1. Webshop personalization
How product recommendations can
increase your success in online sales
9th webinar of the retail ecommerce series
an embitel initiative
1st December 2011
Better eCommerce 2010 Embitel

2. Speaker
Founder
dmc digital media center GmbH,
Germany
www.dmc.de
Chairman
Embitel, India
www.embitel.com
Daniel Rebhorn
dr@dmc.de
• Studied Computer Science at University of Stuttgart
• Entrepreneur since 1992
• Investor and business angel for 5+ IT companies
• Working in retail e-Commerce for last 16 years
• Responsible for development of e-retail sites like Neckermann, Kodak
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3. *) NO real recommendation. Merged from 2 different ones.
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4. Agenda
• Basic understanding of current challenges
• Why and where to use recommendations?
• How personalization works? Successfully!
• Overview of solution approaches and how to choose a solution
• Future of personalization in the web
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5. Basic understanding of current challenges
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6. Key drivers for conversion rate
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7. Sample calculation
• Assuming
– Running a Web-Shop with 50,000 Visitors per month
– Average ticket size (shopping cart) of 1,000 INR
• With a conversion rate of 2.5%
– Total revenue of 12.5 Lakh INR
• Increase of conversion rate of 0.75% to 3.25%
– Increase of revenue to 16.25 Lakh INR or 30%
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8. Analysis of conversion rates (sample date)
50%
45% 43%
40%
35%
30%
25%
21%
20% 19%
15%
10% 8% 7%
5% 3%
0%
below1%
unter 1% 1,0% - 2,9%
1.0% - 2.9% 3.0% - 4,9%
3,0% - 4.9% 5,0% - 7,9 %
5.0% - 7.9% 88% -- 20%
% 20% >>20%
20 %
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9. Analysis of conversion rates (sample date)
Wide range of products
50%
45% 43%
40%
35%
30%
25%
21%
20% 19%
niche portfolio
15%
10% 8% 7%
5% 3%
0%
below1%
unter 1% 1,0% - 2,9%
1.0% - 2.9% 3.0% - 4,9%
3,0% - 4.9% 5,0% - 7,9 %
5.0% - 7.9% 88% -- 20%
% 20% >>20%
20 %
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10. “If I have 3 million customers on the Web,
I should have 3 million stores on the Web.”
(Jeff Bezos)
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11. Why and where to use
recommendations?
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12. Why personalization ?
• amazon generates 20%+ more revenues via recommendations
• Average increase of revenues with recommendations: 5-25%
• Increase of ratings & reviews by 10 times using personalized emails
• 100% increase in sales through personalized newsletters
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13. Why personalization ?
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14. Where to do personalization ?
Product detail page
100% Product overview page
Checkout / Kauf
Search engines
11-48%
Shopping cart
External sites
Direct access
Homepage
Newsletter
Product search
8-36%
Product inspirations
Product recommendations 1.6-17%
0.8-6.5%
50-90%
50-70%
40-50%
20-30%
Exit / Drop out! Exit / Drop out! Exit / Drop out!Drop out!
Exit /
Conversion Rate: 0.8 – 6.5%
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15. Where to do personalization ?
50 – 90% immediately leaving on Homepage
20 – 30 % leaving on product overview page
50 – 70% leaving on product detail page
Why let them go, if there are solutions ???
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16. Where to do personalization ?
Priority of optimization
1. Homepage and landing pages (up to 200% increase in CR !!!)
2. Product overview / shop navigation
3. Onsite search results
4. Newsletter
5. Product detail pages
6. Shopping cart Our suggestion:
Continously track,
analyse and optimize
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17. Product detail page Animated images
Brand overview Product overview page
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18. How personalization works ?
Successfully!
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19. Classification
known
Context oriented Personalized & context-oriented
Context
recommendations Recommendations
unknown
Personalized recommendations
Generic recommendations
Unknown Known
User profile
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20. Classification
• Frequently bought
• Show related together
categories in • Your friend also
search result bought X from
• Customers category
bought X, also
bought Y
known
Context oriented Personalized & context-oriented
Context
recommendations Recommendations
unknown
Personalized recommendations
• Most popular Generic recommendations
products
• New product
Unknown • last time you„ve
releases Known
seen, this …
• Recently sold User profile
• Need accessories
for your last
purchase?
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21. Case -1: Flipkart.com
On the category page
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22. Recommendations, Cross-Selling, Up-Selling
•20-30%
•Exit / Drop out!
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23. Case -2: flipkart.com
On the product detail page
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24. Case -2: babyoye.com
Based on sales data
Based on product data
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25. Case -2: babyoye.com
Based on content data
Based on sales data
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26. Case -3: letsbuy.com
On the product detail page
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27. Case -3: letsbuy.com
On the product detail page
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28. Data sources
• Explicit data (directly given by user)
– Preferences given by user
– Ratings and reviews
– Social media profile
– Order history
• Implicit data (indirectly given by user)
– Surf behavior (previous or real-time, e.g. „products browsed“)
– Context (Search term, click-path, current shopping cart)
– Response on online marketing
– Order history
• Other data
– Demographics
– Products & Content
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29. Product & content data
Context
Navigation
Recommendations
Customer
segmentation, Product data
Demographic data
Historical data Category data
Recommendation
system
(data, configuration
User account and rules) Content data
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30. 2 kinds of recommendation systems
Click stream based systems Repeat buying systems
- real time data - Historical data
- real time output - Pre-rendered output
- self-adopting behavior - Asynchronous integration
- API integration required - Easier to configure/maintain
- high dependencies - Slow reaction time
- Minimized data - Huge data
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31. Approaches in Pre-Sales phase
Product information
Click stream Shoping cart
based systems
Repeat buying
systems
Checkout
Sale
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32. Approaches in Post-Sales phase
Newsletter
Shoping cart
Click stream
Repeat buying
based systems
systems
Checkout
Sale
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33. Repeat buying system – shopping cart analysis
Mathematical Modeling to improve Targeting
1 2 3
Marketing
Machine
Customer 1 Predict next likely product to buy
Predict customer value potential
Customer 2
66% =
Predict customers likely to churn
Customer 3 ? 33% =
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34. Repeat buying system – shopping cart analysis
Challenges
 Large product portfolio  in 60% + of cases
the recommendation is inaccurate
 Changes in product portfolio
 Long-Tail effect  in > 20% + of cases
Topseller are shown
 Long learning time (self fulfilling prophecy)
Solutions
 Adding historical data
 Adding personalization
 e.g. shopping cart analysis within a segmented consumer cluster
 Change of scenarios
 e.g "customers who bought X, also browsed Y"
 Manual overwrite
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35. Manual overwrite
Keep options in mind!
