As an ecommerce site with more than 800,000 different sellers, Etsy is particularly interested in understanding how shoppers find the items they seek. Part of this understanding involves attributing successful purchases to specific features on the site. This attribution model allows us to compare and refine Etsy’s features, but also provides valuable signals for A/B testing, search quality, and recommenders. However, the path to a successful handmade purchase often involves multiple features over the course of several visits. This talk will discuss the challenges of funnel analysis at Etsy and the corresponding deficiencies of several widely used web analytics tools. We’ll then dive into our event sequence matching tool, which we’ve successfully applied to hundreds of millions of visits in a single Hadoop job and is widely used across our big data stack. Finally, we’ll take a look at some of our applications of the tool and compare it to related work.

1. Funnel Analysis in Hadoop at
Etsy
Steve Mardenfeld, Wil Stuckey, Matt Walker
Tuesday, March 12, 13

2. Tuesday, March 12, 13

3. Handmade MarketPlace
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4. Tuesday, March 12, 13

5. Data Driven Development
• Use data to make informed decisions
• Use data to evaluate the efficacy of our products
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6. Funnel Analysis
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7. Registration Funnel
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8. Registration Funnel
50,000 10,000
100% 20%
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9. Registration Funnel
50,000 45,000 15,000 10,000
100% 90% 33% 60%
100% 90% 29% 20%
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10. Funnels ++
• Funnels are more than just an optimization tool
• Use them to understand different pathways throughout our
site
• Partition and compare these pathways by attributes
• A/B tests, categories, queries, cohorts
• Attribution model
Tuesday, March 12, 13

11. Attribution Models
• Tie conversions and successes to specific products and
actions
• Use to gain understanding of our users’ interaction with Etsy
• Help us to measure gains in A/B testing
• Easily compare different varieties of the same product
• Attribution techniques for internal and external attribution
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12. Attributions Illustrated
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13. Attributions Illustrated
• last product interaction - browse
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14. Attributions Illustrated
• last product interaction - browse
• co-occurrence - search and browse
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15. Attributions Illustrated
• last product interaction - browse
• co-occurrence - search and browse
• direct funnel attribution - search
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16. Indirect Attribution Illustrated
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17. Indirect Attribution Illustrated
• direct funnel attribution - shop home
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18. Indirect Attribution Illustrated
• direct funnel attribution - shop home
• indirect funnel attribution - search
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19. Aggregate Results
Direct
Search Clicked Listing Purchased
Events 50,000 25,000 5,000
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20. Aggregate Results
Direct
Search Clicked Listing Purchased
Events 50,000 25,000 5,000
Indirect
Search Clicked Listing Shop Clicked Listing Purchased
Events 50,000 25,000 10,000 5,000 3,000
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21. Aggregate Results
Direct
Search Clicked Listing Purchased
Events 50,000 25,000 5,000
Indirect
Search Clicked Listing Shop Clicked Listing Purchased
Events 50,000 25,000 10,000 5,000 3,000
Combined
Search Clicked Listing Purchased
Events 50,000 25,000 8,000
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22. Segmenting Within Funnels
Old Algorithm
Search Clicked Listing Purchased
Counts 50,000 20,000 15,000
New Hotness
Search Clicked Listing Purchased
Counts 100,000 60,000 15,000
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23. Segmenting Within Funnels
Old Algorithm
Search Clicked Listing Purchased
Step 100% 50% 40%
To t a l 100% 50% 20%
New Hotness
Search Clicked Listing Purchased
Step 100% 60% 25%
To t a l 100% 60% 15%
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24. Segmenting Across Funnels
* Clicked Listing Purchased
Search 100% 50% 40%
Browse 100% 40% 30%
Home 100% 60% 36%
Activity Feed 100% 62% 28%
Ta s t e Te s t 100% 47% 31%
Search Ads 100% 45% 38%
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25. Democratized Funnels
How do we make this awesomeness available for everyone?
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26. Tuesday, March 12, 13

