An organized infrastructure is needed to build an optimization practice, which involves the resources and availability of an entire team. Sounds overwhelming, right? Building a strong optimization practice doesn’t have to be though. With more hands on deck instead of just an individual effort from an analyst, you’ll be able to test your way to new heights and gain the transformational insights that your company needs.
However, if your company hasn’t been doing any testing to begin with, this can be a disruptive process, especially for product teams. For example, if all of a sudden what the team has been building goes through an experiment at the end of the development process, the success of that experiment will impact the future of product development. That is why it is important to have a plan in place.
In this article, we’ll walk you through how to build a strong optimization practice so you can benefit and be well-prepared.
First, it’s important to define the roles and team workflow when building a core optimization team. In other words, this is the “who does what” part of the process. Let’s take a look at the team members involved in building an optimization practice and what they do.
Keep in mind that that isn’t a one-size-fits-all approach. For example, smaller teams could have a few people that simply wear multiple hats.
The next step in building a strong optimization practice is ideating and prioritizing testing. If you do this correctly, you’ll have a backlog of testing ideas so that you have plenty of starting points for testing your data. The goal is to be able to collect these different ideas that come from different sources and have them in one place and ready for prioritization. It’s important to develop a prioritization framework to help shortlist and prioritize future testing based on different objectives. Some variables may include:
The team will then use any relevant insights to gather knowledge from relevant stakeholders and better grade each idea.
After you have all of your ideas collected and prioritized, the next step would be to run experiments on your data. Every testing plan should contain the following:
Running experiments is not just done by the analysts. In fact, there are multiple team members involved in building a strong A/B testing process. Once you implement these steps, you are off to the next part of how to build a strong optimization practice.
Analyzing results has two different components: the actual analysis of the results, and then sharing the results. It’s not just understanding the nuances of the results, but making them available to everyone involved in the process so they can understand the importance of their work.
The objective of analyzing the results is to better understand the user behavior and generate new ideas for future iterations. When sharing the results, the objective is to help with making the optimization program more visible within the organization and also influence the culture and promoting of more data-driven decision-making.
When running individual tests, you might have one that is successful, and you might have one that is unsuccessful. Therefore, there is value in individual experiments because the more you do it, the more you know what to test to better optimize your business. As a whole, building an optimization practice makes your business more data-driven from a day-to-day perspective.
With the above four steps, once you have a strong optimization practice in place, you’ll have incremental increases in your conversion rate, which can lead to a boost in revenue. What company wouldn’t want that? Just remember, building your optimization practice is a team approach, so there’s no need to feel overwhelmed. With the right people and steps in place, you’ll be converting users in no time.