One Dataset to Rule Them All

Vince Morder Loyalty New Zealand

SUNZ 2011 Conference24 February 2011

Te Papa, Wellingtom

Introduction

• Need for data to give a consistent, complete picture of the customer – Data can be too fragmented across your organisation– Too much data preparation for analysts– Results cannot be easily translated to other products.

• Need for better integration of analysis into the business• Even the best analyses are useless unless they are used.• Analyses can have limited lifespan if not adjusted for the

needs of your organisation and the changing habits of your customers.

Inferring a person from outside in

Beliefs, Attitudes, Behaviours

Knowledge, Enculturation

Emotions, core motor skills

Genes, Consciousness

Demographics, Occupation, Location, Family Structure

Understanding customers using data and statistics

Transaction, Account data

Profiles, Models, Segments

Surveys, Panel Groups

Demographics, Address, Occupation

Hard, Explicit

Soft, Tacit

Data

Information

Knowledge

Data, Information, Knowledge

• Data• Bits of unorganized and unprocessed facts • Data is a prerequisite to information.

• Information • Information can be considered as an aggregation of data• Information has usually got some meaning and purpose.

• Knowledge • What resides in the minds of people in your organisation. • Used to transform data into information.

• Knowledge is derived from information in the same way information is derived from data.

Knowledge Management (KM)

• Cannot define knowledge

• KM is different from Information Management

• The function of KM is to create a shared knowledge context.

• Varies from org to org

• Requires a cultural change.

• KM is what you put into place to deliver value from your knowledge.

About Loyalty NZ

About Loyalty NZ

Renowned for its Marketing excellence

Recent awards:• Asia Pacific and Japan HP Digital Print Awards – Direct

Mail Award 2010• 2008 TVNZ NZ Marketing Award - Consumer Services

Gold• AXIS Craft – TV/Cinema/New Media – Animation/Design

& Motion Graphics Silver 2008• AXIS Craft- TV/Cineam/New Media- Visual Effects

Bronze 2008

Fly Buys – “Dream a Little”

• Statistics of the Fly Buys Programme– 14 years of shopping history– 70 Partners (Participants)– 1.2 million active households (70% penetration)– 2.2 million active cards– 1000’s of rewards

• Business Model – Many ways for consumers to collect points, as the coalition of participants

covers the full range of retail products. Strong retention.– Participants pay LNZL for each point collected.– The carrot is the reward. “Dream a Little” is ‘the one thing’. – Cycle of usage and redemption.– Leading innovator in the industry. – Recognised from the start that the real value is from the data.

LNZL Customer Insights exists to deliver our Participants with insights about their customers (and potential customers) to enable them to gain maximum benefit from their involvement with Fly Buys.

Fly Buys Member and Transactional

Data

External Data

DATA WAREHOUSE

CUSTOMER INSIGHTS

TEAM

We do this through leveraging the power in the Fly Buys database by applying

advanced analytics tools and techniques to turn data into actionable insight.

Participant

SKU Data

The Customer Insights Team

The Base Data Fly Buys holds

The Loyalty New Zealand Customer Insights team

Loyalty New Zealand’s Customer Insights team is driven to provide compelling outcomes for Fly Buys Participants leveraging the very best data. This is represented in the vision:

“Providing unrivalled levels of Customer Insights to drive outstanding outcomes”

To enable this to occur, Loyalty New Zealand has invested significantly over the past two years to provide market-leading infrastructure and expertise.

A team of 12 specialists in Wellington are focussed on extracting the right information and insights to support desired activities/requirements.

The Pyramid of Delivery

Monthly Reporting • Every participant gets a monthly summary report

showing their the volume of spend and points accumulated.

Spend VisitsStandard

PointsBonus Points

Members

Average Spend

per Member

Average Spend

per Visit

Average Visits per Member

Spend VisitsStandard

PointsBonus Points

Members Spend VisitsStandard

PointsBonus Points

Total Points Issued

Points Issuance Target

Variance %

J ul 2008 $20,702 468 0 18,450 408 $50.74 $44.24 1.15 - - - - - $20,702 468 0 18,450 18,450 0 No Target

Aug 2008 $50,968 2,039 50 89,000 1,788 $28.51 $25.00 1.14 - - - - - $71,671 2,507 50 107,450 89,050 30,000 66.31%

Sep 2008 $53,249 742 0 30,150 647 $82.30 $71.76 1.15 - - - - - $124,920 3,249 50 137,600 30,150 31,000 - 2.82%

