Raise your hand if you’ve ever struggled with a decision between disciplined testing procedures and expedient decision-making.
For example, think of a time when you’ve had to decide between sticking to your A/B test design—namely, the prescribed sample size—and making a decision using what appears to be obvious, or at least very telling, test data. If your arm is waving vigorously in the air right now, this post is for you. Also, put it down and stop being weird.
Sites that don’t work, don’t convert.
That’s why optimizers conduct quality assurance on sites, landing pages, test treatments, email campaigns, you name it—to make sure they work the way they’re supposed to.
While it’s common knowledge that quality assurance is something you should do, not enough optimizers complete it properly. If they did, there wouldn’t be so many sites that just plain don’t work.
At some point, personalization may seem like a good next step to level up CRO efforts. But companies—if and when personalization does make sense—often try to use algorithms immediately, relying on AI and machine learning to create personalized experiences.
Many also get started with marketing tools that have data collection and AI built-in, resulting in a fragmented experience for the customer and suboptimal results for the company.
What happens when a marketing generalist asks a team of CRO experts for ideas on “low-hanging fruit” or “quick wins”?
Google Analytics shows 104 conversions. Your CRM shows 123 new leads. Heap reports 97. And so on.
It’s easy to get frustrated by data discrepancies. Which source do you trust? How much variance is okay? (Dan McGaw suggests 5%.)
For most companies, Google Analytics is a—often the—primary source of analytics data. Getting its numbers aligned with other tools in your martech stack keeps results credible and blood pressure manageable.
Optimizing the sign-up flow is a never-ending saga for SaaS companies, for whom it’s mission-critical to acquire and activate users as quickly as possible.
When it comes to online imagery, it’s not so much about having images as making sure those images give the visitor a sense of texture, size, scale, detail, context, brand.
According to MDG Advertising, 67% of online shoppers rated high-quality images as being “very important” to their purchase decision, which was slightly more than “product specific information,” “long descriptions,” and “reviews and ratings”:
We talk a lot about creating high converting landing pages, getting traffic that converts, and making the most out of your conversion points.
But what we don’t talk about often are outside-the-box landing page strategies you can use to increase conversions right away.
About two years ago, I wrote an article on using Google Tag Manager (GTM) to personalize your website. Even then, people asked why I wouldn’t just use Google Optimize. At the time, the answer was simple: Personalization was part of Google’s six-figure paid solution.
However, in November 2018, Google released the functionality to all users. Since then, Google Optimize has become a primary platform to initiate personalized experiences. But GTM is still critical to overcome its enduring limitations.
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?