• Why?
– Temporary promotion of specific products/categories
– Prevent inappropriate combinations
• Criterias
– Product attributes (e.g. categories, Price, Color)
– User profiles (e.g. Gender, Revenue history)
– Time (e.g. daytime, month, season)
– etc.
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36. Overview of solution
approaches and how to
choose a solution
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37. Checklist for software
 Support for various recommendation types (lists, banner control, newsletter)
 Self learning system, minimized manual effort
 Gives recommendation even after big changes in product portfolio
 Allows manual overwrite
 Easy to configure: rules, filters, other logic
 High performance and scalability
 Integrated performance tracking and analysis (e.g. A/B test integration)
 Able to handle multi-category-assignments of products
 Able to handle situation of "sparse data" (e.g. in long tail and new products releases
 Support of multiple channels (e.g. in call center, mobile app, POS, etc.)
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38. Available software products
• ATG / Oracle
(www.oracle.com/us/products/applications/atg/index.html)
• Baynote (www.baynote.com)
• SDL / Fredhooper (www.fredhopper.com)
• Certona (www.certona.com)
• prudsys (www.prudsys.com)
• Epoq (www.epoq.de)
• Istobe (istobe.com/product-recommendations.html)
• Avail (www.avail.net)
• Prediggio (web.prediggo.com/product-targeting.html)
• Omikron Fact-Finder (www.fact-finder.com)
• 4-tell (www.4-tell.com)
• Personyze (www.personyze.com)
• Strands (recommender.strands.com/tour)
• youchoose (www.yoochoose.com)
• EasyRec (www.easyrec.org) , open source !!!
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39. Future of personalization in the web
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40. Profiling
Dynamic navigation
Combinded with user generated
recommendations
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41. Fredhopper: Product retargeting in newsletter
User leaves your website…
…and gets your newsletter
•Your-shop.com suggestions
•promotion@your-shop.com
•Your-shop.com created a new suggestions based on the articles you
bought and visited earlier.
•Robert Cavalli
•290 EUR
•m
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42. Adding social recommendations
Analyzing user profile and friend profiles
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43. In other channels and domains
• Why not use personalized recommendations for within your TV ?
• Recommend restaurant based on your location ?
• Recommend (external) service offerings for products ?
(e.g. individual configuration of PC)
• Include recommendations in banking portal ?
(www.sify.com/news/citibank-enhances-its-online-banking-news-business-litkJDajggg.html)
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44. Social Recommendation Engine
Answers questions like:
• Customers who bought this also bought that
• People from my city also bought that
• Interesting products for today (weather,
breaking news, birthday of a friend, ...)
• My friends like
• Other customers with my interests also like
• My best friend likes
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45. Summary
Recommendation works best on
home page and landing pages.
Also keep advantages for
post sales activities in mind.
Check software solutions thoroughly,
and test drive the solutions.
Recommendation can solve your conversion problem
while increasing sales by over 25%.
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46. Our Company
• E-Commerce service company • 350+ employees in
since 1995 Stuttgart (HQ) and Berlin, Germany
• e-commerce projects in • 125+ employees in Bangalore
30+ countries (incl. Europe, US, • Offering Online Commerce services
Australia, India, Japan) in India and overseas
• Responsible for … – consulting
– 100+ Webshops – design
– 1.000.000.000+ USD – technology
E-Commerce Order Volume/year – hosting
– 5.000.000+ – shop management
E-Commerce Transactions/year – online marketing (SEO, SEM, SMM)
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47. List of our webinars
• # 1: E-Retailing - A perfect storm in India
• # 2: Essence of Retail e-Commerce and its optimization
• # 3: SEO - More Visibility, More Traffic & More Sales for free?
• # 4: Social Media Marketing
• # 5: Customer Acquisition & Retention
• # 6: Mobile Commerce for Retailers
• # 7: Online Retailing using facebook
• # 8: Multi-Channel Retailing
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48. Thank you for your interest!
Any questions?
Daniel Rebhorn
dr@dmc.de
www.xing.to/dr
www.linkedin.com/in/danielrebhorn
embitel Technologies (India) Pvt Ltd.
www.embitel.com
www.smarte-commerce.com
www.linkedin.com/companies/embitel
www.facebook.com/EmbitelTechnologies
www.twitter.com/embitel
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49. Backup – references & links
http://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf
http://www.vcbytes.com/tag/recommendation-engine
http://blog.sematext.com/tag/recommendation-engine/
http://en.wikipedia.org/wiki/Collaborative_filtering
http://www.readwriteweb.com/archives/5_problems_of_recommender_sy
stems.php
http://www.tvgenius.net/solutions/recommendations-engine-tv-video/
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