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How to Build Robust User Personas in Under a Month

Customer personas are often talked about in marketing and product design, but they’re almost never done well.

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How to Deal with Outliers in Your Data

One thing many people forget when dealing with data: outliers.

Even in a controlled online A/B test, your data set may be skewed by extremities. How do you deal with them? Do you trim them out, or is there another way?

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12 A/B Testing Mistakes I See All the Time

A/B testing is fun. With so many easy-to-use tools, anyone can—and should—do it. However, there’s more to it than just setting up a test. Tons of companies are wasting their time and money.

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How to Analyze Your A/B Test Results with Google Analytics

A/B testing tools like Optimizely or VWO make testing easy, and that’s about it. They’re tools to run tests, and not exactly designed for post-test analysis. Most testing tools have gotten better at it over the years, but still lack what you can do with Google Analytics – which is like everything. Keep reading »

Survival of the Fittest Variation: Evolutionary Algorithms in Optimization

If you read this blog regularly, you probably don’t need an introduction to CRO or A/B testing. You know the major players, best practices, and you’ve likely tested your fair share of ideas.

But, as an expert, you likely know some of the persistent frustrations with current approaches. To name just a pair:

  • Testing simply takes time.
  • Our best instincts are often wrong.

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How to Do Conversion Attribution Modeling

Customers don’t usually see one ad and then click over to purchase.

In reality, the path is much more complex, and usually includes various marketing channels – organic and paid search, referral, social media, television.

But if you’re a rigorous and data-driven marketer, the question has to cross your mind: how much credit can I give each channel for this conversion?

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How Many Variations Can You Have in an A/B/n Test?

Just when you start to think that A/B testing is fairly straightforward, you run into a new strategic controversy.

This one is polarizing: how many variations should you test against the control?

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How to Make More Money With Bayesian A/B Test Evaluation

The traditional (and most used) approach to analyzing A/B tests is to use a so-called t-test, which is a method used in frequentist statistics.

While this method is scientifically valid, it has a major drawback: if you only implement significant results, you will leave a lot of money on the table.

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Intelligent Agents: An A.I. View of Optimization

As a digital analyst or marketer, you know the importance of analytical decision making.

Go to any industry conference, blog, meet up, or even just read the popular press, and you will hear and see topics like machine learning, artificial intelligence, and predictive analytics everywhere.

Because many of us don’t come from a technical/statistical background, this can be both a little confusing and intimidating.
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10 Statistics Traps in A/B Testing: The Ultimate Guide for Optimizers

Even A/B tests with well-conceived test concepts can lead to non-significant results and erroneous interpretations. And this can happen in every phase of testing if incorrect statistical approaches are used.

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