Good bye conversion rate - a smarter way to optimising Search Engine Result Pages

Faculty of Computer Science Professorship Distributed and self-organizing Systems

Good bye conversion ratea smarter way to optimising Search Engine Results Pages

MartinGaedke.comVSR.Informatik.TU-Chemnitz.de

///// IS-SWIS 2017, Rotterdam, February 9, 2017///

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SearchEngine

SearchSearch

Search

Search

MartinGaedke- http://vsr.informatik.tu-chemnitz.de

Search Engine Result Pages (SERPs)

3MartinGaedke- http://vsr.informatik.tu-chemnitz.de

(simplified) Motivation

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Evaluation Method Efficient? Effective?

User Testing ✖ ✔

Usability Inspection ✖ ✔

Split Testing ✔ ✖


Split Testing

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Buy this Buy this


VersionA VersionB

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


(source:Wikipedia.com)

Version A: Getting the User‘s Zip Code


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$$$ ≠ Usability

Jakob Nielsen: Putting A/B Testing in Its Place, http://www.nngroup.com/articles/putting-ab-testing-in-its-place/


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

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Idea: Infer quantitative measure ofusability from user interactions.


Usability-based Split Testing

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Interface A Interface B

Questionnaire

[WaPPU – UsabilitybasedA/B-TestingSpeicher,Both,Gaedke,ICWE2014]


WaPPUUsabilitySystem

(NaiveBayes classier for thelearning)

Modelfor

Usability

Usability-based Split Testing

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Interface A Interface B

[WaPPU – UsabilitybasedA/B-TestingSpeicher,Both,Gaedke,ICWE2014]


WaPPUUsabilitySystem

TRAINEDModel

forUsability

Usability Model allows for:f(cause) = score

(cause is part of usability)


Sure!

f(cause) = score this function is not bijective

(Not possible to infer optimizations from usability scores )

Limitations?


How to overcome thef(cause) = score limitations?

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Catalog of best practices

a.k.a. Set of causes and optimizations

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SERP Optimization Suite (S.O.S.)

WaPPUfor evaluation

Catalogof best practices


[S.O.S.:DoesYourSearchEngineResultsPage(SERP)NeedHelp?Speicher,Both,Gaedke,CHI 2015]

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fu (score) = {C, C'}

u = usability item numberC = potential causesC' = countermeasures


S.O.S. example: "We have a problem with Distraction"


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User Study• 81participants(~62%male)• avg.age=31.08years• Task:Solveaproblem

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“Find a birthday present for a goodfriend that does not cost more

than 50 Euros.”


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Resultsold 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


Concluding thoughts

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n S.O.S. significantly improved SERP usabilityn Approach developed using HCD/DT

n obtaining usability scores through WaPPU andn recommendations for optimization from a

catalog for suboptimal scoresn http://vsr.informatik.tu-chemnitz.de/demo/SOS

Conclusions


n Catalogs for other categories of Web pagesn Creating trained model for web page and site

categoriesn Crating more generic and reusable catalogsn Reducing the learning time

n Applying the approach to the quality of linked enterprise data

n Improving: incorrect, incomplete etc. datan Dealing with: Co-evolution, Coherence etc.n Data Quality à Fit for business usen LEDS project:

http://www.leds-projekt.de/

Future Work



Data Quality canbeinterpretedasthedegreetowhichdatafitsto current requirements

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�='>'?< +𝜑


FirstInspiringResultsOutputofFitnessresultsofourQualityAssessmenttoolwiththemeansofthedataqualityvocabulary(dqv)

Feelfreetovisit:www.leds-projekt.de

Chemnitz University of Technology #VSR

Thank You!

[email protected]

VSR.Informatik.TU-Chemnitz.de

@gaedke /gaedke


Good bye conversion rate - a smarter way to optimising Search Engine Result Pages

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