Data Fortification at MailChimp: Trends, Pitfalls, and the Customer Experience

June 2015

Largest email service provider8.5m users, 15b sends, 5b actions350 employees all in Atlanta

Email is almost 40x better at acquiring new customers than Facebook and Twitter - McKinsey & Company (2014)

55% of companies generate more than 10 percent of sales from email - Econsultancy "Emailmarketing census” (2014)

Improve the productImprove the user experience

Classic Research

Forecast and Optimize Processes

Inform Customers

Contextualize (Respond)

Trigger

Predict and Personalize

Predict and Personalize

Predict and Personalize

Danger 1: Everything is a nail

Danger 2: Tinkering

Danger 3: When your audience isn’t your customer

Danger 4: Locking your scientists behind a fence

Danger 5: Building a better carriage

Danger 6: The Content Problem

Danger 7: Over-complexity

Danger 8: Hidden consequences

1

1. Know what’s possible• Data• Techniques• Technologies

2. Identify opportunities for the business• In constant

conversation• Not all problems are

data problems

3. Choose what solves the problem• Avoid added

complexity• Use internal data

where possible• Think about the UX

Early Middle Late

Come back for the usability, stay for the data

“We want to mine our own data!”

BARF

Pro Reporting (start with classic research)

• In-app data-mining across any set of emails• Composite analysis• Comparative analysis• Post-hoc segmentation and

exploration• Ability to share

What’s working? What’s not? How do I do better?

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Choose the tools to fit the job

• Apache Kafka – Moving high volumes of data into the reporting system in real time, fault tolerant• Postgres – Our data is already structured,

indexes on what we need (geo, etc.)• MLE – Easy to debug during v1 and

understand why data is driving output• Reporting is seamlessly integrated into

MailChimp proper reusing same JS libraries and PHP framework

Thanks!@John4man