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analytics tools

Questions to ask when analyzing data

In digital analytics, it’s all about asking the right questions.

Sure, in the right context, you can probably get by doing what Avinash Kaushik refers to as “data puking,” but you won’t excel as an analyst or marketer.

In addition, you’ll consistently come up short on bringing true business value to your company.

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Model ship

C.F. Kettering once said, “My interest is in the future because I am going to spend the rest of my life there.”

When we look at data and analytics, we’re focused on the past. How did we do last quarter? What happened H1 2019? And how does that compare to H1 2018? How well did landing pages X, Y, and Z convert last Monday at 1:03 p.m.? (I’m kidding, I’m kidding.)

Data becomes more valuable when we use it to predict the future instead of just analyzing the past. That’s where propensity modeling comes in.

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Google Optimize

Chances are, you’ve heard of Google Optimize by now. It’s Google’s solution for A/B testing and personalization. Over the years, it has become a popular solution for optimizers around the world who wanted a freemium tool to do A/B testing.

In this post, you will learn what you can really expect from this tool. How do you configure it properly? How do you run your first experiment? Let’s go into details:

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Product analytics

According to Amplitude, product analytics “show you who your users are, what they want, and how to keep them.”

I remember the first time that a client told how they were able to increase their sign-up rate for their product by 22% while reducing their marketing costs. The secret to their success?

They simply used their analytics data to make informed decisions.

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Key Performance Indicators (KPIs) for Optimization

It’s easy to get lost down the rabbit hole of metrics for your business. But focusing on the handful of metrics that matter is what will ultimately drive the biggest results.

The trick is figuring out which metrics you should focus on. This article will break down everything you need to know about defining and setting your key performance indicators (KPI’s.)

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Web Analytics Analysis

For a web analytics analyst or a data-driven marketer, these are words to live by: “Without data, you’re just another person with an opinion.”

Optimization isn’t about educated guesses and hunches, no matter how many years you’ve been in the industry. It’s about doing the research, asking the right questions, digging for clues in problem areas, paying attention to the signs when they appear, and running smart A/B tests.

Web analytics analysis is a big part of that. It helps separate the optimizers from just another person with an opinion.

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Kaggle: A Marketer's Guide for Analytics and Data Science

Kaggle, the Google-acquired data science platform, started as a virtual meeting point for machine-learning geeks to compete on predictive accuracy scores.

It evolved into a Swiss Army knife for data science and analytics—one that can help data professionals, including data-driven marketers, elevate their analytics game.

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Behavior-Based Attribution Using Google BigQuery ML

Why create a custom attribution tool? Because with out-of-the-box tools, you’re limited by their functionality, data transformations, models, and heuristics.

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Adobe Analytics Implementation: Hacks You Don’t Know About

A few years ago, our developers rolled out Angular on a few key web pages—without consulting the web analytics team. The pageviews on some of the pages suddenly dropped to almost nothing.

I bought two books on Angular to try to find a solution. Meanwhile, my manager stumbled on an alternative syntax for tracking pageviews in Adobe Analytics. As we found out, a similar alternative syntax also exists to track clicks (and anything else).

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Mobile App Analytics

Where should you start with your mobile app analytics?

If you’re launching a mobile app, you’ve likely considered the differences between optimizing a simple site and a mobile app or game. But have you considered how the measurement and data analysis process will be different?

Without a definitive plan and deep understanding of the fundamentals, your mobile app data will get complicated quickly. There are new tools, new terminology, new reports, new rules for efficiency, and so on.

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