Project Overview
Kontxt
Kontxt is an AI platform that helps defeat mobile messaging fraud and improves customer engagement, designed for RealNetworks.
Dashboard
Problem Definition
How to stop spam messages?
According to statistics, the average person receives around 50 text messages per day on their phone, but not every notification is something they actually want.
Spam, fraud messages, and smishing have become very common and a real problem. These unwanted messages take the form of a simple message, a link to a number to call or text, a link to a website for more information, or a link to a website to download an application.
Meanwhile, we often don’t get notified in situations where we are in danger or we anticipate something.
So how do we approach this complex problem and improve the overall messaging experience?
Process
Double Diamond
The Double Diamond framework (Discover → Define → Develop → Deliver) helped uncover distinct user types, their needs, and the challenges they face when interacting with messages.
Discover
• Personas
• Journey map
• Competitive analysis
• Card sorting
Define
• HMW questions
Develop
• Wireframes
• User flows
• High-fidelity design
• High-fidelity prototype
Deliver
• Usability testing
• Final design
• Design handoff
Discover
Persona
We identified two types of users for this product: customers (mobile network operators, aggregators, businesses) and subscribers (message recipients).
This phase focused on subscribers, so I created a persona representing their pain points, needs, and goals.
Journey map
Building upon the persona depicted earlier, I created a journey map that shows how subscribers behave when getting wanted and unwanted text messages on their phones, in order to brainstorm potential solution and inform design decisions.
Competitive analysis
Competitors with machine learning capability do have most features considered for this project but most lack a comprehensive yet simple solution (a web app) that is crucial in order to provide the best user experience.
Card sorting
Before diving into ideation phase, the team and I wanted to uncover customers' mental models of how they organize and categorize information on a platform that would analyze and classify text messages. Therefore, we conducted virtual card sorting session with participants, and this was the result.
Define
How might we
I created how might we questions to align on both the customer’s goals and the subscribers’ needs, guiding design decisions that caters to all types of users.
How might we improve the messaging experience by stopping fraud and spam content for subscribers?
How might we provide a platform that would analyze and classify messages while allowing customers (mobile networks operators, aggregators, businesses) to access all critical information?
How might we enable customers to strengthen loyalty and grow revenue?
Develop
Wireframes
Analyzing the card-sorting data early in the process helped inform my wireframing phase, where I explored information architecture solutions that aligned with customers’ expectations.
Pictured below are explorations of the Dashboard page.
User flow
After aligning on the proposal with the team, I created a user flow that helped us define the functionality and the information architecture.
Deliver
Usability testing
Later on, a prototype of the potential solution was created for testing purposes. Findings from in-person testing showed that participants (customers) successfully completed tasks, such as monitoring messages and understanding analytics.
Final design
A comprehensive web app with a clean and easy to use interface. White space throughout the app is used to balance design elements and convey grouping.
Message types are presented in various colors to indicate a message type, for instance, green is used for authentication while red for emergency. Having strong and contrasting colors help in better readability, sense of hierarchy and space.
The screens below show how the web app works when the user is a mobile network operator.
Design system
I’ve designed a scalable design system that covers all use cases that have been indentified for this project. Pictured below are some of the responsive web components, based on the atomic design methodology.
Analytics
Messages
Support Requests
Classification
Notifications
Solution
Platform for preventing spam and fraud messages
An AI platform that helps improve mobile content deliverability and detects spam/fraud over SMS, voice and IP channels. It helps mobile network operators, aggregators, businesses deliver a better messaging experience for their subscribers.
Using machine learning, the technology can analyze and classify most message types: Two Factor Authentication, Customer Support, Promotion, Emergency Alert, Notification, Grey Route, Fraud, Spam.
The end result is a spam-free messaging experience for subscribers and increased trust in mobile network operators and brands, delivered through a quote-based enterprise solution tailored to each client’s infrastructure.
Impact
KPIs
Analytics indicate strong adoption and engagement among customers. Reports also show a significant reduction in spam and fraudulent messages received by subscribers.
82%
spam and fraud reduction rate
81%
adoption rate
92%
engagement rate
How we measured
Spam and fraud reduction rate (82%) was calculated by comparing the number of verified spam and fraud messages recorded in the customer’s network logs before and after the launch of the Kontxt filtering system. Kontxt’s analytics provided the message-level data used to identify and validate threats over time.
Adoption rate (81%) was calculated by the proportion of users who interacted with the app out of the total user base, since launch.
Engagement rate (92%) was calculated based on users who performed meaningful actions in the app, such as monitoring messages and interacting with analytics, per month.
Project Details
Project details
Role: Product Design, User Research
Company: RealNetworks
Team: Product Designer, Product Manager, Engineering
Frameworks: Double Diamond, Scrum
Tools:
Design: Figma, Miro
Project management: Jira, Confluence
Analytics: Google Analytics
Year: 2018
Platform: Web
Link: Kontxt.com