Ppc keywords discovery search labs sao paulo 2010 pavel dolezal
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CEE & Automation some funny things Reflections & Suggestions
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PPC keyword discovery talks about populating long and detailed lists of high impact key words for large PPC (AdWords, etc.) campaigns and how to au
Transcript of Ppc keywords discovery search labs sao paulo 2010 pavel dolezal
CEE & Automation
some funny things
Reflections & Suggestions
Summary
Central and Eastern Europe
Campaign automation - long tail
Results for clients
Labs - the future
CEE market:
10%
10%
5%
10%
5% 30%
10%
5%
70%
30%
Search market shareCzech republic
Search market shareSlovakia
Search market sharePoland
Search market shareRussia
The World:
Search market sharethe World
Market share - AuctionsCEE
90% ?
That’s some fish !
Campaign Automation
Accounts management
Campaign structure, setup
Systems communication
Bidding
Keywords finding
Copy creation, variations
Dynamic changes in copy
Reporting
Campaign Automation
Accounts management
Campaign structure, setup
Systems communication
Bidding
Keywords finding
Copy creation, variations
Dynamic changes in copy
Reporting
Fish ?
Thousands of items
Great targeting
Cheap CPC
Advanced stage of purchase cycle
Dynamic
Success of a campaign
Success of a campaign
60%
Success of a campaign
60%Keyword selection
Long Tail
Campaign improvement
KPI’s:number of conversionscost per conversion
decline in July = holidaysdecline in December = Xmas, New Year
Keyword Extractor
Keyword list for each industry
Corpus of human-made catalogues
Subtitles corpus
Twitter corpus
Mathematic models = language independent
three-piece suitechildren's roomoffice furnituregarden furniturebuilt-in wardrobeliving room furnituremassive wood furnitureoffice chair... etc
Specific URL’s=
Specific keywords
Categories - Quality Score for categories
Keywords relevance
Category (words, relevance):Apple Inc. (17, 185)Main topic classifications (53, 183)Apple Inc. Hardware (11, 164)Society (44, 157)IPhone (15, 144)CSV with distances
Keywords (relevance):Apple (174494)Iphone (165368)Legal (20880)Twitter (18249)Iphones (17718)people (17091)apple iphone (14765)Jailbreaking (13771)
right (13156)law (12760)iphone apple (8859)person (8424)home (6488)app store (5616)wrong (4076)
Analysis output
Selection = relevance
Google normalized distance
Spectral analysis
Historical data
... and 10 more filters depending on type of campaign
http://www.mechanicalcinderella.com
<?xml "1.0" encoding="windows-1250"?><SHOP>
<SHOPITEM><PRODUCT>Světélkující podložka</PRODUCT><DESCRIPTION>Fosforeskující okraj, nevyžaduje baterie.</DESCRIPTION><URL>http://obchod.cz-pod-mys/fosfor</URL><ITEM_TYPE>new</ITEM_TYPE><DELIVERY_DATE>1</DELIVERY_DATE><IMGURL>http://obchod.cz/obrazky/podlozky-pod-mys/fosfor.jpg</IMGURL><PRICE>620</PRICE><PRICE_VAT>756</PRICE_VAT></SHOPITEM>
</SHOP>
XML feed - campaign data
XML - next steps
Categorize
Create copy templates
Analyze URL
Filter the best converting ones and give them priority
AnalyzeWatch the campaign
Status
AnalyzeWatch the campaign
Status
Results
Status
AnalyzeWatch the campaign
Status
Results
Set KPI’s
Funnel
Visits
Clicks
Behavior on site
Conversions !
ROI
Examples
Aukro.sk
KPI’s:number of conversionscost per conversion
decline in July = holidaysdecline in December = Xmas, New Year
Results
Cost of conversion
30% down
Results
Cost of conversion
30% down
Number of conversions
320% up
Shopping comparison
120.000 items
30 keywords per item
6 variations of copy
Shopping comparison
120.000 items
30 keywords per item
6 variations of copy
520.000 clicks
CPC 0,04 !
Shopping comparison
120.000 items
30 keywords per item
6 variations of copy
520.000 clicks
CPC 0,04 !
(10% of Brazilian online population)
Food for big fish
1.2.500.000 items
1.2.500.000 items
2.real-time updates
Keyword &!copy
keyword extractor
search engine distance
description analysis, etc.
1.
Keyword &!copy
keyword extractor
search engine distance
description analysis, etc.
1. 2.Ad Systems (AdWords)
dynamic copy
QS based preferences
Keyword &!copy
keyword extractor
search engine distance
description analysis, etc.
1. 2.
3.
Ad Systems (AdWords)
dynamic copy
QS based preferences
Media
proprietary auctioning
online reports from all systems, etc.
Process
RSS feed
Categorization
Keyword extraction and pairing
Process
RSS feed
Categorization
Keyword extraction and pairing
Enriched feed/adgroup
Trixam contextual match
Trixam Quality Score
ROI
CTR
Labs
Aboutness: clothing, shop, everyday life, mankind, fashion, business, services, textile industry, culture, protection from weather
Keywords: clothing, dress, t-shirt, sweater, assortment, textile
Keywords - old analyzer: t-shirts, producer, xfer, advertising, sweaters, clothing, customers
Text complexity: complex - b2b
xfer.cz
Twitter Corpus
Czech tweets (language and location)
geo-location and time-based influence of language deviations
btw. did you know that people are in better mood on Tuesdays than on Fridays?
combined with Aboutness allows creation of Twitter stream on a certain topic
combined with mood analysis can find out positive or negative feeling towards a topic or brand
can generate local news
“The Sparrow”
MADONNAIN PRAGUE13. 8. 2009
“Madonna” - Google search August 2009
“Madonna” - Czech Twitter August 2009
Sometimes
Twitter is quicker
and can predict
future searches
RAMMSTEININ OSTRAVA
17. 9. 2009
“Rammstein” - Google search September 2009
“Rammstein” - Czech Twitter September 2009
Credits:Milena FridmanovaJan BednarJosef Slerka
Thanks for attention
Pavel [email protected]
@pabu01
http://www.ataxo.com
