Have you ever been at a loud party, or a bar, and you suddenly pick out your name being said by your friend down the end of the bar? Your brain exists to find the signal in the noise and pluck out personalized experiences. It's rooted deep in our psychology, and our need for control and to reduce information overload, but at the core... it's just how our brains physically work. There are a number of (expensive) systems that can help you employ personalization on your website, but did you know you could use personalization techniques on your site essentially overnight using free tools, and get some pretty insane results? Using free Google Analytics and Google Tag Manager, we helped one client increase their conversion rate by 32X (from 1% to 32%) using free tools, without even having to go through their developers. We'll walk through why humans do the things we do, and then dig into how you too can employ a similar personalization scheme using tools within the Google Marketing Platform to hopefully get some similarly crazy results.

Remember that time you ran a lunch-and-learn at your company to show a handful of co-workers some Excel tips? What would have happened if you actually needed to fully train them on Excel, and there were approximately a gazillion users*? Or, have you ever watched a Google Analytics or Google Tag Manager training video? Or perused their documentation? How does Google actually think about educating a massive and diverse set of users on their platform? And, what can we learn from that when it comes to educating our in-house users on tool, processes, and concepts? In this episode, Justin Cutroni from Google joined the gang to discuss this very topic! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Past attendees of Superweek have ridden along with Tim as he explored R, and then as he dove deeper into some of the fundamental concepts of statistics. In this session, he will provide the latest update on that journey: how he is putting his exploration into the various dimensions of data science to use with real data and real clients. The statistical methods will be real, the code will be R (and available on GitHub), and the data will only be lightly obfuscated. So, you will be able to head back to your room at the next break and try one or more of the examples out on your own data! (But, don't do that -- the food and conversation at the breaks is too good to miss!)

The Google Analytics Suite of products is now part of the Google Marketing Platform. We will cover how key pieces of the Platform can be used including the Salesforce connectors, Display & Video 360, Google Optimize integration, and Google Cloud integrations. We will review how data can be used actionably for advertising, e-mail, personalization, and surveys.

Mobile Apps are not the same as web, so why have we been measuring them as such? With the old GA Services SDK turning down for some users, it's time to look into how to use Google Analytics for Firebase to measure and action on your mobile app's data. Krista will walk you through the benefits and power of the tool, explain the differences in data model and implementation best practices, and tips for how to migrate.

"The vast majority of commercial web data we analyse, even as professionals, is poor quality." A large part of my job involves auditing Google Analytics setups in order to establish the quality of the data collected. This story brings together some of the extraordinary findings of my work. Its a study of 75 enterprise websites using Google Analytics. The results are somewhat surprising (and depressing) in that they show the general poor quality of data that organisations are working with. For example: the Average Quality Index score is only 35.7 out of 100, and one in five websites have a PII issue i.e. were collecting personal information into Google Analytics.

Summary

Every business with a website needs some way to keep track of how much traffic they are getting, where it is coming from, and which actions are being taken. The default in most cases is Google Analytics, but this can be limiting when you wish to perform detailed analysis of the captured data. To address this problem, Alex Dean co-founded Snowplow Analytics to build an open source platform that gives you total control of your website traffic data. In this episode he explains how the project and company got started, how the platform is architected, and how you can start using it today to get a clearer view of how your customers are interacting with your web and mobile applications.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. You work hard to make sure that your data is reliable and accurate, but can you say the same about the deployment of your machine learning models? The Skafos platform from Metis Machine was built to give your data scientists the end-to-end support that they need throughout the machine learning lifecycle. Skafos maximizes interoperability with your existing tools and platforms, and offers real-time insights and the ability to be up and running with cloud-based production scale infrastructure instantaneously. Request a demo at dataengineeringpodcast.com/metis-machine to learn more about how Metis Machine is operationalizing data science. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat This is your host Tobias Macey and today I’m interviewing Alexander Dean about Snowplow Analytics

Interview

Introductions How did you get involved in the area of data engineering and data management? What is Snowplow Analytics and what problem were you trying to solve when you started the company? What is unique about customer event data from an ingestion and processing perspective? Challenges with properly matching up data between sources Data collection is one of the more difficult aspects of an analytics pipeline because of the potential for inconsistency or incorrect information. How is the collection portion of the Snowplow stack designed and how do you validate the correctness of the data?

