Benifex

Decreasing u-turns by 85% with an AI global search capability

The goal was to enhance employee engagement and content discoverability, something that could be validated by an increase in traffic to article and space pages.

My role

UX Designer

Duration

5 months

Team

1 UX Designer, 1 UX Researcher, 2 FE, 2 BE, 2 QA and 1 PM

Highlights

85%

decrease in employee u-turns

32%

increase in product NPS score

Defining the Problem

70% of employees who search for benefits end up going to intercom and customer support

70% of employees who search for benefits end up going to intercom and customer support

70% of employees who search for benefits end up going to intercom and customer support

What does a good search journey look like?

We knew that there was a high user abandonment rate due to poor discoverability journeys. As part of discovery alongside our UX researcher, I conducted competitive analysis, user interviews and user testing.

Notes from competitor analysis

Search touchpoint user flow

Why do we need a search?

We ran a series of … user interviews to understand what the current pain points were, focussing on information discoverability across the product suite. Some of the key findings included:

Navigation difficulty

User's noted it took a lot of clicks to find the information they were looking for

Lots of scrolling

Manually finding content within very long pages caused confusion and doubt

"I often think that I've been taken to the wrong place, but it's just taken me to the top of the page and I have to scroll down to find what I'm looking for."

Participant commenting on having to find content within very long pages

Design

Purchasing and redeeming gift cards in as few clicks as possible. User's can easily access their purchases in one place as well as see their purchase history.

Search flow

How do user's find the new search experience?

We ran 3 rounds of moderated user testing with 15 participants, capturing existing pain points and search behaviour before collecting feedback from the two design phases.

We also externally tested different prototypes on Lyssna with over 200 unmoderated participants. The aim of these was to better understand the type of content user's would expect to find as well as where they'd expect to see the search bar.

4.38

mean user rating, on a likert scale of 1-5, when asked how easy did you find this task?

Clearer categorisation

Users expressed confusion about whether results were topics, spaces, articles or people

"I think it's easier to immediately see what you want to get. You can actually see what the articles are about or the section they're in".

Participant positively commenting on the detailed context provided with search results

Search overlay

Search results page

AI overview

Impact

After the iterative production release of search, I utilised Hotjar recordings and MongoDB data to track user engagement and address any usability issues. I then put together an impact report utilising a year’s worth of data, including 3 months prior to the release of the first phase, the results of which are outlined below:

32%

increase in product NPS score

62%

increase in time user session time

85%

decrease in user u-turns

Search analytics

Greater insight into user behaviour

We were also able to track the most popular searched keywords and the destinations user’s were taken too, giving us greater insights into the content user’s were looking for and whether their search journey’s were successful or not.