Case Study 1: Predictive text search for mobile shopping
Optimizing mobile shopping on outdoor retailer L.L.Bean's site
L.L.Bean's (LLB) Digital Experience team is a consulting client of Experient UX Design. Their UX and Digital Experience teams run frequent qualitative, moderated User Experience and Usability Studies on their .com to ensure delightful Customer Experiences and increased conversions from browser to buyer. The faster a shopper can find what they're looking for [or didn't know they needed] the more likely they are to make a purchase.
In the first part of this study, we observed users interacting with the Search feature on competitors' mobile sites with the goal of discovering user expectations and desires and identifying what caused confusion or clarity. Using these observations, we made recommendations to the current state of the LLB site and performed A/B testing to identify the option with the best performance.
Clearly defined goals
Outlining an objective definition of success is a surefire way to get relevant findings. Was a hypothesis disproved? Did users dislike a design? Maybe the project or idea itself isn't successful but you've still succeeded in gathering important learnings!
Comparing Competitor Search Features
Home Depot
Bed Bath & Beyond
Bed Bath & Beyond
Users were asked to find an outdoor heater on Home Depot (competitor retailer of outdoor furniture). This search feature bolds the user's entry with predictive text following in grey. It also provides matching search terms in a list under the query field.
Bed Bath & Beyond
Bed Bath & Beyond
Bed Bath & Beyond
On Bed Bath & Beyond (competitor retailer of home goods), users searched for a queen sheet set. This site greyed the user's entry and bolded the predictive text in a list of suggested terms under the query field; it did not use predictive text in the actual search field.
Sorel
Bed Bath & Beyond
Sorel
Sorel is a footwear competitor of LLB. Here, users searched for winter boots. This site did not use predictive text, but listed suggested products along with a picture, detailed product name and prices. This search feature did not use predictive text at all.
Translating observations of user behavior to actionable recommendations
The Experient Design Team synthesized observations of twelve (12) in-depth interview sessions to highlight key findings on mobile search behavior, usability and user expectations. Using video clips, participant quotes and screenshots, we compiled a flow chart to explain the variance in mental models between use case and user type. The context for using a mobile site's search feature is the most important consideration.
Making recommendations
After delivering a readout of the user testing to key stakeholders (UX/UI Designers and Product Dev Teams) we consulted the design team on A/B testing to determine the highest performing redesign of their mobile search feature.