E-COMMERCE PRODUCT TAXONOMY FOR APPLIANCES
Best Buy appliances have been a pillar growth area in a competitive market for the last couple of years. I drove the redesign for a section of the website's information architecture with the goal to help users find and discover products by aligning the taxonomy with customer mental models through card sorting and tree testing. Not only did this helped build consistency between the website's global, local and on-page navigation elements, it also improved the discoverability of our wide assortment of products and ultimately resulted in improved findability and usability for customer wayfinding.
I lead the research, testing, design, and implementation for the information architecture of Best Buy's small kitchen appliances product taxonomy. I collaborated with a business specialist and SEO specialist on the analytics and labeling strategy.
To make a purchase, customers need to be able to easily find the products that they are looking for. I started by conducting a series of interviews with our buyers responsible for category sales. A major goal among them was to bring visibility to the wide assortment of small kitchen appliances that we carry online. The same frustration was echoed by e-commerce executives, seeing a low rate in customers reaching a purchasable product page in the small kitchen appliance category versus other comparable categories. As I dove into an investigation, I realized a barrier to product findability. The category labeling was not representative of the products in that category, and I hypothesized that the products within categories were not organized to align with customer expectations and mental models.
Improve the discoverability of our wide assortment of products
Match the product taxonomy to customers expectations
Key Performance Indicators
Increase small kitchen appliance traffic to category pages
Increase % to PDPs
Decrease exit rate on product listing page
Decrease volume of search queries on product listing pages
Research & Discovery
I started by identifying the key problem areas that could benefit from reorganization by looking at the following web metrics with Adobe Analytics.
Traffic to Categories
How is traffic compared with other similar categories?
Are there other factors that explain traffic disparities? (other navigational elements)
The expectation of traffic based on the importance of the category
Is the category strategically significant even if it has low usage?
Does the page drive traffic to other important pages?
Is content part of a longer user journey, which requires multiple visits before conversion?
Bounce Rate on Product Listing Pages
These pages are meant for customers to narrow down their selection to a product
Does the label name describe the category accurately?
Do on-page elements prevent people from understanding the content?
Based on internal and external factors, is the entrance rate lower than expected?
Does the category have a high conversion rate despite a low entrance rate?
Search Volume & Queries
Is the search query already represented by a category?
Are customers noticing facets on the page to help narrow their search?
What are customers searching for?
Based on the findings, I decided to focus on two categories that had the most opportunities for improvement.
1. Coffee, Tea & Espresso
2. Fryers, Grills and Specialty Small Appliances
I evaluated the current product taxonomy for the category using an organization system focusing on organizational schema and principles. I saw opportunities for recategorization as well as create additional parent-child relationships in the hierarchy. I also conducted a competitive analysis looking at how our competitors were organizing their product hierarchy and paid attention to the similarities and differences. I found that our taxonomy did a poor job in satisfying Millers Law and Hicks Law to help customers remember our assortment and make a decision.
Tree testing is the fastest, most effective way to spot problems with a site’s information architecture. I worked with our UX designer to ask representative users to find products or information using a simple, clickable “tree” of the site’s navigation; and we record each click. The resulting data shows which category structures and labels are intuitive to customers (and search engines), and which ones likely cause confusion, abandonment and lost sales.
Here's the approach we used for our tests:
We focused on growth categories with areas of improvement within small kitchen appliances.
We recruited 15 users for the tree tests. (Technical limitation)
We set up different screeners for the different tests.
We used Treejack from Optimal Workshop to run the tests and UserTesting.com to recruit participants.
Here’s what we learned from the study:
1. There were too many top-level product category choices, leading many users down the wrong path from the start.
2. Category label names sometimes were not representative of the subcategories and were not intuitive to users.
3. A high number of subcategories increased decision-making time for users.
Based on these findings, we recruited 20 participants using Card Sort from Optimal Workshop to:
Understand how customers naturally group products in their minds.
To identify intuitive category and subcategory names.
Here are some results that came out of the card sort with the affinity between products highlighted in yellow:
Strategy & Design
New Product Taxonomy
With the testing data, I designed a new information architecture that:
1. Broadened categories so that users have an easier first choice and are not funneled away from their target by a single wrong click.
2. Reevaluated and reworded problematic subcategory names to prevent confusion.
3. Shortened the number of categories within parents by creating parent-child categories to reduce cognitive load.
We followed this up with a second tree test to compare the usability before and after. We reused the original tasks and recruited a similar group of 15 participants and compared the scores of the current state hierarchy to the proposed hierarchy.
New Label Names
With insight from the data gathered above, I worked with our SEO team to establish the right keywords to use for our category label names. Because the parent and child pages sometimes contained similar keywords, it was important to establish which pages to choose to rank based on traffic and customer intent to reduce keyword cannibalization. In addition, we reviewed the existing category groupings that appeared in the card sort tests and used those as guidelines to come up with terms that customers were already familiar with, while also looking back at our competitor analysis to see what labels other sites were using as well.
I used the new information architecture as the foundation to align the website's global and local and one page navigation elements.
We monitored our KPIs for three weeks and in our post-analysis and compared the categories that were affected by the redesign to the same time frame from the year before prior to the changes. We saw significant changes in numerous categories where the traffic to the category and % to PDP both increased, and search volume declined, suggesting that customers are now better able to discover products that were originally hidden in the IA and better find the products that they are looking for.