Big data, big revenue
-
Upload
gary-allemann -
Category
Data & Analytics
-
view
244 -
download
12
description
Transcript of Big data, big revenue
![Page 1: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/1.jpg)
Enabling the Data & Information Culture
BIG DATA, BIG REVENUE
Why big data should be changing the way we market
![Page 2: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/2.jpg)
Improv
e
custom
er
engage
ment!
Improve
customer
retention!
Optimise
marketing for results!
![Page 3: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/3.jpg)
Half the money I spend on
advertising is wasted; the trouble is I don’t know which half. John Wanamaker
![Page 4: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/4.jpg)
Buying Influences are Different
![Page 5: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/5.jpg)
“…when it comes to purchasing decisions, the most influential recommendations come from people we actually know…”
Josh Cantone, Who are the real online influencers?
ReachResonance
Relevance
![Page 6: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/6.jpg)
SmartSet.ca
![Page 7: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/7.jpg)
How ‘social intelligence’ can guide decisions; McKinsey Nov 2012
![Page 8: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/8.jpg)
Consumer-facing companies must be able to gather and manage the right data, turn it into insights, and translate those insights into effective frontline action.Beyond The Hype: Capturing Value From Big Data And Advanced Analytics in:
Perspectives on Retail and Consumer Goods, Mckinsey & Co, No 1, Spring 201
![Page 9: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/9.jpg)
![Page 10: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/10.jpg)
![Page 11: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/11.jpg)
Avis
Lifetime value = current + potential value
Develop
Maintain Nurture
Retain
Curr
ent V
alue
Potential Value
360° view
![Page 12: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/12.jpg)
![Page 13: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/13.jpg)
£ (m
illio
n)
Supply Chain InventoryManagement
Cooling
6
100
5020
40
60
20
80
100
0
DemandManagement
Tesco
Annual Savings
![Page 14: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/14.jpg)
Tesco’s Data Journey
![Page 15: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/15.jpg)
![Page 16: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/16.jpg)
“We’ll be sending you coupons for
things you want before you even know
you want them.” Andrew Pole, Target
![Page 17: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/17.jpg)
Target
![Page 18: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/18.jpg)
![Page 19: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/19.jpg)
OfficeMax
![Page 20: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/20.jpg)
Big data = lots of small data
![Page 21: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/21.jpg)
Exponentially larger VOLUME
![Page 22: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/22.jpg)
Exponentially larger VELOCITY
![Page 23: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/23.jpg)
Exponentially larger VARIETY
![Page 24: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/24.jpg)
“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
![Page 25: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/25.jpg)
“…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
![Page 26: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/26.jpg)
Finding the value
![Page 27: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/27.jpg)
![Page 28: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/28.jpg)
“ … 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
![Page 29: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/29.jpg)
Value lies in how quickly you can access, process and use the right data
![Page 30: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/30.jpg)
Without impacting on your reputation
![Page 31: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/31.jpg)
![Page 32: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/32.jpg)
Focus on objectives, not tools
![Page 33: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/33.jpg)
Who can do what?
When?Where?How?
![Page 34: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/34.jpg)
Make it manageable
![Page 35: Big data, big revenue](https://reader034.fdocuments.us/reader034/viewer/2022052223/55831b0fd8b42ae55d8b4b3a/html5/thumbnails/35.jpg)
+27 11 485 4856
www.masterdata.co.za
@Gary_allemann
http://www.linkedin.com/company/master-data-management