Cleanliness/accuracy

What kinds of metrics should be tracked in an ingestion pipeline and how do you monitor them to ensure that everything is operating properly? Can you describe the overall architecture of the ingest pipeline that Snowplow provides?

How has that architecture evolved from when you first started? What would you do differently if you were to start over today?

Ensuring appropriate use of enrichment sources What have been some of the biggest challenges encountered while building and evolving Snowplow? What are some of the most interesting uses of your platform that you are aware of?

Keep In Touch

Alex

@alexcrdean on Twitter LinkedIn

Snowplow

@snowplowdata on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Snowplow

GitHub

Deloitte Consulting OpenX Hadoop AWS EMR (Elastic Map-Reduce) Business Intelligence Data Warehousing Google Analytics CRM (Customer Relationship Management) S3 GDPR (General Data Protection Regulation) Kinesis Kafka Google Cloud Pub-Sub JSON-Schema Iglu IAB Bots And Spiders List Heap Analytics

Podcast Interview

Redshift SnowflakeDB Snowplow Insights Googl

Under the 'guise of a discussion about making the leap into a new technology, this bonus mini-episode (hopefully) clears up the on-going confusion about the Kiss Sisters. Moe sat down with her big sister, Michele, to chat about jumping into learning an entirely new skill when time is short, expectations are high, and the learning curve is steep. The specific example they chat about is Michele's dive into Google Analytics data in BigQuery using SQL, but the tips and thoughts are applicable to any new and intimidating platform.

Google Analytics certainly provides us the opportunity to track everything on a website. But how can we take advantage of this opportunity, within such an enterprise, to drive change and improve the quality of the services it provides? Both online and offline. In this session we will go through the four major pylons of such an endeavor: Web & App optimization Data driven Design Integrated data from all sources

There is no shortage of guides and tutorials about how to track everything from videos to the weather (Thanks Simo!). There are very few examples of anyone actually using Google Analytics to take actions and drive change. In this session, I will share my favorite examples from landing page optimization, content ideas, to marrying qualitative with quantitative data. In addition, everyone talks about attribution, but rarely is anyone able to show why it matters for their business. I'll share quick examples to show how to start a meaningful conversation around attribution and show why it matters.

Summary

Buzzfeed needs to be able to understand how its users are interacting with the myriad articles, videos, etc. that they are posting. This lets them produce new content that will continue to be well-received. To surface the insights that they need to grow their business they need a robust data infrastructure to reliably capture all of those interactions. Walter Menendez is a data engineer on their infrastructure team and in this episode he describes how they manage data ingestion from a wide array of sources and create an interface for their data scientists to produce valuable conclusions.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Continuous delivery lets you get new features in front of your users as fast as possible without introducing bugs or breaking production and GoCD is the open source platform made by the people at Thoughtworks who wrote the book about it. Go to dataengineeringpodcast.com/gocd to download and launch it today. Enterprise add-ons and professional support are available for added peace of mind. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I’m interviewing Walter Menendez about the data engineering platform at Buzzfeed

Interview

Introduction How did you get involved in the area of data management? How is the data engineering team at Buzzfeed structured and what kinds of projects are you responsible for? What are some of the types of data inputs and outputs that you work with at Buzzfeed? Is the core of your system using a real-time streaming approach or is it primarily batch-oriented and what are the business needs that drive that decision? What does the architecture of your data platform look like and what are some of the most significant areas of technical debt? Which platforms and languages are most widely leveraged in your team and what are some of the outliers? What are some of the most significant challenges that you face, both technically and organizationally? What are some of the dead ends that you have run into or failed projects that you have tried? What has been the most successful project that you have completed and how do you measure that success?

