Search redesign boosting conversion rate by 12.8%
Role
Design
Team
Product, AI & ML
App
Surface Web
Timeline
3 Months
About Finder—Cyndx's discovery platform
Finder is built to facilitate mergers and acquisitions. Our users are primarily bankers who are looking for a list of potential targets to approach. This application sifts through 28 million companies using a proprietary AI algorithm to find the right target companies with the highest precision.
Problems with search
Finder relied on a faceted search, requiring users to select from a predefined list of companies and 'concepts' (keywords). Users struggled to understand 'concepts,' and slow load times (8+ seconds) led to frustration, especially for those needing multiple attempts to refine results.
#1
The platform used a faceted search requiring users to choose from a predefined list of companies and keywords—that we termed 'concepts'.
#2
Each search took ~8 secs to load and many users provided feedback that it took many tries to get the right results.
Goal
Help users find the right target company organically, with fewer tries and find results with highest precision.
Introducing free-form search
Before: Limited faceted search
After: Introducing free-form search with recommendations
Making empty states useful
Allowing users to explore and experiment with new search UI
Iterations
Based on customer feedback from the support team and observations made during the sales demos with the current platform, we discovered three recurring pain points.
Results
Conversion rate
12.7%
from 8.2%
Net Promoter Score
58
from 42
Time spent/session
~4.5s
from 12.6s
Principles that guided this work
Being intentional about the terminology
This new product enables users to search for companies using Natural Language Processing (NLP), essentially functioning as a "free text search." However, it took some time for our internal teams to adapt their language to this new concept. I had to be deliberate in explaining what "free text search" entails and how it operates in the context of company searches, as it isn't as open-ended as one might expect. Users are still encouraged to apply filters to refine their search results.
Learning to leverage AI
Creating an AI-powered search tool provided me with valuable insights into the capabilities of AI algorithms. It allowed me to anticipate user behaviors and expectations in advance. Collaborating with the data team was also beneficial, as it helped me understand the complete data landscape and discover ways to present data within the context of searches.