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1、<p><p>&lt;p&gt;&amp;lt;p&amp;gt;&amp;amp;lt;p&amp;amp;gt;10 February 200910 February 2009 Personalized marketing using recommendation systems Peter A. Csikos Although the total population is 10 million the number of people actually
2、doing anything on the net is low Source: GKINET / Hungarian Association for e-commerce SOME FACTS ABOUT THE HUNGARIAN E- COMMERCE SPACE People who have bought anything over the internet in 2009 Number of people actually using the internet for at least one hour per month (2010 Q1) Based on 2005 Micro
3、census ONLINE SHOPPING AND E-COMMERCE BETWEEN 2001 AND 2010 Ratio of people actually doing e-commerce and those who are planning to do so and those who are even not planning to do it Source: Hungarian Association of e-commerce 1.075.000 Not even planning to use e-commerce Planning to do e- commerce
4、in the future Did e-commerce WHAT ARE THESE PEOPLE ACTUALLY DOING ON THE INTERNET? Online Banking Doing administrative things Gathering information before regular shopping General Information Regularly &amp;amp;amp;#169; GKINET / Association of e-commerce TURNOVER OF ONLINE SHOPS IN HUNGARY
5、BETWEEN 2001 AND 2010 EUR 500million EUR 350 million PlannedActual &amp;amp;amp;#169; GKINET / Association of e-commerce Holtzbrinck Networks Allegro Hungary H&amp;amp;amp;#237;rek M&amp;amp;amp;#233;dia Lapcom CEMP HVG (WAZ) origo Sanoma General Media Arkon Zrt. Ebolt Kft. E
6、BOLT.HU Extreme Digital EXTREMEDIGITAL.HU Viala Kft. GRoby Groby.hu Key players on the Hungarian e- commerce market Differences in motivation TYPICAL HUNGARIAN PROBLEM FOR E-COMMERCE STARTUPS: GETTING THE DEVELOPERS CLOSE TO THE INDUSTRY Focus, Focus, Focus IT excellence freedom of development fame;
7、 IT cups, contests Publication, open source ROI, NPV, IRR New Products Market share, market size official regulations &amp;amp;amp;#169; Norbert, Buzas, ValDeal / University of Szeged ?Domain: Personalized Recommendation Solutions for E- Commerce, Digital Media and IPTV/Digital Cable Service
8、 Providers ?Our offering: Delivering real time, personalized context- based product recommendations to each and every user, the Gravity solution produces measurable increase in revenues from existing customer bases and improves customer satisfaction ?A fast-growing company: significant number of cus
9、tomers and several pilot projects running including ALEXA top35 video live streaming site, 5 to 30 employees within 12 months ?Investors: US and Hungarian investors, offices in Budapest, London, Utrecht and Berlin, sales representatives in Romania, Russia and Israel and China Business Card Gravity R
10、&amp;amp;amp;amp;D 8 E-commerce market trends 9 The problem 10 Market trends 11 ?Personal product recommendations are one of the TOP5 most important emerging technology priorities in e-commerce (Internet Retailer, ?Emerging Technology” conducted by Vovici Corp, Sept 2008) ?50% of online reta
11、il stores want to introduce personal recommendations (Internet Retailer, ?Emerging Technology” conducted by Vovici Corp, Sept 2008) ?40% of US e-commerce sites plan to adopt customer reviews and ratings (The Kelsey Group and ConStat Inc, ?Local commerce monitor”, Aug 2008) ?77% of retail executives
12、said that online behavior tracking is the most promising website technology for online customer engagement (Retail Systems Research, ?Playing Well with Others: eCommerces Evolving Role in the Customer Experience”, Aug 2008) ?76% of US e-commerce executives said the personalized recommendation may or
13、 definitely increase loyalty (The Executive Guide to Captivating Customers, June 2008) ?Social shopping sites and collaborative technologies are deemed currently as leading shopping innovations and expected to be widespread by 2015 (US household shoppers prediction) (TNS Retail Forward, ?New Future
14、in Store”, May 2008) Internet-based shopping and media consumption differs from the classical one In-store, personal shopping experience &amp;amp;amp;#167; Customers know how to find the desired products &amp;amp;amp;#167; Assistances help the customer: show them the desired products
15、 &amp;amp;amp;amp; recommend Internet shopping experience &amp;amp;amp;#167; Web portals offers much more products, customers get lost easily &amp;amp;amp;#167; Recommendation systems helps the customer to find &amp;amp;amp;amp; discover products The key is to keep th
16、e customers active &amp;amp;amp;amp; increase conversion &amp;amp;amp;#167; 20% of the customers is accountable for 80% of the profits &amp;amp;amp;#167; To gain new customers, companies have to spend 3-4X more than to keep the existing ones THE EMERGENCE OF RECOMMENDATIONS S
17、olution: Change the way you personalize 13 WHAT IS A RECOMMENDATION ENGINE? 14 Recommender systems or recommendation engines form a specific type of information filtering system technique that attempts to present information items (films, television, video on demand, music, books, news, images, web
18、pages, etc.) that are likely of interest to the user. HOW DOES IT WORK? 15 WHAT IT DOES? 16 ?Data collection and processing ?Relevance &amp;amp;amp;amp; preference ordering ?Display recommendations ?Self-learning &amp;amp;amp;amp; improving capabilities Recommender logic ?Mathematical models ?Information systematization THE RECOMMENDATIONS 17 Customer is looking for a product Receive personal offerings: Receive ti&amp;amp;lt;/p&amp;amp;gt;&amp;lt;/p&amp;gt;&lt;/p&gt;</p></p>
