B2B E-Commerce Case Study:

How Tree Testing Improved Baileigh’s Product Findability by 85%

Sales reps were overwhelmed by calls from site visitors unable to find small-ticket products online. IA research helped the company overhaul its site structure and increase self-service, web sales, and sales team productivity.

Challenge: Users Struggling to Find Products

Years ago, we began working with Baileigh Industrial, a leading maker of industrial metalworking and woodworking machinery. One of the first things we did was conduct 1:1 interviews with Baileigh’s sales reps. A major pain point among the reps was website visitors calling to ask about small-ticket products, which limited the amount of time they could spend talking to big-ticket product shoppers.

Approach: Tree Testing & Card Sort Studies

Designing the Study

  • We focused on Baileigh’s metalworking products, which was the most visited section of the site.
  • We recruited 64 users for the test. We found most of them through an ad in the company’s email newsletter. A smaller number came via the company’s social media pages.
  • In a post-test questionnaire, 32% of participants classified themselves as metalworking experts, 55% as intermediate, and 13% as beginners — which matched the mix we were seeking. About 50% had purchased Baileigh products.
  • We used the Treejack platform from Optimal Workshop to run the test.
  • The test consisted of 8 possible tasks, each one focusing on a different top-selling product category. An example: You need to precisely bend some quarter-inch sheet metal into a V shape on a vertical plane. Where would you look?

A demonstration of a tree testing scenario and how a qualitative research participant might think aloud during the task.

Analyzing the Data

Analysis of the task demonstrated earlier.

On tasks where many users took the wrong path, we wanted to know the most common wrong paths. Pietrees allowed us to visualize the click data and spot navigation patterns.

  • Overall performance was poor, with an Overall Score of 4.0 out of 10 across the 8 tasks.
  • There were too many top-level product category choices, leading many users down the wrong path from the start.
  • Some navigation hierarchies overlapped and were not intuitive to users.
  • Similar or misleading subcategory names caused many users to pick the wrong targets.

Research-Driven Changes

  • Broadened categories so that users have an easier first choice and are not funneled away from their target by a single wrong click.
  • Reevaluated and reworded problematic subcategory names to prevent confusion.
  • Included context for tools’ function where possible.

Result: Easier-to-Find Products

Users found the right product category more often and more quickly with the new IA.

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