Working with Google Analytics Data Using Python and Jupyter Notebooks

Python is a programming language with virtually limitless functionalities and one of the best languages for working with data. Jupyter Notebooks, on the other hand, is the most popular tool for running and sharing both your Python code and data analysis.

Putting Python and Notebooks together with Google Analytics, the most popular and a really powerful tool for tracking websites, gives you almost like a superpower for doing your analysis.

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Data You Should Be Tracking Using Google Analytics Custom Events

Out of the box, Google Analytics already tracks a bunch of really useful data points. What the default setup lacks, though, is context and events that are specific to your website and business.

Custom Events provide a perfect solution for adding context and tracking more specific user actions. In this article, we are giving you a good amount of ideas for custom events you should implement on your own and/or your clients’ websites.

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Using Google Analytics for Tracking SaaS

While it was originally built for tracking general marketing and ecommerce websites, Google Analytics can be configured to track your SaaS app as well. After building tracking setups for 11 different SaaS tools, I can say that Google Analytics excels in tracking SaaS. In this article, I’m giving you a bunch of practical guidelines for doing the same with your own (or your client’s) SaaS app.

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How to Detect and Track Incognito Users with Google Analytics and Google Tag Manager

Depending on the type of website you are working with, it is likely that some percentage of your users are using their browser in incognito or private mode.

Since using incognito mode can skew the numbers you see in analytics, it is really good to know the estimated percentage of visitors that prefer to stay incognito.

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Good Nomenclature Is More Important Than You Probably Think

Let’s be honest, most companies don’t really think about nomenclature when it comes to setting up their tags, goals or A/B testing experiments. And that creates a horrible mess that will steal your teams valuable time and makes sure no-one really knows what’s going on.

At Reflective Data, when we start working with a new client, we always start by figuring out what their current system consists of — and in many cases, it’s a real headache. Not to mention, when we ask the client about a specific tag or goal that they set up 6 months ago, they don’t remember anything — and the name they chose isn’t helping much either.

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7 Best Data Visualization Tools in 2019

Data visualization allows marketers to physically see and communicate with their target audience. Marketers typically have all the same information via text, but turning it into a visual aid can allow companies to make business decisions much faster. Simple aids like pie charts or line graphs can take thousands of pieces of information and compress them all down into something legible.

Creating these visuals on your own can take more time than reading through the slog of information. The good news is that technology has created data visualization tools for marketers to easily pour their data into and create the visuals for them.

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