Oct 2008 $242,871 709 0 14,028 340 $714.33 $342.55 2.09 - - - - - $367,791 3,958 50 151,628 14,028 30,000 - 113.86%

Nov 2008 $792,030 1,155 10 13,137 1,066 $742.99 $685.74 1.08 - - - - - $1,159,821 5,113 60 164,765 13,147 30,000 - 128.19%

Dec 2008 $1,321,571 1,737 210 29,376 1,557 $848.79 $760.84 1.12 - - - - - $2,481,392 6,850 270 194,141 29,586 30,000 - 1.40%

J an 2009 $743,986 1,133 10,000 37,118 944 $788.12 $656.65 1.20 - - - - - $743,986 1,133 10,000 37,118 47,118 35,000 25.72%

Feb 2009 $711,170 1,077 1,000 26,636 979 $726.42 $660.32 1.10 - - - - - $1,455,155 2,210 11,000 63,754 27,636 26,000 5.92%

vs Target

report created on 10 March 2009

Month

% Change from Same Period Last Year Year to DatePeriod

Participant Name

Transactional Data

For February 2009

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Total Points I ssued Points I ssuance Target Variance %

Monthly Dashboard Outlet Reports• Spend volumes, # customers, and points issued by month for last 60 months.

Demographic Dashboard Report • Distribution of income, age, segment, commitment of an outlets customers .

ParticipantVisits Dashboard - Participant

Outlet by transaction date

For May 2010report created on 04 J une 2010

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Income

Customers National

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Commitment

"Customers" "National"

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Success Comfort Urban Single Family Provincial Working Grey Cultural Rural #N/A

Segmentation

Customers National

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<20 20-29 30-39 40-49 50-59 60+

Age

Customers National

How to Best Organise the CI Team

• Handling so much data• 5 TB database• Hundreds of Millions of transactions a year.

• The sheer volume of targeting campaigns (5 per week)

• The sheer volume of analytical requests

• …. And still keep developing and improving our services

How to Best Organise the Work

Redesigning how we do our work

Raw Data (Loyalty Host)

BIW: Transformed, Normalised, and

Summarised Data

Reports/Presentations

Output tables

Bespoke code to select data based on specifics of job, enhance with fields of

interest

Code: Analysis, profiles, develop a model.

Intermediate TablesSAS templates to pull data,

run analysis generate automatic profiles and

create models.

Bespoke code to extract data, transform.

Historically New DW and SAS

Raw Data (Loyalty Host)

Select templates, change code parameters for

specifics of job

Reports/Presentations

Summarised Tables

Output tables

Data Warehouse (BIW and SAS)

Stored SAS Procedures

Marketing Comms Process

Reference Tables, Formats, Macros

Sequence of DevelopmentCI has been preparing to do less work

Marketing Comms Tables Hopper

X-Camp Optimisation

Jan 2010

ProductisationProductisation

ProductisationWeb Portal

Dec 2010

BAU Targeting, Bespoke Models and Analysis

• All our analysts are focused on the customer and what drives them. The team wants to ensure we are continually building and enhancing our single view of the customer.– This view needs to be readily available for all analyses, reports,

models, campaigns, etc…

• Same data can feed into our communication management framework – Capture data about interaction – Use relevant customer data to drive the message/offer

• Analysis data combined with customer interactions maximises our understanding of what drives the customer and ensures relevance of communications

Ensuring Data Consistency Across All Analyses and Customer Interactions

Final PrepareTransactions

Cardholders

Rewards Profiles

Campaigns

Models

Mapping

Reporting

Final Prepare – A Single Customer ViewOne dataset to rule them All

Census

Account

Scores

Data Warehouse Analysis OutputSKU

Transactions

Real time data

LNZL had been doing only RFM segmentations on a participant basis. Simply, yet effective.

We wanted a more mass customised segmentation (like Mosaic), but we did not want to use traditional demographic data.

The key objective was to build a lifestyle based segmentation that is equally applicable for all Fly Buys participants, rather than focused on any particular participant or type of participant.

Using our Customer Lifestyle Surveys undertaken by Loyalty NZ over the past two years with 50,000 respondents in each survey, CI team developed knowledgeCUBE segmentation.

• Enables CI team and our participants to move beyond the standard geo/income dominated segmentations – provide an understanding into what makes the people tick.

