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Faculty of Computer Science
Professorship Distributed and self-organizing Systems
Good bye conversion rate
a smarter way to optimising Search Engine
Results Pages
MartinGaedke.com
VSR.Informatik.TU-Chemnitz.de
///// IS-SWIS 2017, Rotterdam, February 9, 2017///
2
Search	Engine
Search
Search
Search
Search
Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de
Search Engine Result Pages (SERPs)
3Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de
(simplified) Motivation
4
Evaluation Method Efficient? Effective?
User Testing ✖ ✔
Usability Inspection ✖ ✔
Split Testing ✔ ✖
Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de
Split Testing
5
Buy this
Buy this
Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de
Version	A Version	B
Conversion Rate:
proportion of visitors to a website
who take action due to subtle
requests from marketers, SEOs,
(semantic) data providers etc.
The problem with Split Testing
6Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de
(source:	Wikipedia.com)
Version A: Getting the User‘s Zip Code
7Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de
8
$$$ ≠ Usability
Jakob Nielsen: Putting A/B Testing in Its Place,
http://www.nngroup.com/articles/putting-ab-testing-in-its-place/
Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de
Goal:
to leverage the
advantages of split
testing AND to
facilitate effective
usability evaluation
Idea:
Rethink the target measure
from the users point of
view (i.e. development &
user – not sales) :
Usability as a measure
for split testing
11
Idea: Infer quantitative measure of
usability from user interactions.
Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de
Usability-based Split Testing
12
Interface A Interface B
Questionnaire
[WaPPU – Usability	based	A/B-Testing
Speicher,	Both,	Gaedke,	ICWE2014]
Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de
WaPPU
Usability	System
(Naive	Bayes classier for the
learning)
Model
for
Usability
Usability-based Split Testing
13
Interface A Interface B
[WaPPU – Usability	based	A/B-Testing
Speicher,	Both,	Gaedke,	ICWE2014]
Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de
WaPPU
Usability	System
TRAINED
Model
for
Usability
Usability Model allows for:
f(cause) = score
(cause is part of usability)
14Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de
Sure!
f(cause) = score
this function is not bijective
(Not possible to infer optimizations from usability scores )
Limitations?
16Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de
How to overcome the
f(cause) = score
limitations?
17
Catalog of best
practices
a.k.a. Set of causes
and optimizations
18
19
SERP Optimization Suite
(S.O.S.)
WaPPU
for evaluation
Catalog
of best practices
Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de
[S.O.S.:	Does	Your	Search	Engine	Results	Page	(SERP)	Need	Help?
Speicher,	Both,	Gaedke,	CHI 2015]
20
fu (score) = {C, C'}
u = usability item number
C = potential causes
C' = countermeasures
Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de
S.O.S. example: "We have a
problem with Distraction"
21Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de
22Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de
23Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de
27
User Study
• 81	participants	(~62%	male)
• avg.	age	=	31.08	years
• Task:	Solve	a	problem
28
“Find a birthday present for a good
friend that does not cost more
than 50 Euros.”
Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de
29Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de
31Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de
32
Results
old SERP new SERP
informativeness -0.17 â < -0.02 â
understandability 0.34 â < 0.45 â
confusion 0.30 â < 0.38 â
distraction* 0.36 â < 0.62 ä
readability 0.45 â < 0.52 â
information
density*
0.04 â < 0.43 ä
accessibility 0.06 â < 0.07 â
overall usability* 59.86% â < 67.50% ä
Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de
Concluding
thoughts
33
n S.O.S. significantly improved SERP usability
n Approach developed using HCD/DT
n obtaining usability scores through WaPPU and
n recommendations for optimization from a
catalog for suboptimal scores
n http://vsr.informatik.tu-chemnitz.de/demo/SOS
Conclusions
34Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de
n Catalogs for other categories of Web pages
n Creating trained model for web page and site
categories
n Crating more generic and reusable catalogs
n Reducing the learning time
n Applying the approach to the quality of linked
enterprise data
n Improving: incorrect, incomplete etc. data
n Dealing with: Co-evolution, Coherence etc.
n Data Quality à Fit for business use
n LEDS project:
http://www.leds-projekt.de/
Future Work
35Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de
36Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de
Data Quality can	be	interpreted	as	the	degree
to	which	data	fits	to current requirements
𝐷𝑄#$%&'(& =	 ∑ ∑ 𝜔-'. / 𝑓𝑢𝑙𝑓𝑖𝑙𝑙 𝑟𝑒𝑞, 𝑜𝑏𝑗-'.<='>'?< +	𝜑
37Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de
First	Inspiring	Results
Output	of	Fitness	results	
of	our	Quality	Assessment	
tool	with	the	means	of	the	
data	quality	vocabulary	
(dqv)
Feel	free	to	visit:
www.leds-projekt.de
Chemnitz University of Technology #VSR
Thank You!
martin@gaedke.com
VSR.Informatik.TU-Chemnitz.de
@gaedke /gaedke
38Martin	Gaedke	- http://vsr.informatik.tu-chemnitz.de

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