Not All Data Is Created Equal

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Information about Not All Data Is Created Equal
Marketing

Published on March 10, 2014

Author: kennethlim

Source: slideshare.net

Description

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.

@KennethLim NOT ALL DATA IS CREATED EQUAL

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

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

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

We analyze data…

not because we want to report it

not because we can

and certainly not because #YOLO

but because we want to improve the outcomes

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

Not all data is created equal

Goal Performance Process Behavior Circumstances Data Hierarchy 16

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

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

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

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

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 =

But wait… there’s more!

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

We can obtain data by asking

We can obtain data by taking

We can obtain data by testing

We should obtain data by asking, taking & testing

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 =

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

The story is never about the data itself

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

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

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