This was a risky approach because it could have meant that we have segments that do not correlate with behaviours that we measure. However, it has worked spectacularly well.

Segmentation

The knowledgeCUBE Segments

Segment Name Description

Dimensions Demographic Skews

Energy0 -> energy from self (independant)

100 -> energy from others (extrovert)

Modernity0 -> traditional100 -> modern

Interests and Activities0 -> feminie type interests

100 -> masculine type interests

Age Gender

Income

Active Family Focus Family focussed people who like to get outdoors 48 49 31 Average Female HigherActive Golden Years Older people who still enjoy a variety of activities 16 10 71 Older Male LowerActives A life full of a wide variety of activities and interests 79 53 67 Average Mixed HigherAdventurous Motorheads Love their cars, but also a variety of other ‘manly’ pursuits 28 50 83 Average Male MiddleArts And Activities Into arts and cultural activities as well as physical activities 79 36 37 Average Female MiddleArts And Crafts Older people who enjoy arts and craft type activities. 65 3 35 Older Female HigherComfortable Golden Years Transitioning from working life to retirement 39 4 56 Older Mixed HigherDaily Drudge Living with a partner in a simple but financially pressured life 26 36 39 Average Female Lower

Dynamic Art Lover People engaged in a dynamic arts scene (tending to be younger than the other arts segments) 54 49 35 Average Mixed Higher

Dynamic Singles Living life to the full while living on their own 58 95 17 Younger Female LowerFinancial Success Have achieved financial success 55 34 60 Average Female HigherGolden Years On My Own Older people living somewhat isolated lives 12 5 42 Older Mixed HigherGolden Years Singles Older people living on their own 24 4 21 Older Female LowerGolden Years Together Spending retirement years with someone else 43 0 32 Older Female LowerHands On Young males doing manual based labour 5 64 57 Average Male LowerHappy Family Focus Comfortable family focussed life 45 63 34 Average Female HigherLads Young men who love sports and bars 39 100 88 Younger Male HigherMature Art Lovers A more sedate pace to life with a strong focus on the arts 53 8 58 Older Mixed MiddleMature Singles Middle aged people living on their own 35 34 20 Average Female MiddleMature Sports Enthusiast Aging men who love their sports 24 54 92 Average Male HigherMature Strugglers Aging and struggling to make ends meet 5 58 9 Average Female LowerModern Cultural Blend Strong cultural ties coupled with a modern lifestyle 100 81 71 Younger Male MiddleModern Young Women Sociable young women living a dynamic and modern lifestyle 52 88 37 Younger Female MiddleMotor Mad Live for their cars (and men's interest magazines) 8 79 66 Younger Male MiddleMr & Mrs Comfortable Living with a partner in a simple, comfortable life 26 31 37 Average Female HigherNew New Zealanders Recent immigrants to New Zealand 6 42 68 Average Male HigherOn My Own Living a simple life on their own 0 40 37 Average Male LowerOn The Move Adventurous outdoor people 33 41 46 Average Mixed LowerParty On Live to party 31 88 26 Younger Female HigherSelf Aware Strongly driven by their spirituality 35 30 36 Average Mixed HigherSocialites Are in to a variety of social activities 70 81 29 Younger Female HigherSporting Women Women who love their sports 54 47 58 Average Female MiddleSports Junkies Young men who live for sport and sport alone 37 79 100 Younger Male MiddleStruggling Cultural Ties Strong cultural ties, but struggling to make ends meet 43 64 3 Younger Female MiddleStruggling Family Focus Family focussed but struggling to make ends meet 18 58 15 Younger Female MiddleStruggling Singles Singles struggling to make ends meet, often a single parent 35 47 0 Average Female Lower

Urban Chic Young urban people for whom style and fashion are important 50 65 38 Younger Female Middle

Young Adults Young adults with a strong sense of self 17 85 37 Younger Male Lower

Example: Ranking a Target Group by the Segments

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Across all participants, we can show how their base ranks according to the segments. Advantages include instant ranking for any data profiling request for any participant (Example below shows ranking for customers who redeemed through our Premium Rewards catalogue)Segments can facilitate knowledge in your organisation.

Results across all activities can be stored at the segment level .

Improving the Marketing Campaign Process

Marketing Campaign Process DesignThe campaign process is completely standardised and integrated with core systems yet process can still handle a wide variety of situations and levels of complexity. Bespoke code has been minimised.

