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Customer Anaytics at Swiss TUG 2009

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  • 1. Customer Analytics is in eBay’s DNA
  • 2. Dr Patrick Déglon Senior Manager, EU Finance eBay International AG – Switzerland Customer Analytics is in eBay’s DNA Teradata User Group Schweiz – June 26th 2009 pdeglon@ebay.com
  • 3. Agenda • Introduction to eBay • Analytics at eBay • Few examples: • Measuring impact of initiatives • Word of mouth and marketing • Onsite Marketing Keyword Targeting • Acquisition & Retention Analysis • Customers Behavior and Internet Marketing 3
  • 4. 1995 – AuctionWeb launches “What I wanted to do was create an efficient market, where regular people could compete with big business on a level playing field. It was a little bit of an experiment.” Pierre Omidyar, founder 4
  • 5. Company Revenues $8.5B 42% CAGR $0.7B 2001 2002 2003 2004 2005 2006 2007 2008 5
  • 6. Today – Leaders in 3 key web activities Trade Pay Communicate #1 in eCommerce #1 in online payments #1 in voice over Internet TM TM 6
  • 7. Our starting point: World’s largest marketplace Buyers • 150M+ unique visitors • 3M items bought daily • 50,000 categories Sellers • 25M+ sellers • 150M+ live listings • Multiple formats Robust and scalable technology platform 7
  • 8. $60 billion in merchandise traded in 2008 ... or $1.2 million every 10 minutes 8
  • 9. eBay: The World's Online Marketplace® every every every 26 2 4 min. min. sec. a Ford Mustang is sold a major appliance is sold a pair of shoes is sold 9
  • 10. Sold or Not Sold? a lunch with Warren Buffett? richest man in the world in 2008 (Forbes) with net worth of $62 billion Sold! for $ 2.1 mio bought by a Chinese hedge-fund manager in 2008 10
  • 11. Still time to buy 2009’s lunch 11
  • 12. Sold or Not Sold? a corn flake shaped like Illinois? Sold! for $1,350 12
  • 13. Agenda • Introduction to eBay • Analytics at eBay • Few examples: • Measuring impact of initiatives • Word of mouth and marketing • Onsite Marketing Keyword Targeting • Acquisition & Retention Analysis • Customers Behavior and Internet Marketing 13
  • 14. Analytics is in eBay DNA: Area for Analytics at eBay example example Marketing Customer Insights Finance Trust & Safety Loyalty Customer Service Search Engine Testing Infrastructure Technology Operations Information Security and many other areas... Finance is owning most of the areas – assuring objectivity and optimal allocation of recourses 14
  • 15. eBay Analytics DW Infrastructure Data Access MicroStrategy Unica Crystal SAS Primary Relational Data SQL SOA/DAL Secondary MPP Relational Data MPP 2.5 PB Teradata MAX 2.2 PB Linux Linux Teradata Wide Area Interconnect 1000 miles Sun Fire 4xxx Solaris Solaris Sun Fire 4xxx 2.2 PB XML, name/value, raw 6.6 PB MPP/HPC/Grid MPP/HPC/Grid XML, name/value, raw Data Integration Ab Initio Informatica GoldenGate UC4 BES MAX SOA 15
  • 16. eBay Analytics Technology Highlights >50 TB/day of new, incremental data >50 PB/day Processed >100k data elements >150^10 new records/day >50k chains of logic >5000 business users & analysts Active/Active turning over a TB every x365 24x7 Millions of queries/day Always online 99.9+% Availability Teradata system 5 seconds Near-Real-time 16
  • 17. DW Sandbox enables agile analytics Analytics teams have access to sandboxes within eBay Teradata data warehouses (~ 100 GB per sandbox): • Enable to keep the “Single analyst’s sandbox Teradata Data Warehouse Point of Truth” philosophy • Improved Time To Market – Days / Weeks vs Months • Enable the business to do agile prototyping • Enable the users to “Fail Fast” – Make it easy to try out new ideas • Eliminate isolated Data Marts 17
  • 18. Analytics Intranet 18
  • 19. Standard Reports Depository 19
  • 20. Excel Reports 20
  • 21. MicroStrategy Reports 21
  • 22. Flash Reports 22
  • 23. Data Warehouse Wiki for Knowledge Sharing 23
  • 24. Multi Column Upload tool for Sandboxes 24
  • 25. Teradata SQL Assistant (aka Queryman) 25
  • 26. Agenda • Introduction to eBay • Analytics at eBay • Few examples: • Measuring impact of initiatives • Word of mouth and marketing • Onsite Marketing Keyword Targeting • Acquisition & Retention Analysis • Customers Behavior and Internet Marketing 26
  • 27. Levers for a village’s marketplace As a market inspector in charge of your local market, what would be your levers? • • • • • • Placement of shops Propose services to shop owners Pricing (fees) – Entrance fee for shop owners – Commission on sales – Services – Entrance fee for visitors Regulations Marketing & CRM – Shop owners – Visitors … Jour de marché à Dreux – Frank Will 27
  • 28. Measuring impact of initiatives A/B test Pre/Post analysis illustrative example (Simulation) illustrative example (Simulation) Number of purchases Number of listings 35,000 Initiative launched 450 400 Impact of the initiative 350 300 test group 200 150 50 0 Aug 1st pre Impact of the initiative 2008 post D 25,000 20,000 250 100 30,000 Initiative launched 15,000 B 2007 C 10,000 control group Sep 1st 5,000 Oct 1st • Randomized Test/Control group methodology is a golden standard in customer insights research 0 Aug 1st A Sep 1st Oct 1st • Used to measure the impact of an initiative in a full market or a market segment 28
