big data in online classifieds
TRANSCRIPT
What data is available in your application domain?
Page viewsUser dataAd placements
Popular products
Number of visits
Device
IP address
Time of browsing
Time spent on site
User behavior Ad
replies
Featured ads
Number of products
Location
Click Through Rates
Purchase history
What does BIG DATA mean for you?
Product details
10M item meta-data
User behaviour
20M user meta-data
1M items 10 parameters per item
x
colourinventory info
price
locationsize
category
2M unique visitors
10 parameters per visitor
x
click
clickssearch
product viewed
page
viewedpurchases
Interactions
ratings ad replies
popular items
popular categoriesgeolocatio
n
User contextual
data
inte
gra
tio
n
Item contextual
data
Catalogue extension
Methods of collecting and distributing user data
COLLECT and REPORT aggregated data of your visitors
USE a RECOMMENDATION system
TRACK each visitor individually
How can it be used for business purposes?
Insight into classified Big Data
Deg
ree
of
insi
gh
t
1st click
2nd click
3rd click
1 week 1 month
1 year
Tracking & data collection
Data analysis
Adequate business response
„Traditional” reactive marketing
Real-time personalization
Item-to-item reco
Price rangeContextDevice
How does personalization work?
+
Recommendation techniques
Content based filtering
Collaborative filtering Recommends products that are
liked by users that have similar taste as the current user
Similarity between users is calculated using the transaction history of users
Domain independent
Recommends additional products with similar properties
1 4 3
4
4 4
4
2
1.4
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0.8
0.5
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-0.4 1.6
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1.2 -0.51.1 -0.4
1.2 0.9
0.4 -0.4
1.2 -0.3
1.3
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0.9
0.4
1.1 -0.2
1.5
0.0
1.1 0.8
-1.2
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1.2 0.9
1.6
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0.5 -0.3
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0.4 -0.20.5 -0.1
0.6
0.2
P
Q
R
1 4 3
4
4 4
4
2
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2.1
0.8
1.0
1.6 1.8
0.7 1.6
0.0
1.4 1.1
0.9 1.9
2.5 -0.3
P
Q
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-0.5 3.5 1.5
1.14.9
What type of data can be used for recommendations?
COLLABORATIVE FILTERING
CONTENT-BASED FILTERING
CONTEXT AWARENESS
SOCIAL RECOMMENDATIONS
Personalized User Journeys – Understand your users and exploit the potential in BIG DATA
• Predicting not just the primary, but the secondary, tertiary, etc. interests
• Apart from history and behavior, focusing on the current context
Based on Interest Seasonality
Ad Replies Holidays
Searches Continuous
Devices used Working hours
Last activity peak Every 3 months, during
weekends
More user action and better user experience impact on your market position and revenue
Generate from 3rd additional party revenues placements
Optimize your marketing spending on ad networks by personalized banners and placements
How can you monetize from recommendations?
Thank you for your attention!
Domonkos Tikk, PhDFounder, CEO, CSOEmail: [email protected] hu.linkedin.com/in/domonkostikk/
Q&A