Data Driven Growth

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Discover Pinterest

John Egan, a growth engineer, talks about data driven growth at Pinterest.

Engineering

John Egan
Growth Engineer @ Pinterest
Data Driven Growth

Where Do You Focus?
New user email campaign
Improved invitation flow
Add more invitation prompts
Share content to social media
Work with partners to get distribution
SEO
Advertising
SEM
Improve signup conversion
Content marketing
Improve new user experience flow
Email campaign to resurrect dormant users
Retargeting campaigns
Social media contests
Paid app installs
Weekly engagement emailCompany blog
Blogger outreach
Add invitations
Get press coverage
Add more social features
Push notifications
Build analytics dashboard

Growth Model
New
Users
Monthly Active
Users
Dormant Users

Growth Model
New
Users
Monthly Active
Users
Dormant Users
Acquisition
Activation
Churn
Churn
Resurrection

Knit:
The best outcomes happen
when you work with others.

Due diligence is cheap,
engineering is expensive

Core: Active multiple times a week
Casual: Active ~1 a week
Marginal: Active <1 a week
New: Joined in the past 28 days
Dormant: Not active for 30 days
Resurrected: Was dormant, but became a

Core Users (multiple times a week)
users)

Most companies aren’t going deep enough with their user data. Conversion funnels, retention, and segmenting by install cohort help you identify where your users are getting stuck or leaving, but aren’t very helpful in telling you what you should do instead. In this session from Opticon 2015, Spenser Skates, CEO and Co-founder of Amplitude, discusses how you can use behavior-based cohorting to maximize user engagement and retention. By grouping your users based on what actions they take, you'll discover how you can improve your product to drive growth. As with Facebook’s famous ‘7 friends in 10 days’, learn how you can find the ‘magic moment’ to focus your product around. Watch the video here: https://amplitude.wistia.com/medias/hdpnipul1l

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In April 2014, Pinterest engineers presented to members of the engineering community at a series of Tech Talks held at the Pinterest offices in San Francisco. Topics included: - Mobile & Growth: Scaling user education on mobile, and a deep dive into the new user experience (with engineers Dannie Chu and Wendy Lu) - Monetization & Data: The open sourcing of Pinterest Secor and a look at zero data loss log persistence services (with engineer Pawel Garbacki) - Developing & Shipping Code at Pinterest: The tools and technologies Pinterest uses to build quickly and deploy confidently. You can find more at: engineering.pinterest.com and facebook.com/pinterestengineering

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ABSTRACT: The ongoing big data revolution has revolutionized the way in which technology is used to empower new business segments like social networking and transform old business segments like traditional retail. However, the DNA that is used to build data processing platform is evolving quite rapidly. There is a plethora of competing tools, technologies, and “religion” for how to build state-of-the-art data analysis frameworks. In this talk, I will go over five ways to build scalable high-performance long-lasting data analysis frameworks in the wrong way. Surprisingly, the industry is full of examples of organization building frameworks in this “wrong” way. Since the “right” way to build a technology framework is dependent on the key business drivers, it is my hope that this talk will spur a discussion on what is the “right” way for Pinterest. The talk will focus on technologies including “data plumbing” (e.g. tools in the Hadoop ecosystem), and statistical modeling methods (e.g. R and Python). In this talk, I’ll try to connect to platform builders, data scientists, and business decision makers. BIO: Jignesh Patel is a Professor in Computer Sciences at the University of Wisconsin-Madison, where he also earned his Ph.D. He has worked in the area of databases (now fashionably called “big data”) for over two decades. He has won several best paper awards, and industry research awards. He is the recipient of the Wisconsin COW teaching award, and the U. Michigan College of Engineering Education Excellence Award. He has a strong interest in seeing research ideas transition to actual products. His Ph.D. thesis work was acquired by NCR/Teradata in 1997, and he also co-founded Locomatix -- a startup that built a platform to power real-time data-driven mobile services. Locomatix became part of Twitter in 2013. He is an ACM Distinguished Scientist and an IEEE Senior Member. He also serves on the board of Lands’ End, and advises a number of startups.

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Understanding Inductive Bias in Machine Learning

SUTEJAS

This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models. The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees. By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.

哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样

insn4465

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一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单专业办理

zwunae

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