Magento-Extension for personalized Recommendations

Transcription

1 Magento-Extension for personalized Recommendations Sell more products and digital media online Increase your advertising revenue Lower costs for service and support Enhance usability of your website Establish new online business models YOOCHOOSE

2 Expectations have been created by Amazon.com 2

3 All major Online Shops invest heavily in recommendation 3

4 and learn about the best usage scenarios 4

5 Introduce Recommendation Increase Revenue Introduce Recommendation Significant Sales Growth* Sales Units through Recommendation Best Project Result Best Project Management Week after Go-live * Source: Schematic Drawing based on Customer Experience (real Customer data are confidential)

6 6 We challenge established players in the market for professional online recommendations and open the high-quality recommendation market for small and medium size online shops.

7 7 Recommender Engine Principles

8 Not available User Profile Available Available algorithms 8 Combine various methods in different places in order to optimally serve the user. Prerequisite for the use of recommendations Examples Product information (meta-data) Not available Available Statistical recommendations on start page Bestsellers Most frequently read Best-rated Personalized collaborative approach Personalized stererotype approach Hybrid filtering Combination of different algorithms Similar items on detail pages Similar items in terms of meta-data (content-based) Users with the same interests as you were also interested in. Collaborative filtering in terms of purchase, click, download or rating events Statistical approach (bestseller, popular) Collaborative approach (e.g. purchase history) Editorial recommendation Content-based (similarities in meta-data) Simple stereotypes (affinity to similar product clusters) Personalized recommendation on start page Items similar to those you saw over the past few days You may like this Combination of methods depending on profile accuracy and placement

9 Typical Usage Scenarios Filters and recommendations are delivered individually per use case or channel. We call this "Scenarios". 9 Typical Scenarios (not limited) Classical Push Channels Landing page Newsletter Mobile Phone Popularity based e.g. top seller Freqency filter Personalized Editorial mix New articles only scenario ID: 000a scenario ID: 000c scenario ID: 000e Product page Notifications ipad App Collaborative e.g. also bought already owns filter Mixed Category filter Long-tail push scenario ID: 000b scenario ID: 000d scenario ID: 000f

10 Tracking Usage Of course, item recommendations, clusters and models can be calculated via classic collaborative approaches. 10 Events for user behavior (not limited) Typical models (not limited) Users who bought also bought 1 Purchasing event 2 Click event 3 Download event 4 Shopping cart event (save, delete) 5 Rating event (depends on rating) Users who clicked also bought Users who rated also bought Users who clicked also interested Users who clicked also clicked 6 Accessing a category page 7 Transfer user profile (login)

11 Provide Filters Recommendations can be filtered with all product-related metadata and online behaviour of users. 11 Filter No products the user already purchased No top-selling products (top 25) Max. shows of identical recommendations per session 3 Max. age of products in days 90 Products must belong to the same category (context sensitive) Recommend only complementary products from other categories (context sensitive) Exclude products from recommendation (Blacklist) Name of Blacklist upload Product price must be equal/higher than current product (context sensitive) equal/lower than current product (context sensitive) similar (+-25%) to current product (context sensitive)

12 Creating personalized Recommendations 12

13 The stereotype approach Pre-calculated models for recommendation significantly improve engine performance in high-end usage scenarios Stereotypes use the attributes of items 2. Stereotype modeling with usage events S2 S1 Item Cluster S3 click buy 3. Calculation of affinities 4. Recommendation from stereotypes A1 A2 A3 Affinity vector S1 S2 S3 Stereotype cluster Personalized recommendation

14 Personalized Recommendation More Clicks Introduce Recommendation 50% more Clicks in A/B-Test* Clicks w/o Personalized Recommendations Additional Clicks with Recommender Engine % More Clicks Group A Group B * Source: A/B-Test with Schwäbische Zeitung Online (YOOCHOOSE Newsletter 11/2010)

15 15 Impact of personalized recommendations?

16 Recommendation Works! 16 77% of the online shoppers regard recommendations as useful to very useful 54% of the online shoppers have noticed recommendations that are based on their own buying events or that of comparable users 34% out of these 54% have bought recommended products * Source: Coremetrics 2010 with statistics from Forrester

17 Good placement is your key success factor 17 Scenarios for relevant product recommendations Landing Page Category Page Product List Product Pre- Shopping Cart Shopping Cart Order Confirmation Call Center Advertising * Source: Coremetrics 2010

18 What you can expect as a result 18 Locations for relevant product recommendations Landing Page Category Page Product List Product Pre- Shopping Cart + 0,5 % + 1,3 % + 9,0 % + 6,5 % + 1,6 % Shopping Cart Order Confirmation Call Center Advertising * Source: Coremetrics ,3 %

19 19 Integration example: wein-konzept.de

20 wein-konzept.de 20

21 Magento Module Tracking 21 Tracking of click and buy events Data contains Product ID and/or Category ID A unique identifier is assigned to every user Transfer of anonymized data to recommendation server via REST

22 Magento Module Recommendations 22 Blocks for Up-Selling, Cross-Selling and Related Products are extended Manually assigned products are displayed first Additional products are requested from the Recommendation Server via REST User Identifier, Product ID and possibly Category ID are sent Up to 10 products are received, but the number of displayed products is limited Scenarios are predefined: up-selling = also_clicked related = top_selling cross-selling = also_purchased

23 Magento Module Configuration (1) 23

24 Magento Module Configuration (2) 24 Module can be deactivated completely Statistics are updated every hour, plus when an admin logs in Yoochoose access data has to be entered here It is checked on saving; if incorrect or no data is entered, the module won t do anything

25 Magento Module Configuration (3) 25 Single Configuration Section for the different recommendation types Recommendations can be deactivated completely for the starting phase (only event tracking) Scenario can be changed here Manual relations can be displayed or not The number of displayed products can be configured All data is prefilled

26 Magento Module Advanced Usage 26 {{block type="yoochoose/recommendation" template="yoochoose/recommendation.phtml"}} Insert in any CMS Block, CMS Static Page or Product Page You can assign a different template Category ID / Product ID are transferred automatically, if on correct page {{block type="yoochoose/recommendation" template="yoochoose/recommendation.phtml" max_items="4" scenario="top_selling" column_count="4"}} More parameters can be given i.e. select a different scenario here

27 27 How your shop can access the module?

28 Magento Module Installation 28 The module can be downloaded for free Search for yoochoose on MagentoConnect You can get a free yoochoose test account on config.yoochoose.net After install, just enter your access data to start recording events Compatible to Magento 1.3, 1.4.x and 1.5.x

29 Facts & History 29 Executives & founders Dr. Uwe Alkemper & Michael Friedmann International team 9 (Germany) + 3 (Israel) employees Founded during downturn Jan. 20, 2009 Shareholders T-Venture & HTGF Jan Foundation of YOOCHOOSE Sept Foundation of R&D subsidiary in Omer (Israel) Jul Live customers - Musicload - Gamesload - SZON Dec Operational Partnership - Pilot customers live T-Laboratories Research Project R&D Key Customers Partners

30 Contact 30 Dr. Uwe Alkemper Dr. rer.-nat., Dipl.-Phys, MBA YOOCHOOSE GmbH Mobile: Andreas von Studnitz Dipl.-Inform. Magento-Freelancer Mobile: [email protected]