27. Awesome infrastructure but...
• Must be an engineer to write your own queries
• Engineering resources become the bottleneck
• Hard to scale as the company grows
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28. So we want to:
• Allow the data engineers to focus on higher priority things.
• Allow people to answer their own questions.
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29. How do we do this?
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30. The Path of Funnel Tools
google spreadsheets internal tools
• Funnel Cake
• Feature Funnel
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31. Funnel Cake
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32. Version 1
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33. Real Time
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34. Lets do it in PHP!
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35. Why?
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36. It’s the “Etsy Way”
• Already have existing infrastructure
• Operationally stable
• The same tools everyone else is using means a better adoption rate
across Engineering
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37. • Event Stream
• Code runs on every page view
• Simple matching system
• (ab)Used memcached as a temporary storage
• Rolled up to DB every min (near real time)
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38. What happened?
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39. Broader adoption!
Over 75 Funnels were created
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40. Shiny Visualized Results
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41. • Funnels had to be setup ahead of time (no backfills)
• Reconciliation is hard
• Limited to events in our web clickstream (ios/android would be
excluded)
• Scaling and Operational issues
• Difficult to maintain multiple stacks
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42. Turns out that we don’t make
Product decisions in real time
http://mcfunley.com/whom-the-gods-would-destroy-they-first-give-real-time-analytics
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43. Version 2
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44. Tuesday, March 12, 13

45. Getting back on the elephant
• Able to carry over the User Interface from v1
• Standardized event sessionization
• Operationally supported infrastructure
• Nightly batch process
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46. Feature Funnel
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47. Attribution for ALL
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48. Built in metrics
• Click through rate
• Listing impressions
• Purchase rate
-
• Purchase from shop rate
• Favorite from shop rate
• More...
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49. Built in metrics
• Click through rate - Direct Attribution
• Listing impressions - Direct Attribution
• Purchase rate - Direct Attribution
• Purchase from shop rate - Indirect Attribution
• Favorite from shop rate - Indirect Attribution
• More... -
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50. Segmentation
• Primarily used for A/B test analysis
• Arbitrary segmentation
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51. How do we get it?
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52. How do we get it?
select (clicks/visits * 100.0) as "CTR"
from feature_funnel
where event_type = 'search'
and ab_test = 'sitewide'
and epoch_s = 1361318400
and group_name = 'ALL_GROUPINGS';
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53. The Future?
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54. Features
• Eliminate the need for an engineer to write the queries
• Robust segmentation
• Not be limited to visit sessions
• Run Ad Hoc queries
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55. Build your own Funnels
show the builder ui here
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56. Mechanics of Funnel Analysis
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57. Input
• Event sequences
• Typically visits to website
• Sessionization scheme left to developer
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58. Tuesday, March 12, 13

59. Funnel
• Search
• Listing
• Cart
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60. Tuesday, March 12, 13

61. Tuesday, March 12, 13

62. Tuesday, March 12, 13

63. Tuesday, March 12, 13

64. Tuesday, March 12, 13

65. Query
• Only discussed event types
• Funnel steps require additional constraints:
• Listing referred by search
• Added that listing to cart
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66. Search
query “dinosaur”
listing_ids 119469855, 90583707, ...
“http://www.etsy.com/search/handmade/patterns?
loc q=dinosaur&order=most_relevant&view_type=gallery&ship_to=ZZ”
... ...
Tuesday, March 12, 13

67. Listing
listing_id 119469855
“http://www.etsy.com/search/handmade/patterns?
ref q=dinosaur&order=most_relevant&view_type=gallery&ship_to=ZZ”
shop_id 7415158
... ...
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68. Cart
added_listing_id 119469855
cart_listing_ids 119469855, ...
cart_type guest
... ...
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69. Query
• Listing referred by search
• Added that listing to cart
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70. Search
query “dinosaur”
listing_ids 119469855, 90583707, ...
“http://www.etsy.com/search/handmade/patterns?
loc q=dinosaur&order=most_relevant&view_type=gallery&ship_to=ZZ”
... ...
Tuesday, March 12, 13

