Business performance analysis is central to any business, as it helps to make or mend products, services, and processes. This book provides several blueprints for setting up business performance analytics (BPA) shops, from process layout for performance measures to tracking the underlying metrics of them using website tools such as Google Analytics and Looker Studio. Delivering satisfying user experiences in the context of overarching business objectives is key to delivering elevated business performance. This book transcends the topic of tracking user behaviors in websites from generic to specific KPI scenario-based tracking using Google Analytics/Google Tag Manager. Business Performance Analysis stands out by helping you create fit-for-purpose and coherent performance analysis blueprints by integrating performance measure creation and website analytics of BPA together. What You Will Learn Design a Business Performance Analysis function Analyze performance metrics with website analytics tools Identify business performance metrics for common product scenarios Who This Book is For Senior leaders, product managers, product owners, UX and web analytics professionals

Lakeflow Connect enables you to easily and efficiently ingest data from enterprise applications like Salesforce, ServiceNow, Google Analytics, SharePoint, NetSuite, Dynamics 365 and more. In this session, we’ll dive deep on using connectors for the most popular SaaS applications, reviewing common use cases such as analyzing consumer behavior, predicting churn and centralizing HR analytics. You'll also hear from an early customer about how Lakeflow Connect helped unify their customer data to drive an improved automotive experience. We’ll wrap up with a Q&A so you have the opportunity to learn from our experts.

Hundreds of customers are already ingesting data with Lakeflow Connect from SQL Server, Salesforce, ServiceNow, Google Analytics, SharePoint, PostgreSQL and more to unlock the full power of their data. Lakeflow Connect introduces built-in, no-code ingestion connectors from SaaS applications, databases and file sources to help unlock data intelligence. In this demo-packed session, you’ll learn how to ingest ready-to-use data for analytics and AI with a few clicks in the UI or a few lines of code. We’ll also demonstrate how Lakeflow Connect is fully integrated with the Databricks Data Intelligence Platform for built-in governance, observability, CI/CD, automated pipeline maintenance and more. Finally, we’ll explain how to use Lakeflow Connect in combination with downstream analytics and AI tools to tackle common business challenges and drive business impact.

Tag Manager Italia collaborated with CNH to design and implement a global GA4-based data strategy, unifying analytics across their extensive operations. This session explores the whole project, with a focus on how advanced tools like BigQuery and Databricks enabled data centralization, while custom Power BI dashboards and privacy-compliant frameworks empowered informed decisions and enhanced marketing and business outcomes.

This session shares real-world lessons from implementing Google Analytics 4 (GA4) and integrating multi-channel data into BigQuery for an enterprise Retail Media Network. From navigating internal politics and shifting stakeholder landscapes to adapting to evolving requirements and re-scoping complex projects, this talk highlights the critical factors that can make or break large-scale analytics initiatives.

Using BigQuery for GA4 data is nothing new. We have all been exploring or weighing the capabilities of the export to some extent. And we have all been wondering.

With GA4 putting web and behavioural data in a data warehouse into the hands of more analysts than ever before, you may be wondering how to get the best from your data in BigQuery (or any data warehouse), keep costs manageable, and how to give your users the best performance possible. This talk will cover different approaches to data modelling, the trade-offs associated with each approach, and how the dashboard/BI tool you’re using (whether it be Looker or Looker Studio, Tableau, Power BI etc) impacts your data modelling.

We all know that data, like wine and cheese, becomes more valuable when combined. And, just like wine and cheese, they can lead to serious headaches. Whether you are emailing Excel files around, capturing data from thousands of IoT-devices, or just joining your Google Analytics and sales data, you can benefit from following a structured process to minimize your headaches. After debugging yet another failed pipeline I have distilled my experience of building data ingestion pipelines in 8 simple (though not necessarily easy) steps from setting up triggers to archiving and retention.

Google Professional Services is a team of technical consultants and engineers, many of whom have been heavily involved in GA4 migration projects over the last years.

2023 was full of GA4 monkey stuff. As a matter of fact, most of analyst's life is full of coding and tech.

The usage of GA4 and BigQuery real-time reports features can be quite challenging, especially in high-traffic volume websites and other demanding environments. For example, assuming that you find a viable solution for a specific project, it is crucial to determine in advance the projected BigQuery expenses, in order to avoid unpleasant surprises. Architecture, data management, limits and quotas on API Requests are also part of this complex equation. Matteo and Roberto will share some real-world solutions for GA4 and BigQuery real-time needs tested with several clients in different industries.

This session delves deep into the mechanics and methodologies of constructing AI-driven applications tailored for GA4.

For those who celebrate or acknowledge it, Christmas is now in the rearview mirror. Father Time has a beard that reaches down to his toes, and he's ready to hand over the clock to an absolutely adorable little Baby Time when 2024 rolls in. That means it's time for our annual set of reflections on the analytics and data science industry. Somehow, the authoring of this description of the show was completely unaided by an LLM, although the show did include quite a bit of discussion around generative AI. It also included the announcement of a local LLM based on all of our podcast episodes to date (updated with each new episode going forward!), which you can try out here! The discussion was wide-ranging beyond AI: Google Analytics 4, Marketing Mix Modelling (MMM), the technical/engineering side of analytics versus the softer skills of creative analytical thought and engaging with stakeholders, and more, as well as a look ahead to 2024! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Anna Nerezova is a Digital Marketing and Cloud Transformation Consultant with 15 years of experience in data, analytics and optimization. She has built innovative solutions using Google Cloud to solve problems in the media and entertainment, tech, health and non-profit industries Anna is a Google Cloud Engineer Scholar, and is on the 100 Women in Analytics list by Google Analytics. She is also a Google Developer Group NYC lead and a mentor for the Northeast, and a proud Women Techmakers Ambassador.

Anna is passionate about using the latest AI/ML technologies to provide solutions and bring growth and better experience for all users. She is a strong advocate for diversity and inclusion, and is committed to helping underrepresented groups become entrepreneurs and financially independent.

Hiring, salary adjustments, inflation, rate increases, crappy GA4 implementations - there's plenty for agencies to do, and worry about, in 2023. Let's look at some ways to survive and maybe even thrive in the new year - possibly with some guidance from the animal kingdom.

In this talk, we will delve into the features and capabilities of Google Analytics 4's measurement protocol. We will discuss its strengths and limitations, and explore how the client-side protocol can be used to bypass some of these limitations. Finally, we will present a real-world use case for the client-side protocol and demonstrate its potential benefits.

Marketing teams need analytics. Product teams need analytics. There are marketing analytics vendors like Google Analytics and Adobe Analytics. There are product analytics vendors like Amplitude and Mixpanel. But what are the real differences between marketing and product analytics? Do organizations need different teams for each? Should organizations use both types of tools? Will one replace the other or will these disciplines converge?

In August 2020 Matteo and Roberto met for the first time a fascinating creature with a cumbersome name: App + Web. It was not love at first sight. After all, they were already engaged in a longtime romance with Universal Analytics.

While the subject of privacy has been around for a while, the good old "collect it all" approach is doing better than ever. With pressing expectations to start leveraging GA4 data, customers realized that information comes at a cost. The more data, the higher the cost.

GA4 brought an insane amount of work to anyone using Universal Analytics currently. HelloFresh decided to clean up its implementation and do things "out of the box".