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

Thanks!@John4manJohn-Foreman.com

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