71. Listing
listing_id 119469855
“http://www.etsy.com/search/handmade/patterns?
ref q=dinosaur&order=most_relevant&view_type=gallery&ship_to=ZZ”
shop_id 7415158
... ...
Tuesday, March 12, 13

72. Listing
listing_id 119469855
“http://www.etsy.com/search/handmade/patterns?
ref q=dinosaur&order=most_relevant&view_type=gallery&ship_to=ZZ”
shop_id 7415158
... ...
listing.ref == search.loc
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73. Cart
added_listing_id 119469855
cart_listing_ids 119469855, ...
cart_type guest
... ...
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74. Cart
added_listing_id 119469855
cart_listing_ids 119469855, ...
cart_type guest
... ...
cart.added_listing_id == listing.listing_id
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75. Query
• Search
• Listing & Referred
• Cart & AddedListing
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76. Pattern Matching
• Apply query to event sequence
• Select out tuples of matching events
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77. What About Incomplete Funnels?
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78. Tuesday, March 12, 13

79. null
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80. null null
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81. Funnel Analysis
• Replace events with 1
• Replace nulls with 0
• Sum
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82. null
null null
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83. 1 1 1
1 1 null
1 null null
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84. 1 1 1
1 1 0
1 0 0
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85. 3 2 1
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86. min(3, 1) min(2, 1) min(1, 1)
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87. 1 1 1
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88. What Do We Keep?
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89. Tuesday, March 12, 13

90. Search Listing Cart
query “dinosaur” listing_id 119469855 added_listing_id 119469855
“http://www.etsy.com/search/handmade/patterns?
listing_ids 119469855, 90583707, ... ref q=dinosaur&order=most_relevant&view_type=gal cart_listing_ids 119469855, ...
lery&ship_to=ZZ”
“http://www.etsy.com/search/handmade/patterns?
loc q=dinosaur&order=most_relevant&view_type=gal shop_id 7415158 cart_type guest
lery&ship_to=ZZ”
... ... ... ... ... ...
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91. Tuesday, March 12, 13

92. Segmenting Funnels
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93. Tuesday, March 12, 13

94. Search Listing Cart
query “dinosaur” listing_id 119469855 added_listing_id 119469855
“http://www.etsy.com/search/handmade/patterns?
listing_ids 119469855, 90583707, ... ref q=dinosaur&order=most_relevant&view_type=gal cart_listing_ids 119469855, ...
lery&ship_to=ZZ”
“http://www.etsy.com/search/handmade/patterns?
loc q=dinosaur&order=most_relevant&view_type=gal shop_id 7415158 cart_type guest
lery&ship_to=ZZ”
... ... ... ... ... ...
Tuesday, March 12, 13

95. Listing Cart
listing_id 119469855 added_listing_id 119469855
“http://www.etsy.com/search/handmade/patterns?
ref q=dinosaur&order=most_relevant&view_type=gal cart_listing_ids 119469855, ...
lery&ship_to=ZZ”
query
shop_id 7415158 cart_type guest
... ... ... ...
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96. Cart
added_listing_id 119469855
cart_listing_ids 119469855, ...
query listing_id
cart_type guest
... ...
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97. query listing_id cart_type
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98. Segmented Funnel Analysis
• Extract segmenting properties
• Compute indicators as before
• Group on segmenting properties and sum
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99. MapReduce
• Work is done map-side
• Common first step in our jobs
• Expensive computation limited to first round mappers
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100. Event Sequence Pattern Matching
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101. Components
• Predicate: matches/rejects events
• Query: tuple of predicates
• Match: tuple of events
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102. Match Predicates
• Select an event based on:
• Full event sequence
• Prior matched events
• Current candidate
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103. Match Predicate DSL
• Combine predicates with logical operators
• val Query = Seq(Search, Listing & Referred, Cart & AddedListing)
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104. Semantics
• Fixed number of events in match
• Arbitrary number of matches per sequence
• Collect and extend all partial matches
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105. Tuesday, March 12, 13

