SUNZ 2011- Susan Needham -  NZ Post case study genius
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  • Cartoon = typical response!
  • We start with sexy analysts, now we “play around” with some data! Targeted Communications reviewed the Lifestyle Survey data and all the other data held by NZ Post to look for ways to add value to our clients One area we identified was in segmentation Hence we kicked off a project in September 2009 to develop our own segmentation model There were a few motivations when we developed the model; we wanted it: To be genuinely adding value to our clients, as there is no point creating a “ me-too ” solution Speeding up the work and reducing the cost to our clients Providing organisations
  • Genius is conceived, and we go through the gestation period
  • After “playing around” with data, Genius is born! Geodemographic segmentation is based on two simple principles: People who live in the same neighborhood are more likely to have similar characteristics than are two people chosen at random. Neighborhoods can be categorized in terms of the characteristics of the population which they contain. Any two neighborhoods can be placed in the same category, i.e., they contain similar types of people, even though they are widely separated.
  • Tie back to theme “relationship”???
  • Note that we plan to add more DPID level data in future – Movers (presence of children), iTRY registration, and obviously Lifestyle Survey updates. So the amount of sub-MB data will grow over time. Often the most common current segmentation approaches will often lead to sub-optimal solutions – relying too heavily on statistics, and not allowing sufficient room for pragmatism to lead to a more simple, usable and therefore actionable solution.
  • Meshblocks are represented by thick black borders Properties are represented by smaller tiles within meshblocks Map shows that Genius ™ offers a high definition, granular view of NZ
  • Hot off the press developments! Following on from the birth of Genius, we have some siblings…
  • The “children” – playmates for Genius From April 2011 We can better service the industry
  • Think of Marketers as the “mother in law” in the whole relationship picture – they can be challenging, and they think they know best!
  • Ongoing process to relationship alive. Looking for ways to service marketers – and to keep them happy…
  • NZ Post has utilised both its data and its analytical capability to produce a range of innovative new products Most organisations could do more to fully utilise their data Analysts also need to look at how they communicate with marketers and other business people Need to utilise more visual techniques where feasible


  • 1. Sexy Analytics. Pure Genius! Susan Needham 24 February 2011
  • 2. ... a new kind of professional has emerged, the data scientist, who combines the skills of software programmer, statistician and storyteller/artist to extract the nuggets of gold hidden under mountains of data. Hal Varian, Google’s chief economist, predicts that the job of statistician will become the “sexiest” around. Data, he explains, are widely available; what is scarce is the ability to extract wisdom from them. From The Economist, February 2010 Statisticians are sexy!
  • 3. Key challenges faced in most organisations
        • Business people and analysts don’t always speak the same language
        • Organisations either don’t have advanced analytical capability or tools, or they are under utilising them
  • 4. Data we have available Page
  • 5. Genius TM Page
  • 6. The birth of Genius…
        • We kicked off the project in September 2009 to develop our own geo-demographic segmentation model
        • The clients told us what they wanted - a model based on family mix, lifestyle pattern, where people spend their money and their attitudes would be most beneficial to the market
        • We formed a project team with a mix of technical, business and marketing expertise
        • We then had to determine which data to include – what information we already had and what to purchase externally
        • Information at sub-meshblock was critical to our clients, as was the frequency of the updates
  • 7. Successful segmentation factors
        • To develop a successful segmentation, it is necessary to follow an approach that is designed to ensure a happy marriage between what are often seen as opposing poles of a continuum – Statistics and Pragmatism
        • A good segmentation solution should have the following characteristics:
        • Large enough to be worth targeting
        • Sufficient variance between clusters
        • Minimal variance within clusters
        • Easy to identify in the real world
        • Stability over time
  • 8. Data strategy to maximise segment granularity
    • We wanted as much data at DPID (household) level as possible
        • We utilised our own Rural (~200K households) and Lifestyle data (~200K households) to achieve this
        • For remaining households we determined their segment at a higher geographic level, and distilled it down to the household level
        • For medium to large meshblocks we got as much data as possible at a sub-meshblock level
          • We ended up with 55k sub-meshblock partitions vs 37k meshblock partitions: ~48% increase in granularity
  • 9. Segmentation objective and approach
    • Objective (for Urban Segmentation):
        • To create 20 to 40 segments that are distinct for key variables (relating to lifestage & affluence) from our collection of household, sub-meshblock and meshblock data
        • Data challenge
          • 55k sub-meshblock partitions and 1,300+ variables
          • Development data files total more than 30gb
    • Key Tools
        • SAS – for the heavy duty data crunching and model building
          • The main procedure utilised was Proc Cluster
        • Excel – for data visualisation
  • 10. Segment Visualisation Over-represented Under-represented Distinctive Features Page
  • 11. The final step
        • As you can see the colourful spreadsheet would make most people’s eyes glaze over!
