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MRIA 2014 National Conference — The Big Data Dig

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The following are my notes taken live from a presentation from Susan Williams (Cadillac Fairview) and Susan Ince (Epic Consulting).  Notes may have typos in them.  A short video with the presenters is below.

Intro: 10 years of data, over 1 million gift card base

Case Study

  • gain insight from big data project on shopping centre gift card database
  • learn more about consumer purchase patterns to apply to future marketing gprograms
  • leverage rich databank of data available

Challenges Mining Big Data

  • big data not big research or big quant
  • cannot use standard market research software
  • clients may be buried in data
  • data can be in silos across different business units
  • critical data may not be stored in client organization
  • need to merge/consolidate files
  • surprises when digging begins

Strategy for success

  • develop a dat and analysis plan with clients
  • discuss areas where clients expect the greatest value/ROI
  • match the plan to business stregy
  • selected a limited number of areas to focus on or start small
  • proceed in stages and make trade-offs/set priorities as you go along based on what will best support client business goals
  • clients and data analysts working together in dynamic produces to figure things out
  • reporting/results framed for senior executive buy-in

This case study:  

The data files:

  • very hard to open
  • 14 variables (purchase code and card code were the linking variables), created new variables — example lifestyle of card

Plan & Approach

  • Initial:  Scope, identify issues with merging data, preliminary data runs, establish criteria to filter down
  • Decisions/Criteria For detailed reporting used 26 top card values with bases size of 1,000

Learning

  • Lifecycle of a shopping card:
  • 95% are spent within a year
  • 10% are spent within a week to 10 days
  • by 2 months over half are spent
  • 4 months three-quarters have been spent

Other learnings:

  • Approximately 2/3 are spent on one day, with most in just one transaction.
  • Average number of transactions per card is 1.9, with the highest about 5.
  • The $50 card is the most popular denomination.

Redemption Location:

  • 65% of gift carded redeemed are purchased at the same mall
  • 35% are purchased from another CF mall
  • Proves that the national brand is important because of the 35%

Insights:  Top Retailers

  • anchors are the top retailers both for$ spend, # transactions and also for cross-shopping
  • identified top 20 retailers for both spend and number of transactions
  • Hudson’s Bay and Retailers Cross Shopped Most Often
  • Apple — 3rd highest in redemptions after Sears and The Bay, top spend per transaction at $123, low cross shop between other retailers, consistent with other analysis done to date

 Conclusions:

  • Learned a lot more, but learned a lot of what big data can be.
  • Important to make sure you have the right questions, do not just go in and pull out “stuff”.

Impact

  • Clients need help to get their data out of silos and deliver valuable insights from their big data depositories
  • big data needs people with the know-how to look at data, to get into the data, to find models, to integrate from multiple sources and leverage to create vlaue
  • market research does not need to be sidelined or end up as roadkill on big data highway

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