Chart Annotation @ Fullstory
Expand and retain good-fit customers
Type
- Industry Work
- Product Design
Duration
- 3 months
My Role
- Product Designer
What I Did
- Product Design
- UX Research
Project Overview
Add context to data changes with annotations
Fullstory is a behavioral analytics platform that aims to combine qualitative and quantitative insights. In FY25, Fullstory made a big bet in focusing on good-fit customers in retail, e-commerce, food, travel and finance industries. These good-fit customers rely heavily on real-time data to monitor website performance, sales numbers and A/B testing outcome. In other words, data teams across these industries need a way to capture changes caused by outages, launch of new products or A/B testings in their data to help inform critical business decisions.
The launch of annotations not only fulfill user needs of adding context to changes in charts but also receive a lot of love from Fullstory enterprise customers with high adoption rate at 18.83% within 1 month of release without any use of in-app announcements.
Business Opportunities based on User Insights
Data teams present with charts to show impact
Apart from bringing new capabilities to help data teams better understand the changes in the data, I noticed opportunities for customer expansion while reviewing notes from customer calls – Data teams within large organizations often include screenshots of charts from Fullstory in their presentation to the leadership team. Moreover, in day-to-day conversations, data teams share links to Fullstory dashboards to shed light on changes in data with cross-functional partners.
Along with the launch of unlimited seats in Fullstory, I see annotation as an experiment in deepening account penetration beyond merely a way to help data teams recall events. Throughout the process of building annotations, I helped the team make key product decisions based on the formation of an expansion loop.
Design Challenge
How might we foster conversations without bringing distractions while increasing perceived value beyond data teams through annotations?
Two of the most heated debates I had with my triad throughout the process were
- How visible should the ability to add annotations be?
- How are annotations on charts different from notes or comments?
Slapping an annotation on a chart is easy but it should not distract data teams from understanding and interacting with data. Moreover, how might we know whether the annotation feature is a better solution compared to notes or comments when it comes to bringing attention to an event that leads to a change in data? Does the new concept of an annotation bring more confusion or collaboration – How annotations play a role in fostering a more collaborative data analysis process is the key to keeping the expansion loop rolling.