Leads by Office
Making	
  Data	
  Driven	
  Decisions	
  
Marius	
  Kremeyer	
  
Op6mizely	
  Europe	
  
Netflix Redesign
 

Boo!	
  Hiss!	
  Shock!	
  Horror!
The	
  customer	
  is	
  always	
  right?

	
  

Engagement	
  	
  
	
  

	
  +	
  30	
  basis	
  points	
  

Reten6on	
  ...
GeWng	
  started?

	
  
Digging	
  deeper

	
  
 

Keep	
  tes6ng

“If	
  you	
  don’t	
  measure	
  it,	
  you	
  won’t	
  improve	
  it”	
  
	
  	
  	
  	
  	
  	
  	
 ...
Key	
  learnings	
  
1.	
  The	
  data	
  is	
  what	
  ma_ers	
  
2.	
  Explore	
  and	
  then	
  Refine	
  
3.	
  Always	...
Thank	
  You!	
  
Optimizely introduction - Marius Kremeyer
Upcoming SlideShare
Loading in …5
×

Optimizely introduction - Marius Kremeyer

2,382 views
2,332 views

Published on

Intro on Optimizely by Marius Kremeyer at "A night of persuasion & optimization" by the http://www.onlineoptimizers.eu meetup group (http://www.meetup.com/onlineoptimizers). Location: Optimizely HQ - part of Amsterdam e-Week.

Published in: Business, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
2,382
On SlideShare
0
From Embeds
0
Number of Embeds
6
Actions
Shares
0
Downloads
3
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Optimizely introduction - Marius Kremeyer

  1. 1. Leads by Office Making  Data  Driven  Decisions   Marius  Kremeyer   Op6mizely  Europe  
  2. 2. Netflix Redesign
  3. 3.   Boo!  Hiss!  Shock!  Horror!
  4. 4. The  customer  is  always  right?   Engagement        +  30  basis  points   Reten6on          +  20  basis  points      =  160,000  members   Lesson  Learned?   “Just  hold  true  to  the  philosophy  that  the  data  is  what  ma+ers”       Bryan  Gumm  –  Manager  of  Experimenta6on  -­‐  NeRlix  
  5. 5. GeWng  started?  
  6. 6. Digging  deeper  
  7. 7.   Keep  tes6ng “If  you  don’t  measure  it,  you  won’t  improve  it”                                                                            Henry  Ford  
  8. 8. Key  learnings   1.  The  data  is  what  ma_ers   2.  Explore  and  then  Refine   3.  Always  be  tes6ng!  
  9. 9. Thank  You!  

×