106. Search Listing Shop
Listing Cart Home
Search Listing Search
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107. Search, Listing, Shop, Listing, Cart, Home, Search, Listing, Search
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108. Search, Listing, Shop, Listing, Cart, Home, Search, Listing, Search
Query Root
Search
Listing & Referred
Cart & AddedListing
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109. Search, Listing, Shop, Listing, Cart, Home, Search, Listing, Search
Query Root
?
Search
Listing & Referred
Cart & AddedListing
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110. Listing, Shop, Listing, Cart, Home, Search, Listing, Search
Query Root
Search Search
Listing & Referred
Cart & AddedListing
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111. Listing, Shop, Listing, Cart, Home, Search, Listing, Search
Query Root
?
Search Search
?
Listing & Referred
Cart & AddedListing
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112. Shop, Listing, Cart, Home, Search, Listing, Search
Query Root
Search Search
Listing & Referred Listing
Cart & AddedListing
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113. Shop, Listing, Cart, Home, Search, Listing, Search
Query Root
?
Search Search
?
Listing & Referred Listing
?
Cart & AddedListing
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114. Listing, Cart, Home, Search, Listing, Search
Query Root
Search Search
Listing & Referred Listing
Cart & AddedListing
Tuesday, March 12, 13

115. Listing, Cart, Home, Search, Listing, Search
Query Root
?
Search Search
?
Listing & Referred Listing
?
Cart & AddedListing
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116. Cart, Home, Search, Listing, Search
Query Root
Search Search
Listing & Referred Listing
Cart & AddedListing
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117. Cart, Home, Search, Listing, Search
Query Root
?
Search Search
?
Listing & Referred Listing
?
Cart & AddedListing
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118. Home, Search, Listing, Search
Query Root
Search Search
Listing & Referred Listing
Cart & AddedListing Cart
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119. Home, Search, Listing, Search
Query Root
?
Search Search
?
Listing & Referred Listing
?
Cart & AddedListing Cart
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120. Search, Listing, Search
Query Root
Search Search
Listing & Referred Listing
Cart & AddedListing Cart
Tuesday, March 12, 13

121. Search, Listing, Search
Query Root
?
Search Search
?
Listing & Referred Listing
?
Cart & AddedListing Cart
Tuesday, March 12, 13

122. Listing, Search
Query Root
Search Search Search
Listing & Referred Listing
Cart & AddedListing Cart
Tuesday, March 12, 13

123. Listing, Search
Query Root
?
Search Search Search
? ?
Listing & Referred Listing
?
Cart & AddedListing Cart
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124. Search
Query Root
Search Search Search
Listing & Referred Listing Listing
Cart & AddedListing Cart
Tuesday, March 12, 13

125. Search
Query Root
?
Search Search Search
? ?
Listing & Referred Listing Listing
? ?
Cart & AddedListing Cart
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126. Query Root
Search Search Search Search
Listing & Referred Listing Listing
Cart & AddedListing Cart
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127. null
null null
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128. Tuesday, March 12, 13

129. Tuesday, March 12, 13

130. Match Tree
• Purely functional data structure
• Holds matched events and indices
• Match prefixes shared
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131. Match Tree Algorithm
• Fold over sequence accumulating tree
• May extend any non-terminal node
• Each level in tree corresponds to predicate
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132. Practicality
• Explodes, but
• Queries are constant length
• Sequences are bounded (visits)
• Predicates constrain growth
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133. Summary
• Why funnels are interesting
• What we’ve built with them at Etsy
• Our approach to funnel analysis
Tuesday, March 12, 13

134. Questions?
• Steve Mardenfeld
• Wil Stuckey @quiiver
• Matt Walker @data_daddy
Tuesday, March 12, 13