This document discusses the importance of reliable data and taking action based on data insights. It begins by noting that many decisions in history were flawed due to unreliable data, like the tulip bulb bubble crash of 1637. The document then argues that while data leads to conclusions, taking action requires addressing emotions and desires. It provides two case studies where companies analyzed their data to identify issues, formed hypotheses, and tested solutions that increased revenue and qualified leads. The overall message is that data is only useful if it is used to drive actions that create value.

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JUST
OBSERVING
DATA IS LIKE
WASTING YOUR
LIFE IN FRONT OF A

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43% of the people died
prematurely from stress

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Not from stress, but from the belief that stress
is bad for them.
Source: Kelley, Litzelman, Witz research 2012, University of Wisconsin School of
Medicine and Public Health

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1. What happens without reliable data
2. Why the world needs data, anyway?
3. Data insights + actions = love
(2 case studies)
4. Q & A

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1. What happens without reliable
data?

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What was the price of
a tulip bulb in 1637?

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At one point in 1637, 12 acres (5 ha) of land
were offered for a Semper Augustus tulip bulb

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"Tulip price index1" by JayHenry - Own work from data of Thompson. Licensed
under CC BY-SA 3.0 via Wikimedia Commons

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Other F***-up’s because of twisted data:
> eBays’ stock price went from 107 to 7 dollars
in the dot com bubble (it got back to 400 after
10 years)
> In Romania, the price of an apartment went
up by 1000% between 2002 and 2007

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2. Why the world needs data, anyway?

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“The essential difference between emotion and reason
is that emotion leads to action, while reason leads to
conclusions.”
Donald B. Calne

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Data
Conclusions
Desires
Actions
Profit

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Pain
Desire
Emotions Action

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Emotion means
e-motion?

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How to convince the decision makers to act
on data?
1. Make them aware about the potential
2. Make them feel their pain
3. Make them desire
4. Repeat
* they are human beings, as well

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3. Data insights + actions = love

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Anomalies

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Patterns

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Patterns

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Case Study #1 - fashion webstore
1. Dived into Google analytics
2. Discovered that:
- 72% of the non-bounce visitors left without using the search, nor the size filters
- the conversion rate for visitors which used the search/filters was 8 times bigger
3. Made the hypothesis that the visitors are more likely to buy if they are finding
exactly what they needed
4. Built a segment with these visitors
5. Made a creative for those

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Case Study
This pop-up, which asked visitors for their dress size, acted as a filter pushing visitors toward more personalized
results.

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The result:
+ 48% revenue
for that product category

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Case Study #2
1. Deep-diving into Google analytics
2. Saw a huge drop-rate on the last part of the form
3. Made an on-exit intent survey to find out the real needs of the visitors
which got further into the conversion funnel
4. Made the hypothesis that the visitors are more likely to complete a lead
form if they are disclosing the need they want to cover with a loan
5. Triggered an on-exit intent survey to find out their needs and then asked
for their contact

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Case Study #2

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The result:
+ 25% qualified leads
for the same ammount of traffic

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Data is useful only if you use it.

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Questions?

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Want more action?
Use SPWK2015 code on www.marketizator.com
(you’ll get a free lifetime account.
If you live 100 years, that will worth $118 800 ;)

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Thank you!
@valentinradu
valentin@marketizator.com