        • Our next challenge was to bring it to life in a way that would make it meaningful to marketers and other business people
        • We did this by assigning each of the 36 segments a name, and grouping these up into similar clusters
          • The segment names are very “kiwi”, and as descriptive as possible
        • We also trawled through very detailed information to produce a summary for each segment
  • 12. NZ population by Genius™ Clusters The NZ population is divided into 9 clusters and 36 segments
  • 13. A. Urban Affluence
        • The most affluent cluster of NZ representing the top 12% of NZ households
        • More likely to have a university degree or diploma, and to have post-grad qualifications
        • Median house value of this cluster is $775k mostly in the decile 9 to 10 school zones
        • This cluster is over-represented by those with homes in family trust
        • Much more likely to have their own business, with income from self employment and / or have investment income
        • Skewed to age 45 to 64, self-employed or have investment income
        • Main location – Remuera, Mt Eden, Oriental Bay, Khandallah, Fendalton, Chatswood
  • 14. A. Urban Affluence
    • A1 Cream of the Crop
    • Top 1% of NZ Population
    • High spenders in most categories, eg. dry cleaning, gardening, beauty & hair salon etc
    • A2 Flushed with success
    • 2 nd highest spender in a broad range of categories eg. home electronics, furnishings, cafes
    • Average house value $894k
    • A3 Saffron & Silk Ties
    • Likely to be born overseas with overseas qualifications, skew to Asians
    • Top spenders on recorded music and smash repair
    • A4 Secure Urban Families
    • Slight skew to couple with two children
    • Average house value of $647k
    • A5 Stable Futures
    • Slight skew to having no children
    • Houses more likely to be built before 1940, with average value of $558k
  • 15. Genius™ map - Karori area
  • 16. A few challenges
    • Our old SAS server was having a “melt-down” during the Genius development
        • This did result in things taking longer than planned
        • Working with some of the larger datasets did cause the server to slow down or freeze
        • We did eventually get a new SAS server right towards the end of the development
          • At the same time we moved to using EG, which presented other “challenges”
      • Our new server is already having space issues – after only a year!
    • Things did take a lot longer than we originally anticipated
        • We produced weekly progress reports to keep management informed of delays
  • 17. But wait there’s more…
  • 18. Car prediction model
        • Due to changes in privacy legislation, the supply of refreshed NZTA vehicle registration data will no longer be available
        • NZ Post have purchased NZTA registration data - with car ownership history for 2.4 million car owners
        • We have combined this with other NZ Post data (inc Genius TM ), and created a single customer view for each NZ Car owner – containing:
          • Name and latest address
          • Number of cars owned - may be used for inferring family composition
          • Car age and prices at the time of purchase – for inferring the owner’s preference for new cars and budget for purchases
          • Car make and model – for inferring preference for (eg European cars, coupe)
          • Car type – for inferring family composition and lifestage (e.g. people movers)
          • Time between purchases – for inferring when will be the next purchase
  • 19. New Car Purchase & Genius TM Page
  • 20. Ethnicity prediction
        • A number of our clients wanted to be able to predict the ethnicity of every household in NZ
        • We are currently developing this utilising the names we have from various data sources
        • Using a combination of a proprietary look-up dictionary and the probability of occurrence from the Lifestyle Survey, we have predicted the actual ethnicity of the person or the ethnic origin based on their surname
        • MAORI: Hape, Moka
        • PACIFIC ISLANDERS: Folau, Tuipulotu
        • ASIAN: Cho, Huang
        • VIETNAMESE: Banh, Bui
        • JAPANESE: Fujimori, Fujino
        • INDIAN: Guha, Gupta
        • OTHER EUROPEAN: Cloete, Taliaard
        • EUROPEAN: Bolton, Gifford
        • Will be used to further enhance the accuracy and granularity of Genius
  • 21. Making analytics sexy to marketers
  • 22. First we whip the data into shape
        • Before we can begin, we need to append DPID’s to the client database
          • A DPID is a D elivery P oint Id entifier that has been allocated to every house, business, church, school in New Zealand, some 2.2 million points
          • Each one is flagged as delivered or not delivered to
        • Then we remove duplicate records to obtain a single customer view
        • Then we can use the DPID to append other data sources…
  • 23. Let’s take a random address from your database
    • Suburb = Remuera
    • Deprivation Index 1 (In the wealthiest 10% of country)
    • The meshblock for that random address has approx:
        • 126 residents in 42 dwellings
        • Median Age 41
        • Median 4 bedrooms per dwelling
        • 75% of households earning HHI $100k+
        • 44% of residents have a Bachelors Degree or Higher education
    • From Movers/NZCOA
        • Mr and Mrs X moved address in July 2008
        • They are owners not renters
        • They moved from Wellington
    • From Lifestyle Survey
      • They have two children, a cat and a dog
      • They drive a BMW and a VW Golf
      • They have household income of over $150k
      • They like water sports
      • They have a two year fixed rate loan reviewed in May 2011
      • They are considering a trip to Europe
    • The address is a residential property, with a “no circulars” sign
    • They are in Genius Segment “A1 Cream of the Crop”
    By linking the DPID of your customer to other data sources you build a much bigger picture of who they are Page
  • 24. Then we profile a client’s database
        • We would normally start by profiling the client database by Genius segment to get an understanding of existing customers
          • We would sometimes do sub-profiles focusing on their “best” customers
  • 25. And we usually produce some maps
  • 26. Page
  • 27. Then find prospects that are similar
    • Acquisition
        • Now we know the Genius segments to focus on, we can target suitable acquisition lists to find prospects that fit this Genius profile
        • We can then target the best areas for:
          • Unaddressed letterbox drops
          • Semi addressed mailing
          • Addressed mailing
    • Cross-sell
        • We can profile the Genius segments of customers that are the most profitable, then find more customers that fit this profile from the wider customer base to cross-sell to
  • 28. Summary
        • We started off with the right people – with technical capability and business nous
        • We had the right tools
          • SAS has been an integral part of the development of Genius TM , and other work we do in our team
        • We had the right data to make the project a success
        • And we had the organisational vision to support a project such as this
        • A marriage made in heaven!
  • 29. Questions & Discussion