Contact Info

@hackwalter on Twitter walterm on GitHub

Links

Data Literacy MIT Media Lab Tumblr Data Capital Data Infrastructure Google Analytics Datadog Python Numpy SciPy NLTK Go Language NSQ Tornado PySpark AWS EMR Redshift Tracking Pixel Google Cloud Don’t try to be google Stop Hiring DevOps Engineers and Start Growing Them

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Develop your digital/online marketing skills and learn web analytics to understand the performance of websites and ad campaigns. Approaches covered will be immediately useful for business or nonprofit organizations. If you are completely new to Google Analytics and you want to learn the basics, this guide will introduce you to the content quickly. Web analytics is critical to online marketers as they seek to track return on investment and optimize their websites. Introduction to Google Analytics covers the basics of Google Analytics, starting with creating a blog, and monitoring the number of people who see the blog posts and where they come from. What You'll Learn Understand basic techniques to generate traffic for a blog or website Review the performance of a website or campaign Set up a Shopify account to track ROI Create and maximize AdWords to track conversion Discover opportunities offered by Google, including the Google Individual Qualification Who This Book Is For Those who need to get up to speed on Google Analytics tools and techniques for business or personal use. This book is also suitable as a student reference.

Miroslav will present Analytics from a completely different perspective. Some things he will reveal about the life and job of an Analytics / data expert will be painful, honest, interesting and most of all, funny. After his Stand-up, you will look at attribution, data collection and Analytics dimensions / metrics, from a new point of view. Please, don't expect everything will be 100% accurate - after all, it's a stand-up.

Correct and accurate measurement of a hybrid app with GA is hard. The Native app outer can be measured in GA using app measurement and the mobile web content can be measured using normal universal analytics GA. The problem is that you can't tell where hybrid app users came from in the mobile web GA - there is no useable traffic source. You can't measure the app and web in the same property but you have hybrid app mobile web and pure mobile web in the mobile web property. This sounds like a complex mess and it is but we've solved it and we'll explain how with a real live demo and technical walk through with Q & A.

Enhanced Ecommerce introduced some new metrics that explain user behavior – something Google Analytics should be used for. Robert, a corporate web analyst or the EEEO (Enhanced Ecommerce Executive Officer) will show us how he uses Cart-to-Detail Rate, Buy-to-Detail Rate and some calculated metrics for better campaign as well as warehouse planning in the EEEO (Enhanced Ecommerce Enabled Organization).

Somewhere along the spectrum of "logging into Google Analytics" and "the machines are in control" is the world of the power analyst who interacts with the data on the fly, applies statistics to large data sets, and develops interactive visualizations that go well beyond the capabilities of Excel. Those power analysts are operating on the fringes of the domain of the "data scientist" -- a role for which no one can really agree on a concrete definition! In this session, Tim -- who has never claimed to be and never will claim to be a data scientist -- will share what he has learned from trying to understand the scope and nature of that role. And, beyond that, how he has grown as a digital analyst, expanded his skills to "program with data" with R, and increased his value to the organizations with which he works as a result.

User experience monitoring is essential for enhancing the usability and performance of your mobile native app. How are your customers using your product? Which features do they prefer? How can you spot trouble before it adversely affects your product? This O’Reilly report provides an overview of several metrics you can apply, based on different use-cases. Author Wolfgang Beer explains the typical instrumentation and publishing process of mobile apps, and takes you through different instrumentation approaches. With screenshots from popular tools such as Google Analytics, Ruxit, Fabric, and Flurry Analytics, this report helps you choose the metrics that will help you improve your product’s performance. Monitor performance to understand your app’s stability and usability Measure app user engagement by identifying active and new users, and determining median session length Determine your app’s current retention and churn rates Gather business intelligence by defining users according to personas and lifetime value Oversee the service and infrastructure dependencies of your app in real time Visually track user behavior with heat maps and navigational paths Add automated or manual instrumentation before you publish your app

