using self-service analytics in one year

Transcription

1 #TC18 using self-service zulily to grow 1.5m customers in one year Sasha Bartashnik Marketing Analytics Manager zulily

2 introduction Sasha Bartashnik Manager, Marketing

3 agenda How we achieved 1.5m in customer growth Our approach to marketing optimization What s next in marketing analytics Q&A

4 zulily: a place for fun shopping routine (regular) shopping fun shopping The fun of browsing is new ideas on what to buy. I am constantly getting inspired and updating. I get a real endorphin rush when a new idea gets ignited in me.

5 zulily today 71% of orders placed on mobile devices 1 91% of orders placed by repeat customers 2 we launch 100 new sales every day typically for 72 hours A typical Costco has 4,500 SKUs; we launch more than 9,000 product styles daily with minimal inventory 1 Based on customers that made purchases during 2017 calendar year. 2 Based on orders placed from January to December 2017 by customers who previously purchased from zulily.

6 a day in the life of zulily marketing launch hundreds of ads serve millions of impressions convert tens of thousands of customers

7 Active Customers (millions) enabling growth 7 6 Active Customers 6.4M Active Customers % increase in active customers 13% increase in revenue 1 - Q12014 Q22014 Q32014 Q42014 Q12015 Q22015 Q32015 Q42015 Q12016 Q22016 Q32016 Q42016 Q12017 Q22017 Q32017 Q42017 Q12018 Q An active customer is defined as an individual who has purchased at least once in the last twelve months, measured from the last date of a period. Active customers are in millions. All data as of Q

8 how we achieved growth? self-service analytics Analytics team can move fast without needing to involve IT in everyday activities. Business users get real-time access to key data without needing to involve analysts to generate basic insights.

9 marketing analytics team growth Create data science function separate reporting from analytics develop self-service toolset BQ migration

10 building the Tableau + BigQuery pipeline

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12 zulily Tableau + BigQuery pipeline data team pushes all data structured and unstructured, real-time and batch into BigQuery marketing analysts & data scientists join multiple data sources using BigQuery s SQL analysts develop models on BigQuery data marts using a variety of common data science tools as well as internal ETL platforms marketers and analysts use Tableau for selfservice analytics on data and model results stored in BigQuery

13 key centralized data in Tableau Last Week Stats Last Fiscal Week Efficiency LTV LTV 2/5/2018 ### ### ### Activation Rate App Installs App Visits Bounce Rate Conversion Rate Clicks Opens Orders 0d Orders 14d Order 365d Revenue 0d Revenue 14d Revenue 365d Total Cost Visits All data shown is masked to protect proprietary information.

14 intra-day self-service insights All data shown is masked to protect proprietary information.

15 holistic view of cross-company metrics Revenue Active customers Brands on site Order delivery Cancellations All data shown is masked to protect proprietary information.

16 our approach to marketing optimization

17 marketing analytics program components 1 customer acquisition 2lifecycle management 3 measuring the brand 4 monitoring

18 1 customer acquisition foundational data layer with one view collect raw data join to multiple traffic sources determine visit source add customer segmentation information

19 1 customer acquisition understand value of a new customer use historical data produce variables using data science model predict value days after acquisition find existing high value customers Which behaviors distinguish them from others? Are new customers showing these behaviors? To what extent?

20 1 customer acquisition what goes into the zulily predictive model? What she uses to access zulily What her first purchase was like How she engages with us Where she is located How she found us thousands of variables considered for the model hundreds of models tested dozens of variables chosen using gradient boosting machine learning +85% accuracy in predicting 1 year revenue

21 1 customer acquisition optimize new customer acquisition a/b testing: $ $$$ creative timing shift budget & bids landing experience paid vs unpaid DR vs brand

22 model score 2 lifecycle management targeted messaging lift by likelihood to be high value lean in to these segments for offer targeting lift (%) in chance of purchase

23 2 lifecycle management using personalization to optimize Favorite brands Site and App Engagement Love this feature! Purchase History Behavior Text and Customer Service

24 Demand / Send Click Rate Open Rate Send Count 2 lifecycle management optimization based on response Performance Metrics YoY All data shown is masked to protect proprietary information.

25 2 lifecycle management building the right models & tools Purchase/Churn Propensity Modeling Attribution Model Incrementality Engagement Modeling Lookalike Modeling Customer Level Profitability Modeling

26 3 brands & partners how we track the brand NPS TV response customer surveys & VOC

27 4 monitoring monitoring data science models Variable X Bin All data shown is masked to protect proprietary information.

28 4 monitoring monitoring inputs to data science models All data shown is masked to protect proprietary information.

29 what s next in marketing analytics

30 the future of zulily and the industry Using self-service philosophy to be more efficient, do more with less A renewed focus on sustainable growth, long-term customer relationships Increased integration between marketers and data scientist the rise of marketing engineers ex: advanced attribution

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