Not All Data Is Created Equal: Data Analysis for Marketers
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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.
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Not All Data Is Created Equal: Data Analysis for Marketers
- 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
- 10. not because we want to report it
- 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
- 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 =
- 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