It would be tough to argue that any other industry has been hit harder by the COVID-19 pandemic than the travel industry.

When the holidays rolled around in 2020, the shopping industry looked very different, as there was less bustle in the stores and more bustle on the keyboards. Companies had to adapt to the increase in traffic volume and demand for online shopping. During the holiday period, Optimizely customers leveraged all their experimentation capabilities to create

As an experimentation program manager there will be plenty of moments where the phrase “herding cats” hits a little too close to home. Experimentation is a new set of processes and analytical rigor that require consistency, and we know consistency doesn’t happen overnight. Don’t be discouraged! Those are normal growing pains and the type of

To have a successful experimentation program, you have to start with “what is the goal?” of your growth and experimentation efforts.

When you’re starting your experimentation journey it can be tough to figure out where to start.

Personalization is talked about a lot. There is an expectation from users to deliver a tailored, hyper-relevant experience for them that fulfills their needs quickly with more relevancy. That may seem like a great goal, however, you should not operate with the assumption that more personalized equals better. Personalization should still be treated as experience

Optimizely’s Full Stack enables product teams to improve their visitors’ experience in a quicker, easier, and safer way than the typical product development process by providing the tools to experiment, optimize and rollout new features to ensure value is delivered to your customers. No matter what part of Full Stack you are using, you will

Some of the posts I’ve written in the past on key components of building a strong and scaling experimentation program seem obvious (such as having an executive sponsor), but that’s just table stakes if you want your program to be successful. Some components are not difficult, but doing them will ensure success and scale. Yet

Scaling experimentation is often the toughest challenge for organizations as they start to see the tangible results of experiments. Companies start to focus on how to make the practice a reality in all aspects of their business. Determining roles, responsibilities, and workflows of an expanding experimentation program may seem like a daunting task, but it

As we work with our customers to help them understand the results and learnings from an experiment, one of the main questions we hear is “why am I not reaching significance?” or, more specifically, “why is this experiment inconclusive?” When I hear this question, my first check is to look at what metrics are set

When starting your program, you may not have built out your backlog of experimentation ideas just yet. Or you may need to fill your backlog with ideas from new parts of the organization. Maybe you’ve been really focused on iterating on successes and it’s time to take a step back and refill the tank with

As the Lead Strategy Consultant at Optimizely, I’m lucky to work with customers of differing levels of Experimentation program maturity, from customers who are just starting their program and getting their team in place to customers that never make a decision without experimenting first. My work focuses on building Experimentation methodologies with a company’s teams