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.
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?
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.
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.
But don’t sweat it, in this post, I will try to clear up a some of this confusion by introducing a simple, yet powerful framework – the intelligent agent – which will help link these new ideas with familiar tools and concepts like A/B Testing and Optimization.
Web personalization is all the rage, but are you trying to run before you’ve learned how to walk?
Here’s another presentation from CXL Live 2015 (sign up for the 2016 list to get tickets at pre-release prices).
While optimization is fun, it’s also really hard. We’re asking a lot of questions.
Why do users do what they do? Is X actually influencing Y, or is it a mere correlation? The test bombed – but why? Yuan Wright, Director of Analytics at Electronic Arts, will lead you through an open discussion about the challenges we all face – optimizer to optimizer.
CXL Live 2016 is coming up next March (get on the list to get tickets at pre-release prices). We’re going to publish video recordings of the previous event, and here’s the first one.
You run A/B tests – some win, some don’t. The likelihood of the tests actually having a positive impact largely depends whether you’re testing the right stuff. Testing stupid stuff that makes no difference is by far the biggest reason for tests that end in “no difference”.