Hopper

Campaign

Specifications

Contact

History

Campaign

variables

Campaign

Results

Lead Initialisation

Final Prepare(standard)

Comms Tables(Data Warehouse)

SAS EGuide (Analyst)

End Processing(Standard)

Mailfile

Campaign Code

(Bespoke)

Campaign

Specifications

Contact

History

Campaign

variables

Campaign

Results

Model Development

The Gatekeeper

Hopper

Campaign

Specifications

Contact

History

Campaign

variables

Campaign

Results

The Gatekeeper becomes the common final funnel to all campaign files done by various analysts.

Mailfile

Mailfile

Mailfile

MailfileCross

Campaign Optimisation

Campaign Files

Selection Profiles

• Campaign files always need to be checked for quality, so we have improved our processes involving quality checking and signoffs as well as improved standard selection profiling reports :

CampaignParticipantDeployment DateData DateAnalyst

Total Minimum Maximum Total Minimum Maximum Non Shoppers - 14,906 $351,413 $2 $3,208 7,270 1 49 Shoppers Outlet 1 3,375 $2,135,191 $3 $7,128 36,934 1 92 Shoppers Outlet 2 4,437 $4,664,140 $4 $6,985 56,007 1 86 Shoppers Outlet 3 3,210 $3,167,771 $0 $8,006 46,616 - 104

Total - 25,928 $10,318,515 $0 $8,006 146,827 - 104

Commitment Band # Members % Members Age Band # Members % Members Missing or Null 300 1.2% 1 - 18 212 0.8% 1 - 19 12 0.0% 19 - 29 4,691 18.1% 20 - 29 446 1.7% 30 - 39 6,028 23.2% 30 - 39 2,877 11.1% 40 - 49 6,400 24.7% 40 - 49 4,551 17.6% 50 - 59 4,785 18.5% 50 - 59 4,961 19.1% 60 - 69 2,753 10.6% 60 - 69 4,331 16.7% 70 - 79 875 3.4% 70 - 79 3,369 13.0% 80 - 89 165 0.6% 80 - 89 2,421 9.3% 90 - 99 7 0.0% 90 - 99 1,569 6.1% 100 + 12 0.0% 100 + 1,091 4.2% Total 25,928 100%

Total 25,928 96%

Territorial Authority # Members % Members 30 - 39 6,028 28.9% 40 - 49 6,400 30.7% 50 - 59 4,785 23.0% 60 - 69 2,753 13.2% 70 - 79 875 4.2%

Total 20,841 100%

Spend (based on latest behavioural period) Visits (based on latest behavioural period)Communication # MembersSegment

Rachel Wilson

CAMPAIGN DETAILSCampaign NamexxxxxxxxxxxxxxThursday, 21 January 2010Wednesday, 20 January 2010

SELECTION PROFILING

Post Campaign Analysis

• Basic Sales • Response v Non

Response• By Selection Variables • Top performing outlets• ROI Calculations

Earlier in September LNZL won the international Direct Mail (DM) Award for the industry leading Fly Buys Point Summary mailing.

Publicis Singapore and Jon McKenzie, Digital Creative Director Leo Burnett, commented ‘that the new look loyalty statement showed that with great design thinking and an underpinning data strategy, this communication represented – best in class. It was the stand-out entry in what is a hotly contested category’

The underpinning data strategy is in fact driven by Gatekeeper in being able to allocate 175 different messages for 750,000 customers. This is over 600 trillion variations!

The CI team will continue to evolve the Gatekeeper to handle more sophisticated simultaneous optimisation criteria. A great example product that does this type of optimisation is the SAS Marketing Optimisation.

Industry Recognition for Loyalty NZ

The CI team now runs 10 campaigns in a week for our participants.• Half of these have had models or segments applied to them. • We are on track to do over 500 campaigns by 31-March-2011.

The team offers over 20 analytical products, from simple reports to profiles, to maps and even SKU-based models.

Continuing to broaden the scope of our thinking to think about the customer from a single view. Contact strategy and strategic segments are being refined for 2011…

Knowledge management framework for realising synergies across analyses. Layering our data and insights onto our common frameworks in order to continually understand what drives our customers.

And this is just the beginning...

What CI has become at Loyalty NZ

Never stop thinking about what your data can do for your marketing and your business

Make synergies in your Analyst team by making One Dataset to Rule Them All.

Establish knowledge management practices to give life to the One dataset.

Take Aways