A look into understanding and analyzing data from a marketing perspective. This presentation explores relationships within data and structures data within a hierarchy. This presentation was given by Kenneth Lim as a guest lecture at the VU University Amsterdam on January 13, 2014.

1. @KennethLim
NOT
ALL
DATA
IS
CREATED
EQUAL

5. If Big Data was useful,
we would have called it
Useful Data

7. Donald Rumsfeld:
“
There are known knowns; there are things we
know we know.
We also know there are known unknowns; that
is to say we know there are some things we do
not know.
But there are also unknown unknowns -- the
ones we don't know we don't know.
”
7

8. Finding Data
known knowns
known unknowns
unknown unknowns
8
Data
Application
Analysis & Testing
Discovery

9. We analyze data…

10. not because
we want to report it

11. not because
we can

12. and certainly not because
#YOLO

13. but because we want to
improve the outcomes

14. We must learn
how our goals are impacted
by understanding the
relationships within the data

15. Not all data
is created equal

16. Goal
Performance
Process
Behavior Circumstances
Data Hierarchy
16

17. Circumstances: variables that can influence Behavior, e.g. Seasonality
Performance: what you need to reach your goal, e.g. € 2M Profit
Process: a key figure that impacts Performance, e.g. Profit Margin per Product
Behavior: individual actions within the Process, e.g. Products Bought and Price Paid
Goal
Data Hierarchy
17
: what you ultimate want to achieve, e.g. 10% Financial Growth

18. Online Shop Example
Known Knowns Known Unknowns
Goal Profit
Performance Revenue
Costs
Process Revenue per Customer
Revenue per Order
Revenue per Email Campaign
Behavior Website Visits
Products Bought
Orders Made
Amount Paid
Discounts Applied
Abandons
Emails Opened
Email Links Clicked
Customer Online Times
Circumstances Holidays
Gifts
Email Campaigns
Birthdays
Anniversaries
18

19. Circumstances
Understanding Relationships within Data
19
Goal
Performance
Process
Behavior
Profit
Revenue
Emails
Opened
Email Links
Clicked
Email Campaign
Amount
Paid
Revenue per
Email Campaign
Discounts
Applied
Abandons
Customer
Online Times

20. Email Campaign Process
20
1.
Email Received
9.
Email Order
Revenue (in €)
4.
Order
Placed?
5.
Abandoned?
7.
Discount
Applied?
8.
Email Order
Discount (in €)
6.
Email Order
Abandon (in €)
2.
Email Opened
3.
Email Link Clicked
Yes Yes
Yes
No No

21. Measuring Revenue per Email Campaign
21
Total Email Order Revenue
Total Number of Emails Sent
Total Unique Email Opens
Total Number of Emails Sent
Total Email Order Revenue
Total Email Order Revenue + Total Email Order
Discounts + Total Email Order Abandons
*
Revenue per Email Campaign =
Adjusted Revenue per Email Campaign =

22. But wait…
there’s more!

23. Circumstances
Improving Revenue per Email Campaign
23
Goal
Performance
Process
Behavior
Profit
Revenue
Emails
Opened
Email Links
Clicked
Email Campaign
Amount
Paid
Revenue per
Email Campaign
Discounts
Applied
Abandons
Customer
Online Times

24. We can obtain data
by asking

25. We can obtain data
by taking

26. We can obtain data
by testing

27. We should obtain
data by asking,
taking & testing

28. Improving Revenue per Email Campaign
28
Total Unique Email Opens
Total Number of Emails Sent
Total Email Order Revenue
Total Email Order Revenue + Total Email Order
Discounts + Total Email Order Abandons
*
Adjusted Revenue per Email Campaign =

29. An Evolving Approach to Data
1. Understand the impact of and the
relationships within the data
2. Collect the data that is important
3. Analyze the outcomes
4. Optimize the approach
29

30. The story is never
about the data itself

31. Final Thoughts
• Always look to improve the outcome
• Establish a firm understanding of the
relationships within your data
• Challenge the unknown
• The story is never about the data itself
31

32. Kenneth Lim
designxdata.com
kenneth.lim@designxdata.com
@kennethlim