Earnings Q&A Analyzer

Driving Meaningful Value for IR Analysts

The Problem

Earnings presentations are some of the most important events for Public Companies, and preparing for earnings is a primary (and high pressure) task for the users of our platform.

Problems We Identified

Low usage of earnings-related features during earnings season
Early client feedback uncovered people had a hard time finding relevant earnings content
We have high content costs related to earnings content, but were not seeing the ROI in customer value.

The Goal
Can we better understand what our clients need during earnings season so we can unlock value and deliver the best tools on the market for preparing for earnings?

My Role
Client Research, UX Design, Prototyping, User Testing, Hi-Fidelity Design, Developer Hand off (design specifications, scoping and grooming of stories)

Client Interviews

We recruited 8 clients for discovery research to dig into what our customers are doing today around earnings and uncover pain points and opportunities. These interviews helped us identify a key opportunity around preparing for earnings Q&A.

Key Findings:

IR teams have specific quarterly tasks related to earnings that require our data and content
Clients had low awareness of the earnings features and content in the platform
Pre-earnings workflow of question preparation as an key opportunity--a high-pressure time consuming task where client's always feel they should be doing more.

Proof of Value Experiment

To test the client value of our Earnings Q&A Preparation content, we added a quick download button to a personalized pre-earnings email that we recently launched to all users. The data we received from this experiment helped validate this project for the platform roadmap.

High click-through rate to content from email
Client Requests to add this content to the platform
Sales and Client Success teams use this feature to engage with clients

Feature Evaluation

We evaluated feature ideas based on core use cases, and unique value proposition.

Design Studio Sketches

Before jumping into wireframes, I ran a design studio with the key stakeholders and other design team members. The format is quick sketching sessions and then voting.

Ideas for quickly getting to key views of the content identified in research
Ideas for how to display the information so it is scanable with a high data density
Ideas for filter interactions
Ideas for insights and trends on top of the content
Ideas for interaction patterns and key user expectations for the feature

Wireframes

Because the scan-ability of the content was an important criteria, I created mid-fidelity wireframes for phase 1 to prototype some real data and validate our concepts.

Facilitate feasibility discussions with the engineering team
Visual reference for internal feedback sessions
Validation and alignment with stakeholders

Concept Validation through 1x1 Customer Interviews

User Testing Round 1

We tested the low-fidelity wireframes with clients and internally for early validation of our direction and to gauge the value clients find in the feature.

Pivot Points

Mixed reaction to the quick filter grouping
Support for adding additional download formats
Support for ability to select specific analysts
Confirmation that this feature is useful for more than just earnings transcripts

I developed quick filters as a new UX pattern to easily change several filters in one click
Research confirmed that options for format as well as options to extend the view are important to clients.
Search is key to this feature, so I designed the UI to be scan-able and use highlighting to draw the eye to the relevant content.

User Testing

Currently testing across clients as 1x1 monitored usability test via zoom.

Early Results

Emphasis on the analyst name may be the wrong hierarchy - considering using company or nesting the results in a pivot style.
Quick filters may not be capturing the way that all companies think about slicing the universe - considering removing them if they are not adding value.
Confirmation of the value in expanding search with custom topic detection capabilities and offering a quick summary of the key insights.