA large financial institution wanted to improve its engagement with customers of Islamic background. It constructed a product with relevant features – eg no interest earning during Ramadan – and mailed a targeted, culturally-relevant communication to key prospects.

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Requirement to append to a customer record

8 Realtime coding for enhanced customer experience

DescriptionThis chart shows the process of how a client would use Origins Realtime to engage better with a particular cultural groups.

UsesMore personalisation and better targeting add relevance to communications and achieve greater efficiency in the promotion of goods and services. Consumers experience improved service through communication with people who are more culturally competent.

Typical Client ApplicationsA private health insurer seeks to better engage with Chinese customers. After a name is entered on an enquiry form, realtime coding indicates those who are most likely to be of Chinese background. In the call centre, the enquiry can then be routed to a customer service agent who is culturally competent in dealing with Chinese people. The agent can then provide information about products , features and offers that are best suited to people with a Chinese background. In addition, the tone in the language (even if in English) will be better aligned with Chinese cultural expectations .

Record ID Personal Name Family Name CEL Code CEL1789 WEI-TING CHENG MBB CHINESE CANTONESE

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Requirement to append to a customer record

9 Customer segmentation

DescriptionMost large consumer-facing organisations group their customers into distinct segments based on a range of internal transaction history and external data elements, one of which may include a name origin code.

UsesSegmentation helps organisations understand groups of customers better so that their diverse needs can be better met. Using this resource, a client can develop solutions and communications that are optimised to the characteristics of the group.

Typical Client ApplicationsA large loyalty card-based retailer wanted to combine a range of geodemographic data with its own product purchase history and transaction records. The retailer selected Mosaic, a market-leading geodemographic system and a name analysis coding tool because it knew that its products were more appropriate to members of some cultural communities than others. The resulting segmentation enabled them to create products, communications and offers that were tailored to the characteristics of different segments.

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Requirement to append to a customer record

10 Multipurpose use for research, insight, targeted communications and campaign measurement

DescriptionThis table shows a sample extract from a customer database indicating how the Origins data would appear in a table form.

UsesUsing this resource, a client would be able to readily create a set of customers for ad hoc analysis – eg identifying the name origin mix of those customers who were entitled to a government grant.

Typical Client ApplicationsA government department wanted to understand if there was a cultural skew in the take-up of grants to assist with child care costs. The department needed an evidence base to justify an in-language campaign targeted towards unrepresented groups. The plan also included analysis of campaign response rate by name origin group to assess the effectiveness of government spend.

Record ID Personal Name Family Name CEL Code133 BEAUMONT DU PREEZ DCB143 MAHENDRA JEKKULA KAA620 THI HA MAA

1076 ANKIT SHUKLA KAA1296 DAVID KING AAA1751 PETER POTAMIANOS FAA1918 PETER FITZPATRICK BBB2306 VINAY SHAKYA KAA2320 SEBASTIAN WINGHAM AAA2863 BECKIE COOPER AAA3327 TRACIE WEST AAA3399 NATASHA SANDERSON AAA3466 STEVEN HARRISON AAA3863 HELEN RENISCH DDA4742 ZIA BESSARAB DPE6013 JAYDEEP MUNSHI IHA6200 CARLA ARDI DHA6843 TAHIR SAEED IHA7137 LETICIA CONNELL AAA7226 ANEATA HICKEY BBB

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Requirement to append to a customer record