Monkeys & Math How MailChimp Catches Bad Guys
Metrics, Monkeys & Marketing Lessons on using Big Data from MailChimp
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Who am I?OR guyFormer consultantMailChimp data scientist
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Email MarketingConventional WisdomData
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Our Goal: Compliance Cheaper, Faster, Better
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Our Goal: Compliance Cheaper, Faster, Better
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Permission and HygieneTranslated into Two Measurable Response Variables: Public CorrelationHard Bounce Rate
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Hard Bounces must be predictedNeed an abundance of features / weak learners:
Earthlink email addresses are 10x more likely to hard bounce than GmailUsers whose list location does not match their billing or login locations have a 1% higher bounce rate
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ListsUsersContent
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Final modeling setInter-mediate ModelingData CollectionCategoryUser
Acct metadataAcct MetadataListAddress historyBounce ModelList scoresCampaign
Campaign historyPrev. camp. perf.
Content
Content Model
Content scoresPredictionsFinal Model
Public Lists
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Design to eliminate risk/complexity
FunctionToolSlow StorageSharded PostgreSQLFast StorageRedisModeling LanguageRMachine Learning ModelRandom ForestAPI + Background ProcessesPython
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Now that the Infrastructures in PlaceAd Hoc Analysis Deep List Investigation
Send Time Optimization
Automatic Segmentation and Content Optimization
Now that the Infrastructures in Place
Now that the Infrastructures in Place
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