A complete, start-to-finish guide to Google Analytics instrumentation and reporting Google Analytics Breakthrough is a much-needed comprehensive resource for the world's most widely adopted analytics tool. Designed to provide a complete, best-practices foundation in measurement strategy, implementation, reporting, and optimization, this book systematically demystifies the broad range of Google Analytics features and configurations. Throughout the end-to-end learning experience, you'll sharpen your core competencies, discover hidden functionality, learn to avoid common pitfalls, and develop next-generation tracking and analysis strategies so you can understand what is helping or hindering your digital performance and begin driving more success. Google Analytics Breakthrough offers practical instruction and expert perspectives on the full range of implementation and reporting skills: Learn how to campaign-tag inbound links to uncover the email, social, PPC, and banner/remarketing traffic hiding as other traffic sources and to confidently measure the ROI of each marketing channel Add event tracking to capture the many important user interactions that Google Analytics does not record by default, such as video plays, PDF downloads, scrolling, and AJAX updates Master Google Tag Manager for greater flexibility and process control in implementation Set up goals and Enhanced Ecommerce tracking to measure performance against organizational KPIs and configure conversion funnels to isolate drop-off Create audience segments that map to your audience constituencies, amplify trends, and help identify optimization opportunities Populate custom dimensions that reflect your organization, your content, and your visitors so Google Analytics can speak your language Gain a more complete view of customer behavior with mobile app and cross-device tracking Incorporate related tools and techniques: third-party data visualization, CRM integration for long-term value and lead qualification, marketing automation, phone conversion tracking, usability, and A/B testing Improve data storytelling and foster analytics adoption in the enterprise As many as 10-25 million organizations have installed Google Analytics, including an estimated 67 percent of Fortune 500 companies, but deficiencies plague most implementations, and inadequate reporting practices continue to hinder meaningful analysis. By following the strategies and techniques in Google Analytics Breakthrough, you can address the gaps in your own still set, transcend the common limitations, and begin using Google Analytics for real competitive advantage. Critical contributions from industry luminaries such as Brian Clifton, Tim Ash, Bryan and Jeffrey Eisenberg, and Jim Sterne – and a foreword by Avinash Kaushik – enhance the learning experience and empower you to drive consistent, real-world improvement through analytics.

As an analyst, it's never a good idea to make predictions without data. With that said, for our first predictions episode, we've chosen to make some big and small predictions for the digital analytics space for the remainder of 2016 -- using only experience and intuition! Join us in Episode 30 as we rely solely on intuition to predict the next 9 months of a multi-billion dollar industry - all in under 45 minutes. Note: Due to the lag between recording and release, our prediction during the episode about a certain Heisman Trophy winner actually came true...before this episode launched.

People, places, and things mentioned in this episode:

Tealium Ensighten Signal Mixpanel Amazon Redshift Looker Adobe Analytics Google Analytics Optimizely Adobe Target Johnny Manziel Cleveland Browns Paul DePodesta Moneyball Ben Gaines Median Absolute Deviation (MAD) Brian Clifton Domo Sweetspot Intelligence Tableau Software eMetrics "I Predict a Riot" (Kaiser Chiefs)

Data quality is often taken for granted. Many organizations fall into complacency with tools like Google Analytics, where tracking is installed but rarely optimized, configured, or scrutinized. As it turns out, this type of plug-and-play analytics can be detrimental to your measurement strategy. In this talk, Simo will show his experiences of working with vastly different organizations and methodologies for tag management, highlighting the format with which he's had most success. He will also showcase how a basic setup of Google Analytics (or any other popular web analytics platform) is simply not enough, together with a case study or two of how to turn the limitations of these platforms to your advantage.