Interos Risk Scorecard

Interos is an AI B2B tool that monitors risk within the customer’s supply chain. The company profile provides a high-level overview of a company, as well as a deep dive into its risk data. The value of the product is dependent on the value of the data it provides.

I was the lead designer 

  • Worked daily with PM

  • Spoke regularly with engineers on feasibility, implementation support, design QA

  • Worked with UX researcher to validate concepts and usability test

  • Interfaced with data scientists providing risk data

  • Synced with two other designers for feedback 

My Role & the Team

Overview

  • Risk analysts, procurement managers, and supply chain analysts need to easily navigate the company and risk profile, as well as clearly understand the data presented, in order to assess the risk of companies either within their supply chain or that they’re vetting for new business

Problem Statement

  • Provide a high-level overview of a company’s risk

  • Enable user to drill down and digest four tiers of data

  • Show rich granular data with actionable insights

  • Position Interos as a trusted leader in the supply chain industry  

Product Goals

  • Risk managers & analysts

  • Procurement managers

  • Supply chain analysts

  • Third-party consultants

  • Compliance officers

Users

  • Customers satisfaction; they can easily navigate, understand and trust the data

  • Company wins new business deals by providing a seamless and valuable data-rich workflow 

Success Metrics

Use Cases

Data Hierarchy

Vetting New Supplier

Monitoring Risk

Legacy Platform

  • No design culture; interface had never been updated

  • Legacy features and language weren’t clear to the users; transparency is key to earn customer’s trust

  • Promised customers richer data to produce actionable insights

  • Needed a more useable and scalable UI to display more data and acquire new business

Concept Ideation & Validation

  • Drilldown through three tiers of data

  • Recognizable stock ticker pattern

  • Validated through testing

  • I pushed for breaking out the design into deeper pages for scalbility

  • Confusing IA and visual heirarchy; having everything on one page was crowded and hard to digest

  • The team wanted the scores to be omnipresent but it made for duplicative/redundant info

  • Secondary nav with both Overview and Risk factories was confusing & redundant

  • Design not scalable for future content

  • Working UI was not standard dark design practice (i.e. darker foreground, lighter background)

First Iteration

Ideation

Second Iteration & User Testing

Strengths

  • Updated dark design patterns

  • Broken out into deeper level to offer context and clear presentation

  • Modified the secondary nav pattern to include the risk scores

  • Customers were easily able to navigate through and understand the data

Issues

  • Expanding still table not scalable enough; too many attributes

  • Data & Eng team informed us they would not be able to show accurate time stamps

  • Customers wanted more context on scoring breakdown

  • Customers expressed concerns over data accuracy and sourcing

Final Iteration
& Prototype

  • Deeper level of table reveals more information on attributes and is more scalable

  • Sticky header to reproduce company context and main CTAs

  • Easy lateral navigation between risk factors with risk scores in context

  • Easy navigation through the tiers of data

  • Information stays within Risk Profile Context

  • More space for the platform to scale and include more data

Looking Back

Impact

A post-launch survey was sent to customers to measure perceived usability & usefulness. The MVP showed an increase of 10% on the System Usability Scale.

What would I do differently?

  • Push harder on defining the data content in the early stages

  • Done discovery research with customers; collected data to inform the initial designs 

Moving Forward

  • Work towards getting accurate time stamps

  • Add back in trending arrows and graph

  • Scale to include more risk metrics

  • Pull in news stories about events that affect the company risk data

  • Conduct research and design a mobile companion app post-launch survey was sent to customers to measure perceived usability & usefulness.