Releasing new products into the world is both fun and exciting. For most product teams, researching, designing, and building these experiences generates a lot of interest and support. Everyone wants to be a part of the new stuff, because that is the innovative stuff, right? Hmmm, nope.
Ok, I set you up there. So it isn’t always building something entirely new. Sometimes it is completely overhauling an existing experience using new frameworks and new technologies. Or maybe it's redesigning the visual experience from the ground up. That’s innovative, right? Well, maybe.
I know, I know. I set you up again. The truth about innovation is 99% of the time, it happens gradually. It requires meticulous persistence. Truly innovative solutions are most often the result of experimenting, testing, and iterating repeatedly. The most valuable part of this process is we learn so much from our experimentation, we can now see the things that were not obvious to us before. We understand how and why our solution works so well because we’ve identified which levers to pull to influence certain results. We can apply this knowledge to all of our future endeavors. It’s a combination of both art and science.
The DXO process consists of 4 basic steps — Identify, Hypothesize, Test & Evaluate. It is intended to be lightweight and fast. The ultimate goal is to get to learnings quickly so you can test again, repeating the process until we have something significant to implement. Let’s take a quick look at what is involved in each phase of the process.
Define the experience we want to optimize and the problem(s) we need to solve.
Explore possible solutions to the problem by reviewing existing data and research.
Validate our hypotheses through testing. First decide how we will test our theory (A/B test, usability test, etc.). Then determine what we will measure to determine a successful result.
Analyze the results and determine the next steps. We may decide to implement the proposed solution, make adjustments based on learnings and test again, or go back to the drawing board.
The DXO Strategy Tools
The steps in the process seem simple enough, but sometimes it is hard to get started. Lots of people have ideas and opinions about what to do, and most of them are valid. Getting everyone on the same page is the real challenge. This is where the DXO strategy tools I developed come into play. These canvases allow product, design, engineering, and marketing teams to align on what resources are available and how optimization strategy will be carried out.
The Optimization Strategy Map and Experimentation Canvas I created were designed to be quick and easy to fill out. In most cases, it can be done in 10–15 minutes. Some data may need to be gathered before starting, but even then, it should not take long to complete.
These tools are also scalable. Because they are contextual, they allow us to determine what will work for our organization, regardless of size or resources. Once complete, the canvases can be easily shared out to stakeholders and partners to initiate larger conversations. Finally, they result in a clear and actionable path forward creating the best experiences possible.
Optimization Strategy Map
The first resource in the DXO toolkit is the Optimization Strategy Map, which is used to provide a high-level plan. The map can be divided into four primary categories of information. Below is a breakdown of the different sections of the map.
Identify the product or experience we want to optimize. This can be an entire product or a specific experience within a product.
List all the areas of the experience that we would like to measure. For example, if we were optimizing a SaaS CRM platform for small- and medium-sized businesses, we might list the Pipeline Dashboard, Communication Tracker, Email Template Designer, and Workflow Automation Tool in this section.
In this section, we will identify any quantitative user research methods available to us within our organization. Common practices include A/B testing and surveys.
List the most important analytics we are tracking within our products. Using our hypothetical SaaS CRM platform as an example, the number of contacts created, the number of email templates created, the number of emails sent, or the number of custom workflows created might be things we would track.
In this section, we will catalog all the qualitative user research methods available to our teams. Some examples include usability testing, interviews, or diary studies.
The iteration plan describes how we plan to work to improve the experience from a high level. Here we can define goals for specific target metrics to achieve with these efforts. Using our SaaS CRM example, we might target adoption of rate of specific features or reducing support calls by a specific percentage.
The cadence section lays out how often we will test things and which validation methods we will use. Depending on website traffic, we could aim to run 4 A/B tests per month. Maybe we want to launch 1 user surveys per quarter or a UX benchmark study after every major release. Keep in mind, these are goals that are highly reliant on our team's resources and capabilities. What’s right for our organization, may not work for others.
Experimentation Plan Canvas
While the Optimization Strategy Map focuses on the overarching strategy, the Experimentation Canvas focuses on specific experiments we will run. It ties directly to the DXO process steps discussed earlier in this article. Through a handful of simple questions, it allows us to define a test plan that we can share out and put into practice.
- What are you testing?
- What do you know (about the problem from the research and data your organization has done)?
- What is your hypothesis?
- What will you test?
- What is your primary metric?
- What are your secondary metrics?
- How will you iterate?
By completing the Optimization Strategy Map first, we will have already gathered much of the information needed to complete the Experimentation Canvas. Using these tools together, we can define an overall strategy and develop a detailed experience improvement plan for every area of our product. Even better, this framework can apply to an entire suite of products. It is infinitely scalable. Using this approach, we can tap into the tried-and-true formula for innovation. Repeated experimentation and learning. It is the most historically consistent means of innovative problem-solving.
In my next article in this series, I will walk through a case study of how my team used the toolkit to plan out the roadmap for our browser extension. Until then, download the DXO Toolkit, which includes examples of how to fill it out.