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SUNZ 2011 - Vince Moder - Loyalty - One dataset to rule them all
 

SUNZ 2011 - Vince Moder - Loyalty - One dataset to rule them all

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  • Don’t forget that it is easy to focus on the path ahead, but if the previous achievements are not shared, others who don’t understand the value will disregard your work making it difficult to maintain momentum
  • The first thing I needed to do was lay out quite clearly all the work we do as a team. This turned into a new way that we classify and track the work that we do
  • Next we started finding the common steps in our projects and using Kaizen approach to determine how we can make the jobs be done more efficient. SAS templates of code in a shared code library were key. We need to find the common steps across all projects in order to create generic building blocks common to all projects.Pink are manual analyst work.Blue are tablesGreen is automated sections of code.We wanted to ultimately convert all bespoke (pink) steps into green (common, standardised) codes.
  • Pink are manual analyst work.Blue are tablesGreen is automated sections of code.Stored SAS Procedures – like Data Preparation, profiling tool, RFM, PCA, and Model development
  • Too much automation prevents analysts to build the most appropriate model they want.Too much flexibility lengthens both the model building and model production process.Analytical Data Mart aims to automate majority of the tasks without preventing analysts from accessing the data required to build the models they want.
  • Final prepare is the dataset at the top of the data pyramid. It contains 600 key variables at both the card and account levels from all earlier datasets
  • Notice the three main dimensions of Energy, Modernity, and Interests and Activities
  • Notice the three main dimensions of Energy, Modernity, and Interests and Activities
  • Notice the BIW Marketing Comms Entry Tables. Entered by CI (and eventually MC) via APEX Screens. Informed by the campaign brief.Red box of Initialise is Oracle processing, inherited from current initialisation process. Developed, maintained, and improved by Martin.Notice the Rules in lower left. These are to be called up by the Control Program (Vince and Martin).The Control Program will be heavily macrotised and only the top control inputs will need to be changed for a campaign. It will even give instructions to the Gatekeeper.The Data Prep Module (Corrin’s creation) will be used to enhance all files with required fields (as specified by Control Program).Once leads have been enhanced, remove obvious exclusions based on code.Model development module comes next, either automatic or bespoke (Dean)
  • Don’t forget that it is easy to focus on the path ahead, but if the previous achievements are not shared, others who don’t understand the value will disregard your work making it difficult to maintain momentum
  • Don’t forget that it is easy to focus on the path ahead, but if the previous achievements are not shared, others who don’t understand the value will disregard your work making it difficult to maintain momentum

SUNZ 2011 - Vince Moder - Loyalty - One dataset to rule them all SUNZ 2011 - Vince Moder - Loyalty - One dataset to rule them all Presentation Transcript

  • One Dataset to Rule Them All
    Vince Morder
    Loyalty New Zealand
    SUNZ 2011 Conference
    24 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
    Demographics, Occupation, Location, Family Structure
    Beliefs, Attitudes, Behaviours
    Knowledge, Enculturation
    Emotions, core motor skills
    Genes, Consciousness
  • Understanding customers using data and statistics
    Demographics, Address, Occupation
    Transaction, Account data
    Hard, Explicit
    Profiles, Models, Segments
    Data
    Surveys, Panel Groups
    Soft, Tacit
    Knowledge
    Information
  • 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.
  • The Customer Insights Team
    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.
    CUSTOMER INSIGHTS TEAM
    DATA WAREHOUSE
    Fly Buys Member and Transactional Data
    ParticipantSKU Data
    External Data
    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.
  • 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.
  • 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 .
  • 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
    New DW and SAS
    Historically
    Raw Data (Loyalty Host)
    Raw Data (Loyalty Host)
    BIW: Transformed, Normalised, and Summarised Data
    Bespoke code to extract data, transform.
    Summarised Tables
    Select templates, change code parameters for specifics of job
    Bespoke code to select data based on specifics of job, enhance with fields of interest
    SAS templates to pull data, run analysis generate automatic profiles and create models.
    Intermediate Tables
    Code: Analysis, profiles, develop a model.
    Output tables
    Output tables
    Reports/Presentations
    Reports/Presentations
  • BAU Targeting, Bespoke Models and Analysis
    Sequence of DevelopmentCI has been preparing to do less work
    Productisation
    Web Portal
    Jan 2010
    Dec 2010
    Productisation
    Productisation
    Stored SAS Procedures
    Reference Tables, Formats, Macros
    Marketing Comms Process
    X-Camp Optimisation
    Marketing Comms Tables
    Hopper
    Data Warehouse (BIW and SAS)
  • Ensuring Data Consistency Across All Analyses and Customer Interactions
    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
  • Final Prepare – A Single Customer ViewOne dataset to rule them All
    Data Warehouse
    Analysis Output
    SKU Transactions
    Profiles
    Rewards
    Campaigns
    Cardholders
    Final Prepare
    Models
    Transactions
    Census
    Mapping
    Real time data
    Account
    Reporting
    Scores
  • Segmentation
    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.
  • The knowledgeCUBE Segments
  • Example:Ranking a Target Group by the Segments
    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 Design
    The 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.
    SAS EGuide
    (Analyst)
    End Processing(Standard)
    Comms Tables
    (Data Warehouse)
    LeadInitialisation
    Final Prepare
    (standard)
    Hopper
    Campaign Code (Bespoke)
    Model Development
    Mailfile
  • The Gatekeeper
    The Gatekeeper becomes the common final funnel to all campaign files done by various analysts.
    Campaign Files
    Hopper
    Mailfile
    Mailfile
    Mailfile
    Mailfile
    Cross
    Campaign
    Optimisation
  • 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 :
  • Post Campaign Analysis
    Basic Sales
    Response v Non Response
    By Selection Variables
    Top performing outlets
    ROI Calculations
  • Industry Recognition for Loyalty NZ
    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.
  • What CI has become at 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...
  • Take Aways
    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.