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.
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.
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.)
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.
Chances are, you’ve heard of Google Optimize by now. It’s Google’s solution for A/B testing and personalization. It launched in beta in 2016 and left optimizers around the world waiting in line to try it out.
Since it left beta in March 2017, anyone can give it a try without the wait. But what can you expect? How do you configure it properly? How do you run your first experiment?
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.
Why create a custom attribution tool? Because with out-of-the-box tools, you’re limited by their functionality, data transformations, models, and heuristics.
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).
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.
Over the last 18 months or so, Google Data Studio has evolved from an appealing but clunky application to a tool that we recommend to any digital marketer.
Data Studio allows you to communicate data simply and in a repeatable format, and their expanded integrations, customizations, and editability have made Data Studio dashboards extremely powerful.
A relatively new feature, data blending, came out last year. This underused function can do a lot of cool things; it also has some limitations. Once you’ve got your head around the basics, the possibilities are endless.