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BIG DATA, BIG REVENUE 
Why big data should be changing the 
way we market 
Enabling the Data & Information Culture
Improve 
customer 
retention!
Half the money I 
spend on advertising 
is wasted; the trouble 
is I don’t know which 
half. 
John Wanamaker
Buying Influences are Different
“…when it comes to purchasing decisions, 
the most influential recommendations 
come from people we actually know…” 
Josh Cantone, Who are the real online influencers? 
Reach 
Resonance 
Relevance
SmartSet.ca
How ‘social intelligence’ can guide decisions; 
McKinsey Nov 2012
Avis 
Lifetime value = current + potential value 
Develop 
Retain 
360° view 
Maintain Nurture 
Current Value 
Potential Value
£ (million) 
Supply Chain Inventory 
Management 
Cooling 
6 
100 
50 
100 
60 
40 
20 
20 
80 
0 
Tesco 
Demand 
Management 
Annual Savings
Tesco’s Data Journey
“We’ll be sending you coupons for 
things you want before you even 
know you want them.” 
Andrew Pole, Target
Target
OfficeMax
Big data = lots of small data
Exponentially larger VOLUME
Exponentially larger VELOCITY
Exponentially larger VARIETY
“Building out Big 
Data capabilities 
too often becomes 
the end goal itself”. 
What you need to make Big Data work: The pencil: Matt 
Ariker, Forbes CMO Network Article
“…most significant obstacle to big data efforts… 
is the gap between the need and the ability to 
articulate measurable business value” 
Analytics: The real-world use of big data in financial services
Finding the value
“ … the key is to focus on the big decisions for 
which if you had better data, … you’d make 
more money.” 
David Court, McKinsey, 2013
Focus on objectives, 
not tools
Who can 
do what? 
When? 
Where? 
How?
Make it manageable
http://www.linkedin.com/company/master-data-management 
@Gary_allemann 
gary@masterdata.co.za 
+27 11 485 4856 
www.masterdata.co.za

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Capstone Project on IBM Data Analytics ProgramCapstone Project on IBM Data Analytics Program
Capstone Project on IBM Data Analytics Program
 

Big data, big revenue

Editor's Notes

  1. Customer Engagement Who are your customers? Where are they? What do they want from us? What’s the best way to contact them? When’s the best time to talk to them? Customer Retention What influences our customers’ loyalty? What can we do to promote loyalty? How do we keep them coming back? Optimise Marketing Which channels offer us the best value? Where do we get the best return? Where should we focus our spend? How did that last campaign do? Test Measure Analyse
  2. Crowdtap, from http://mashable.com/2012/06/13/influence-marketing-infographic/
  3. REACH RESONANCE RELEVANCE Crowdtap, from http://mashable.com/2012/06/13/influence-marketing-infographic/ Influence the people that are influencing your audience
  4. http://www.linkedin.com/today/post/article/20140219191358-127250022-content-and-data-is-at-the-center-of-everything
  5. How ‘social intelligence’ can guide decisions; McKinsey Nov 2012
  6. Implemented customer segmentation by value: lifetime value model offered added value to top segment good offers (value) to bottom segments = loyalty at both ends 360deg customer view aids customer service and informs marketing to ea segment Mined data to discover areas where could add extra value for extra market share
  7. 3rd largest retailer in the world Began using data to inform business decisions 1990 First store-brand customer loyalty card “Clubcard”
  8. Supply Chain: stock, depot, delivery, supplier purchase optimisation; out-of-stock on special offers reduced by 30% Inventory: stock levels shown on Google maps; instore camera tech eg “broccoli cam” Cooling: Central mgt of temps; data aligned with Google maps; remote diagnostics so techs arrive with right parts Demand: Use of weather forecasts and regional sales patterns; hot day in Scotland = different sales and stock req from hot day in South of England Big data is also a powerful, effective force in the multi-channel strategy that Tesco sees as central to understanding the future of consumer retail behavior. This strategy addresses the consumer’s desire to use physical stores, mobile devices, and desktop computers in combinations. For example, a consumer could use an internet kiosk in the store to order an item for collection in the store the next day. A mobile device could be used to order groceries to be delivered at home. These combinations of retail channels require the company to understand not only the purchasing patterns of individual consumers, but also their channel preferences and logistics requirements.
  9. American retailer Used big data analytics to devise a “pregnancy prediction” score based on buying patterns Sent relevant coupons to expectant mothers throughout pregnancy Cornered “baby-on-board” market using “heightened focus on items and categories that appeal to specific guest segments such as mom and baby.” (Greg Steinhafel, President: Target)
  10. [A] man walked into a Target outside Minneapolis and demanded to see the manager. He was clutching coupons that had been sent to his daughter, and he was angry, according to an employee who participated in the conversation. “My daughter got this in the mail!” he said. “She’s still in high school, and you’re sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?” The manager didn’t have any idea what the man was talking about. He looked at the mailer. Sure enough, it was addressed to the man’s daughter and contained advertisements for maternity clothing, nursery furniture and pictures of smiling infants. The manager apologized and then called a few days later to apologize again. On the phone, though, the father was somewhat abashed. “I had a talk with my daughter,” he said. “It turns out there’s been some activities in my house I haven’t been completely aware of. She’s due in August. I owe you an apology.” Read more: http://www.businessinsider.com/the-incredible-story-of-how-target-exposed-a-teen-girls-pregnancy-2012-2#ixzz2tDCFvr9O
  11. American “superstore” chain selling office products, technology and furniture Largest retailer in the sector Named one of “World’s Most Ethical Companies” in 2013
  12. Occasional customer of OfficeMax One purchase of paper since daughter’s death in car crash Company bought 3rd party data to use for targeted marketing
  13. Quality Validation Reporting Business Rules Governance
  14. Moore’s law also applies to data Relational databases reaching capacity and scaling limits
  15. Exponentially larger rate of production
  16. Formats outside scope of traditional relational dbs Internet of things
  17. This is not an IT problem: You know what you need; IT doesn’t. You can’t always wait for IT to understand. You need to be able to get the data yourself!
  18. Analytics: The real-world use of big data in financial services, Report by IBM Institute for Business Value and Saïd Business School at the University of Oxford, May 2013
  19. Can be like looking for a needle in a haystack
  20. Throwing more hay on the stack won’t help you find the needle
  21. McKinsey Chief Marketing & Sales Officer Forum (2013-07-09). Big Data, Analytics, and the Future of Marketing & Sales (Kindle Locations 121-123). McKinsey & Company. Kindle Edition.
  22. Access Usage Storage Maintenance Retirement
  23. Access Usage Storage Maintenance Retirement