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Snowplow, Metail and Cascalog

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How Metail uses Snowplow and Cascalog

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Snowplow, Metail and Cascalog

  1. 1. 1 Snowplow and Cascalog METAIL - YOUR ONLINE FITTING ROOM Presentation by Rob Boland, Lead Data Architect
  2. 2. 2 Introduction • Introduction to Metail – who we are, why we use Snowplow • How the Lambda Architecture has influenced our Data Architecture • Where Cascalog fits in at Metail and why it works well with Snowplow • Example of where we’ve used Cascalog and how it works • Looker forward to the future
  3. 3. 3 Every body is unique and should be celebrated
  4. 4. 4 YOUR ONLINE FITTING ROOM
  5. 5. 5 • Sign up with just a few clicks • See how the clothes look on you • Build layered outfits • Get size recommendation http://trymetail.com/collections/metail
  6. 6. 6 1. Customer shape & size data can now aid brand’s buying & selling decisions 2. Body shape & outfitting data -> crowd sourced outfit recommendations Product portfolio: Data services UNDERSTANDING SHAPE PROFILE OF CUSTOMERS HOW SHAPE VARIES BY SIZE Do we need to create new collections to cater for clusters of different shapes? Do we need to change the fit profile by size to accommodate different shapes?
  7. 7. 7 KPI Analysis – Can we prove it actually works? Metric Definition Return on Investment [(VPVuplift * All Visits ) - Investment] / Investment Net sales revenue Value of retained items in bin Value per visitor Net Sales Revenue / Visitors Visits (sessions) Set of activities with <= 30 minutes between consecutive events User Conversion Orders / Visitors Adoption Rate Number of user’s who use Metail / Number of user’s shown Metail Average Order Value Median value of all orders tracked in the time period Return Rate Number of items returned / Number of Items purchased Average Retained Order Value Median value of all orders tracked in the time period after removing returned items AB Set up: 50/50 split test Managed by: Metail through their AB test platform
  8. 8. 8 KPI Analysis – Can we prove Metail impact? Data Collection We need to know visitor counts, order values, which test group the user was in, whether they actually used Metail or not, time on site, what garments they wore, etc. etc.
  9. 9. 9 Enter Snowplow
  10. 10. 10 What Metail looks like (for now…)
  11. 11. 11 Data Collection! Now what? Read the Big Data book (Still MEAP after 3 years!)
  12. 12. 12 Lambda Architecture
  13. 13. 13 Cascalog to produce Batch Views Turn the Snowplow event stream into a normalised schema Body Shape Orders Items Ordered Returns Browsers (visitors) Sessions Garment Details AB Events Snowplow Events
  14. 14. 14 Cascalog: Snowplow ETL Runner Output -> Batch Views Cascalog is designed to process Big Data on top of Hadoop. It is a replacement for tools like Pig, Hive, and Cascading which operates at a significantly higher level of abstraction than those tools [1] Write Clojure code to create our data processing jobs • The code you write has be MapReduce aware, but the low level implementation details are taken care of • What we’re really doing is adding another ETL Step to the Snowplow flow [1] http://cascalog.org/ Cascalog is written in Clojure (JCascalog in Java, or Scalding in Scala) It’s easy to run on Amazon EMR – fits in with the Snowplow flow nicely
  15. 15. 15 Cascalog – Worth the effort? Couldn’t you achieve the same output working with the events table alone? …kind of But there are two key benefits: 1. Breaking the data into a manageable schema means you can directly access the data you care about 2. Complex logic and aggregation is easier to achieve Real example: • KPI Data Aggregation
  16. 16. 16 Cascalog – KPI Data Aggregation Value per visitor Net Sales Revenue / Visitors User Conversion Orders / Visitors Adoption Rate Number of user’s who use Metail / Number of user’s shown Metail How do we calculate KPIs from our Snowplow data? In both the Active and Control groups, we need: • Visitor Count • Engaged Visitor Count • Order Count • Order Value
  17. 17. 17 Cascalog – KPI Data Aggregation Visitors Count • Snowplow tracks visitors – our code just has to look up visitors who are in the test we’re measuring Engaged Count • Fire a structured event to Snowplow each time an ‘engagement’ event occurs. For each visitor in the test, our code has to find whether or not they engaged with Metail Orders We encode all of the relevant order information on the page in JSON and fire an unstructured event with the details Order Count • Our code needs to find all of the order events in the time period Order Value • Our code needs to read the order value and sum it together
  18. 18. 18 Cascalog – KPI Data Aggregation We can do better! What we really want is a user level summary of the data domain_id engaged order_value order_id ab_group 0014822757d9a81f null 175.89 89281949 out 0015ca5144f0fae7 null null null out 0015dd8901887010 null 310.22 25394849 out 0015e633aa2c158d null null null in 00204e1bcc87b734 null null null out 0042472794f2b57a null 191.98 89392136 in 004389f95e620dd0 null null null out 0044867c3d7b1cf5 null null null out 00456d1e9300296e null null null out 0045dc05b4262ed2 null null null in 0045f74358a842c1 TRUE null null in 00462b685f4188ad null null null out 0048fccbe230dc57 null null null out 0049a5d24498051d TRUE 101.96 27529849 in
  19. 19. 19 Cascalog – Implementation 1) Read in the Snowplow events data in HDFS 2) Remove events we don’t care about
  20. 20. 20 Cascalog – Implementation 3) Take those events, pull out the bits we care about and join them together
  21. 21. 21 What do we do with the Batch Views? Take the output and crunch it in R (or Incanter) A lot of the subsequent analysis we run on our batch views requires statistical packages, so we run our advanced analysis in R. Thankfully, having the batch views ready has led to far fewer of these:
  22. 22. 22 A Looker Ahead Not everyone can write Cascalog and R. Looker will open our batch views and Snowplow events to our Business Analysts
  23. 23. 23 www.metail.com Contact information ROB BOLAND LEAD DATA ARCHITECT rob@metail.com Skype: rpboland

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