  • 29. Agenda • Introduction to eBay • Analytics at eBay • Few examples: • Measuring impact of initiatives • Word of mouth and marketing • Onsite Marketing Keyword Targeting • Acquisition & Retention Analysis • Customers Behavior and Internet Marketing 29
  • 30. Market growth: Simple diffusion model of new products eBay evolution in a market Illustrative Example The number of New Users is proportional to the number of Existing Users and to the number of potential Users left in a market. i.e. the more people are registered… … the more people can encourage their surrounding to join eBay Exponential Adoption (network effect, word-of-mouth) New Users Existing Users Potential New Users Mathematically: ∆N = A ⋅ (NMAX − N) + B ⋅ N ⋅ (NMAX − N) marketing … the less there is people left to join eBay Market Size Limitation (saturation) word-of-mouth ⇒ ∆N = a + b ⋅ N − c ⋅ N2 i.e. New Users (∆N) is a 2nd order polynomial of Existing Users (N), i.e. inversed U shape 30
  • 31. Market growth: Mathematical model Mathematically: ∆N = (A + B ⋅ N) ⋅ (NMAX − N) Dummy example with NMAX = 1 mio (all customers) A = 10-3 (Marketing) B = 10-7 (Word-of-mouth) i.e. (2nd order polynomial) ∆N = A ⋅ (NMAX − N) + B ⋅ N ⋅ (NMAX − N) marketing dN (New Customers) word-of-mouth N (Total Customers in mio) 30,000 1.00 dN (New Customers) 30,000 dN 2nd order polynomial N 25,000 S curve 25,000 0.75 20,000 20,000 Bell curve 15,000 0.50 10,000 15,000 10,000 0.25 5,000 0 Jan 08 5,000 Jan 10 Jan 12 Jan 14 0.00 Jan 16 0 0.00 0.25 0.50 0.75 1.00 N (Total Customers in mio) 31
  • 32. Agenda • Introduction to eBay • Analytics at eBay • Few examples: • Measuring impact of initiatives • Word of mouth and marketing • Onsite Marketing Keyword Targeting • Acquisition & Retention Analysis • Customers Behavior and Internet Marketing 32
  • 33. Example of Internet Marketing: Portals Customers start from their homepage, ... ... click on an IM ad/banner ... and land on our site 33
  • 34. Example of Internet Marketing: Paid Search Customers search on Internet, ... ... click on an IM ad ... and land on our site 34
  • 35. Example of Internet Marketing: Onsite Marketing Example of keyword targeting tests BILLBOARD CTR BY KEYWORD TARGETED CONTENT NikeAir JordanXX3 Tokidoki Handbag Blackberry Pearl Apple iPhone Apple No MacBookAir Keyword • Tested top 100 keywords targeting on homepage with dynamically generated merchandising graphic • Results showed up to 7x click through rate on homepage billboard (varies by keyword), demonstrated lift between 100%-400% across multiple markets and multiple placements 35
  • 36. Agenda • Introduction to eBay • Analytics at eBay • Few examples: • Measuring impact of initiatives • Word of mouth and marketing • Onsite Marketing Keyword Targeting • Acquisition & Retention Analysis • Customers Behavior and Internet Marketing 36
  • 37. Return On Investment (ROI) is a 2-D problem Class (Month of registration) Activity Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug 06 06 06 06 06 06 06 06 06 06 06 06 07 07 07 07 07 07 07 07 Jan 06 Feb 06 Acquisition Revenue Retention Revenue Mar 06 Apr 06 May 06 Jun 06 Jul 06 Aug 06 Sep 06 Oct 06 Nov 06 Dec 06 Jan 07 37
  • 38. Internet Marketing: cost to acquire a new user Amount (Cost & Revenues) Cumulative monthly revenue from new user since acquisition Cost to acquire one new user Cost per new active user = Y Revenue per new active user = Z/month Pay back after X months Months since Registration 38
  • 39. Agenda • Introduction to eBay • Analytics at eBay • Few examples: • Measuring impact of initiatives • Word of mouth and marketing • Onsite Marketing Keyword Targeting • Acquisition & Retention Analysis • Customers Behavior and Internet Marketing 39
  • 40. Bidding behaviors and Internet Marketing Investment Which customer purchases are triggered by a marketing campaign? 2 bids missing Behavioral bid Uncorrelated to IM X days X days bid bid Jan 1st bid Feb 1st IM bid Correlated to IM bid bid click bid bid bid bid click Y days 1 bid is uncorrelated bid bid Mar 1st Y days all bids are incremental 40
  • 41. Latency time for each pair click - bid Negative Latency Bid before Click (no causality) Behavior only Positive Latency Bid after Click (potential causality) Behavior & Internet Marketing impact Number of events (pairs click-bid) Real IM increment (correlated bids) Level of behavioral bids -14 -12 Level of behavioral bids -10 -8 -6 -4 -2 0 2 User click on an ad-banner at time=0 4 6 8 User bid on an eBay item X days later 10 12 14 Latency (days) 41
  • 42. Customer Analytics is in eBay’s DNA Question? Contact details: Patrick Déglon eBay International AG Helvetiastrasse 15/17 3005 Berne pdeglon@ebay.com http://global.ebay.com 42