Designing a successful digital product is tricky and challenging. In the crowded space of the WWW, your product may not end up in search results, and this is why findability precedes usability in designing for the web. If your product can’t reach your audience, it has failed already.
Therefore the big question here is how do you ensure that your product is easily found and accessible by your target audience?
The answer to your question lies in information architecture. Design a superior user experience (UX) using a tree test.
Top 4 Reasons Why Your Business Needs Tree Testing
Still unsure of what a tree test can do to your design venture?
Let’s take a look into the true capabilities of a tree test.
Primarily, you would be running a tree test because of any one of the following reasons.
- baselining an existing tree
- detecting the problem area and points thereby establish a base score
- experimenting test trees trying to solve existing information architecture problems
- comparing each version against each other to find the best possible solution to the existing problems
However, a tree test also does a lot more for your product. Following are a few primary areas where a tree test can be beneficial.
Why It Pays To Use A Tree Test
1. Evaluate product navigation
A tree test can improve your digital product’s online findability. With the test tree, you can evaluate your product’s existing information navigation system. How? Well, users complete a series of tasks looking for items using the site structure. Using this method to evaluate your site structure, you have a way to measure how easy it is for users to find things.
Treejack is one of the most popular tools used for tree-tests.
With a remote tree test, the users can be located anywhere, and they can take the test in their own time. You benefit from getting quality, quantitative data.
By using online test conduction, results accumulate from global users. Thus, it reduces the costs of on-premise tests.
The biggest asset of tree testing is that it is designed for experiments. The test sessions are concise, each test of around 15 – 20 minutes, having a maximum of 20 tasks per session.
This improves the success rate of completing the test by users significantly. These tests, like card sorting, are pretty much simple with low complexity that readily helps with the dropout rates.
Combined with remote access to the test, all this makes data collection fast and the data analysis process lean. This means whatever insights you derive from your test analytics, you can apply them in no time.
How To Optimize Your Tree Tests
Ensuring you get the maximum benefits out of your tests is one of the most important yet neglected design tests areas. But if you can make sure to ask the critical questions without fail, your tests will not fail you.
Following are a set of core questions that can ensure you make the most of tree testing.
1. What is the objective of your tests?
This is perhaps the anchor point of your complete questionnaire. This answer is going to help you achieve the hyper-targeted activities for your test. For instance, you may want to analyze the results of your design changes in the navigation structure.
2. Who is your target audience?
Answering this question right in the beginning can save you a lot of pain in the long term. A good practice here is to take some time out and think deeply about your product visitors.
If your answer is: “everyone”, then you are doing it wrong. Remember, if everyone is your audience, then no one is your audience.
3. Define the independent variables
Deciding on the independent factors that are dynamic can produce multiple end-results.
Use factorial experimental designs. This method enables you to examine each variable in isolation.
4. Define the dependent variable.
Dependent variables can also bring a lot of improvements to your overall user experience. To assess its impact, you have to observe its effect on its respective independent factor instead of the end-user interface result. For instance, you can examine the precision of an area locator for completing preset tasks.
5. What do you use while comparing trees, a control group or a treatment group?
Control Group acts as a benchmark. It is not vulnerable to changes. Here, comparing the altered results to the original group is possible. You can assess the degree of difference between the results of both groups.
6. While comparing testing trees, do you use a between-subject design or a within-subject design?
It’s important to decide whether you want the same group of test-takers to participate in all the versions of your test tree or anyone.
Whether you choose both strategies or not, both have their own merits. Therefore, it would be best if you decide based on your test goals. For instance, choosing a between-subject approach can reduce the test taker fatigue, and the learning effect could even prove to be time-efficient.
The whole point of executing a tree testing activity is to ensure you stay updated about your target audience’s behavioral nodes. This means conducting frequent tree tests can boost the overall information architecture health of your digital product.
The above tips and tricks are definite to help you optimize all your tree testing campaigns, but the heaviest success metric still lies in your hands.
Ensuring you understand your target audience will make sure that your design assumptions walk the closest to reality.
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