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2018-01-23T17:49:34.982Z
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A few months ago I had the opportunity to chat with my friend and work partner Feras Alhlou, Co-Founder and Principal Consultant at E-Nor & Co-Author of Google Analytics Breakthrough. Feras and I have known each other for almost 10 years, and it is always great to hear more about the work that he and his first-class team are doing.

Here are the questions we discussed, checkout the answers in the video below. I have also added some of my favorite highlights from the interview after the video.

  1. [01:05] What's the process that you use to make sense out of data?
  2. [02:41]During this process, what do you actually do when you start working with data?
  3. [04:07]When analyzing data, how can we make sure that we are looking at the context to understand what is happening around us?
  4. [07:24]How can Data Studio and better data visualizations help companies make more data-driven decisions?

We believe analytics is a business process. We start with an audit, both from the business side and the technical side - we want to engage the stakeholders to understand how to measure what matters most to the business. Once we have the data in place, we go to the reporting layer - how do we report on this data? Then, we start to be able to analyze the data and find some actionable insights. Last,...

I have always appreciated the work of the Bill and Melinda Gates Foundation, it is really amazing to see people working so hard to make the world a better place. But I was left speechless when I opened their new report: GoalKeepers 2017. It tells the stories behind the data to help "accelerate progress in the fight against poverty by helping to diagnose urgent problems, identify promising solutions, measure and interpret key results, and spread best practices".

First and foremost, the goals themselves are superb - I can't think of more important issues to fight for. But I was also impressed by the information design, it is spotless. They used the right medium for each piece of information: text, images, videos, animations and charts. The report is engaging and, before you realize, you spent an hour going through it. So I was touched both as a person that cares about what is happening around me and as a professional appreciating good work.

Interestingly, a few months ago I was looking for some data to build a sample report, and I chose the maternal mortality dataset from UNICEF's data portal. I built the report and used it, but didn't take the time to publish it - ever heard of procrastination? :-)

In this article I will provide more context into GoalKeepers 2017 using publicly available...

Last year I wrote about the Marvel vs. DC war on the big screen. It was super fun to merge two of my passions (data visualization and comics) in one piece. It started with my curiosity to understand what all those movies are amounting to, and I think it helped me prove a point: Marvel is kinda winning :-)

One of the things that annoyed me was that I had to link to the interactive visualization, readers couldn't see the amazing charts in my article (!) - so I ended up including static screenshots with some insights explained through text. While some people clicked through to play with the data, I suspect many just read the piece and went away, which is suboptimal - when I publish a story, my goal is to allow readers to interact with it quickly and effectively.

I am extremely excited that now Google Data Studio allows users to embed reports in any online environment, which empowers us to create an improved experience for telling stories with data. This feature will be an essential tool for data journalists and analysts to effectively share insights with their audiences.

A year has passed since I did the Marvel vs. DC visualization, so I thought it was time to update it (5 new movies!) and share some insights on how to use Data Studio report embedding to...

A/B testing is the field of digital marketing with the highest potential to apply scientific principles, as each A/B experiment is a randomized controlled trial, very similar to ones done in physics, medicine, biology, genetics, etc. However, common advice and part of the practice in A/B testing are lagging by about half a century when compared to modern statistical approaches to experimentation.

There are major issues with the common statistical approaches discussed in most A/B testing literature and applied daily by many practitioners. The three major ones are:

  1. Misuse of statistical significance tests
  2. Lack of consideration for statistical power
  3. Significant inefficiency of statistical methods

In this article I discuss each of the three issues discussed above in some detail, and propose a solution inspired by clinical randomized controlled trials, which I call the AGILE statistical approach to A/B testing.

1. Misuse of Statistical Significance Tests

In most A/B testing content, when statistical tests are mentioned they inevitably discuss statistical significance in some fashion. However, in many of them a major constraint of classical statistical significance tests, e.g. the Student’s T-test, is simply not mentioned. That constraint is the fact that you must fix the number of users you will need to observe in advance.

Before going deeper into the issue, let’s briefly discuss what a statistical significance test actually is. In most A/B tests it amounts to an estimation of the...

LOR

Every mobile app professional today uses mobile app analytics to track their app. Yet there are some key elements in their analytics workflows that are naturally flawed. The solution is out there, and you might have missed it.

The flaw, and a fairly big one at that, is in the fact that app analytics pros sometimes focus solely on quantitative analytics to optimize their apps. Don't take this the wrong way – quantitative analytics is a very important part of app optimization. It can tell you if people are leaving your app too soon; if they're not completing the signup process, how often users launch your app, and things like that. However, it won't give you the answer as to why people are doing it, or why certain unwanted things are happening in your app. And that's the general flaw.

