Increasing trust among users by leveraging community-based data

January to March 2023

THE PRODUCT

This is a powerful map-based tool that helps our client make intelligent decisions for their cell phone coverage company.

The platform helps our client analyze areas to improve their business by:

  • Expanding and improving cell phone coverage
  • Developing new brick and mortar stores
  • Competitor acquisition

THE PROBLEM

Research showed users struggled to find reliable data to make reports and presentations

This lack of data validity caused a lack of trust between users and the platform, apart from increasing errors in the data reports.

SOLUTION PREVIEW

A two-pronged solution addressed users' lack of trust in the data and reduced repetitive tasks.

Here is a preview of the solutions, before jumping into the process.

Solution 1

The Hub Solution allowed users to find validated data quickly.

Solution 2

The new Search includes a description, previews, and tags to improve filtering.

OUR TEAM

As a small consulting team, our task was to improve the usability of the platform.

To do this, my co-designer and I interviewed a handful of users to test this idea and reported back to the rest of the team: 3 developers and the Product Manager.

USER GROUP

Our users are unique. They’re engineers who use maps to identify areas to improve cell phone coverage.

The engineers’ main goal is to create reports or presentations for their teams or for leadership. They do this by using data to analyze specific geographic areas. Once they’re done, users will export reports or share workspace links with other users or their team.

RESEARCH

But in order to increase trust, we set out to understand: how were they using the platform? We talked to a handful of users to find out.

Constraints

  • Our client granted us limited access to users and back end data, based on their priorities
  • We worked within a limited design system
  • Branding and content has been sanitized for confidentiality

Goal

Our goal was to understand whether the platform served more of a communal space for power users and more of a browsing tool for non-experts.

TWO RESEARCH INSIGHTS TO DRIVE DESIGN

The research yielded two main insights, which guided the design process.

 

Users are unsure whether data is “the right one” which causes distrust between users and the platform.

Users reported that they were often unsure of how updated a data set was, which was crucial for their reporting. They became uncertain about whether data sets were the “right ones,” which created distrust in the work they were doing in the platform.

To address this issue, we thought about how we might create more trust between users and the data, to increase the accuracy of the users' work and increase platform usage.

Priority criteria was based on implementation effort, confidence in estimates of other criteria, and impact on users

We explored the idea of a "Hub," which organized data into workspaces. This was based off user suggestions.

In order to increase trust and accuracy, the new Hub would include:

  1. Updated data sets
  2. Necessary data details
  3. Approved data by leadership

The corporate-approved workspace section and map previews generate more trust between users and the data.

Insight 1 Hub Solution

Users spend much of their time repeating the same actions to create workspaces for their analyses.

Users typically create workspaces with the same kind of data for different requests. Looking for the right data or workspace each time is redundant and time-consuming.

This led us to think about how we might streamline the process of creating a workspace to reduce time on task and repetitive actions.

Priority criteria was based on implementation effort, confidence in estimates of other criteria, and impact on users.

OUR FOCUS

Users rely heavily on the search and query functions to quickly find the right data.

Since users search for the same kind of data sets when creating a workspace, we focused on how we could improve the data set search experience.

HOW MIGHT WE?

The current search experience lacked the information and previews for users to quickly find what they need.

Original Search UI

SOLUTION

We updated the data search experience to include a description, the ability to preview, and tags to improve filtering.

Updated Search UI

Search Results Modal

SOLUTION CONT.

We didn’t just update the search experience; we curated a section of the hub to highlight frequently used data so that users can quickly pull them for their own reports.

User Quote

Community Workspaces

A UNIFIED SOLUTION

Our final solution included a new Hub for workspaces and a re-designed Search experience for datasets.

User Pain Point

Our Solution

The new Hub gives users a centralized place to find corporate-approved and community workspaces.

The updated search experience grants users more detail about the data sets they’re looking for to improve efficiency and speed.

We created new design artifacts and outlined the specs for our development team to ensure the designs were scalable and replicable.

New search components

New Hub components

CONCLUSIONS

Based on conversations with users in the exploration phase, we are confident that the changes we made improved users’ experience with the platform.

Because of client priorities, our team was not able to measure the impact of the design changes made. If I had access to client data, I would measure the impact of the following with my product manager.

Customer satisfaction survey

Taking a baseline pulse before and after launching the changes would allow us to measure the impact of our designs. The feedback collected could directly inform iterations of the design.

Rate of export

An increase in rate of export could point to the level of trust users have in the platform. If users trust the platform, they may increase the level of export of their data sets.

Rate of data set upload

A decrease in rate of data set uploads would point to users feeling confident enough in the data already in the platform. Of course, even up-to-date data today would need to be replaced eventually, so we don’t expect data set uploads to end altogether.

Linkedin