Conversion

In this special episode, Matt Wright talks with Brent Kostak, Product Marketing Lead for Optimization and Experimentation at Adobe, and David Arbour, Senior Research Scientist at Adobe Research. Together, they explore the launch of Adobe’s Experimentation Accelerator, a new AI-first platform built to automate and scale experimentation programs across enterprises.

How do you take an immature experimentation organization – the kind that runs one or two a/b tests a month – and turn them into a booking.com? This is a question that we – and many of our clients – have been trying to answer for years. We’ve approached  this problem from many different angles.

If you want to know what your users are doing on your website, A/B tests are your go-to tool. But if you’re looking to uncover why they’re doing it, user testing is the key to unlocking those insights. One common question that arises is whether to conduct moderated or unmoderated user testing to gather insights. Both approaches can uncover the “why” behind user behavior insights that pure analytics or A/B testing alone might miss.

What question keeps product leaders up at night? Crying babies, anxious dogs, general unease about the state of the world. Oh, and: “Is our product priced right?” And that’s for good reason – pricing can make or break your business. If you get it right, you unlock massive value – selling your product at the optimised price to balance profit and demand. (But get it wrong and you leave serious money on the table.) The problem is, there’s not always a clear answer to the question.

There are many good reasons why marketing and product departments should work together on A/B testing, from driving efficiencies to creating better experiences. However, achieving harmonious cross-team collaboration is difficult, and most businesses want more than “good reasons.” So here’s the TLDR: A study by Kameleoon found that companies are 81% more likely to grow when product and marketing strategies are aligned.

In the fast-paced digital world, businesses constantly seek ways to understand their customers better and innovate without unnecessary risk. The problem? Developing new features or products based on assumptions can be costly and often leads to disappointing results. Enter painted door tests. Painted door tests are a clever, cost-effective type of experiment used to gauge user interest in potential new features before fully developing them.

Here at Conversion, we’re always looking for ways to create an unfair competitive advantage for our clients. Nothing allows us to do this more effectively than our Experiment Repository. As far as we know, our Experiment Repository is the largest, most robustly tagged collection of experiment data in the world. By putting this one-of-a-kind resource at our clients’ disposal, we’re able to give each of them a sizable, one-of-a-kind edge over their competition.

Confidence AI: the next generation of A/B test prioritization

Most redesign projects will fail, but businesses will often be unable to identify why. Have you experienced the frustration of a website redesign that backfired, leading to a decline in conversion rates? Or does your brand find itself caught in a costly cycle, redesigning the website every few years without seeing significant improvements in customer experience or sales? We’re here to explain why complete redesigns can harm businesses and why it may not be the solution you’re looking for.

As experimenters, we often overlook the distinction between a hypothesis and its execution. A hypothesis represents a theory we aim to validate, while an execution is how we specifically plan to validate it. It’s conceivable to possess a robust hypothesis supported by evidence and yet execute it poorly. This raises a question: What measures can we take to ensure our execution effectively validates our hypothesis? The ALARM protocol is not just another tool in the world of experimentation.