The answer lies in expanding your arsenal – adding qualitative analytics to your workflow. Together with quantitative analytics, these tools can help you form a complete picture of your app and its users, identify the main pain points and user experience friction, helping you optimize your app and deliver the ultimate product.

So today, you are going to learn how to totally revamp your analytics workflow using qualitative analytics, and why you should do it in the first place. You'll read about the fundamentals of qualitative analytics, and how...

Have you ever wondered how divorce and marriage rates have trended over the last 150 years? Or what reasons husbands and wives give when getting a divorce? Fortunately these, and other questions, can be answered with data. The UK Office for National Statistics make available two extremely interesting and rich datasets on marriages and divorces, providing data for the last 150 years.

Following the discovery of these datasets, I decided to uncover trends and patterns in the numbers, working with my colleague Lizzie Silvey. Two important questions were in our minds when exploring the data:

  1. Who wants a divorce and why?
  2. How do wars and the law impact marriage and divorce rates in the UK?

We discuss our findings in this article, but you can also drill down into the data using this interactive visualization that we created using Google Data Studio.

Divorce petitioners and their reasons

The ratio of petitioners has been stable since around 1974 (70% women and 30% men), the time at which both genders started having the same rights and divorce could be attained more easily.

In the charts below we see the trends for 'Adultery' and 'Unreasonable behaviour', the two most common reasons provided (out of five possible) - each line shows the number of divorces granted to the husband or wife for a specific reason.

Quick wins, low-hanging fruits - we're all looking for the shortest, most effective route to improve sales. As optimizers, it's what we do.

Think of the most important actions a customer can take on your website. Registering for a new account, making an inquiry about a product, filling out billing and shipping information - each of these vitally important actions are made through forms, which means: Optimizing those forms can have a big effect on your conversion funnel.

Of course, to optimize these forms, you have to understand how visitors are reacting to them, and what their behaviors indicate. And to do that, you have to measure their activity on your website or app.

Form analytics data is, perhaps, the most important information you can have to understand how your conversion funnel works. When you have access to this type of user data, you can start to see where you're losing potential customers, determine why they drop off, and create concrete steps towards funnel improvements that will reap huge rewards. All of this begins in Google Analytics and Google Tag Manager with Tracking Forms.

This article will show you how to create events in Google Tag Manager that will allow you to track the behavior of visitors interaction with forms. Having this data will enable you to optimize the forms to fit it with visitor expectations and increase the...

[Cross-posted from The Next Web]

Whether you are a marketer trying to persuade people, a technologist building a startup, or an executive making business decisions, data is your partner. You can use it to make better decisions and create insightful data stories inside and outside your company.

The first step is to accept your data relationship: you are partners forever. Once you understand that, there is an important consideration that will define how to tell your data stories: the context of where they live, which also defines the audience that will interact with them. In this post I will go through some important lessons I learned when visualizing and communicating data in and outside Google.

Data is your partner, live with it!

Data is no longer "next year's big thing", we have gone through that many times over and almost everyone accepts data as a valuable team member. But not everyone can understand and make use of it optimally, which means lots of decisions are still made based on intuition - if you don't believe me, check PwC's Global Data and Analytics Survey 2016, it shows some interesting numbers on how often managers use data during the decision-making process. Data education is a crooked road and we have a long journey ahead of us.

One of the reasons for that is similar to the well-known phenomenon called mathematical anxiety,...

So here's the deal: you've spent a ton of time with your data and you know it inside out. You've wrangled, sliced and diced it and are now the expert with this data for this problem. You've uncovered new, actionable insights that will lead to fantastic opportunities or improve your bottom line. Great! Time to show your colleagues or your boss or your clients these findings.

You open your data tool of choice, quickly create some charts and make it all look pretty with a flashy color scheme or fancy logos. More often than not, we fly through this final stage and don't give the data visualization step the due care it needs. This is insane!

Think about it. Your charts and dashboards are most likely the only piece of information your boss or client will interact with. The only information! And yet, here we are, creating default charts and missing the opportunity to really convey our message.

Effective charts are a compelling way to show your data. The human brain is simply better at retaining and recalling information that has been presented visually.

In this article I will discuss several techniques that will help you create more effective charts to communicate the underlying data.There's no big secret here. However, by applying deliberate thought, a handful of best practices, and allocating sufficient time in projects for the data visualization step,...