Design & Messaging Featured Process Design

How UI/UX Investments Turn Into ROI

For most businesses, having the right answer to the client’s problems is the key to success. The most successful companies in the world today are using design to lead clients into finding their firm as, not just the right, but the easy answer to their concerns—we’ll get to this in a second. Despite the success of these companies, a lot of executives are still reluctant to invest in good UI/UX design to improve their performance and rev-up profitability, and their reluctance is understandable.

At the surface, investing in improved user experience may seem unnecessary. You may think that you can put that amount into other aspects of your business, like marketing or research. While those departments still deserve your attention, a fair amount of understanding and evidence may convince you and your stockholders to invest in improving the overall experience of your customers whenever they interact with your product or brand digitally.

The End-Goals of UI/UX Design

Whether it’s about building up your brand’s user experience from scratch or redesigning your platform, UI/UX design primarily has four end-goals.

  1. Usability or ease of navigation and the platform’s discoverability
  2. Consistency within interactions and design
  3. Workflow or the efficiency of user flow and interaction design
  4. Brand Perception or the amount of confidence, trust, and the perception of quality users have with your brand.

Whenever your clients are interacting with your brand online through your company website or your app, improving Usability means organizing your information architecture in a way that makes sense to users.

Improvements in usability results in fewer customer support costs. Your company’s ROI will come in the form of the amount of money saved over time. Good UI/UX designers could conduct A/B tests to determine how your customers think whenever they’re interacting with your brand.

A good example would be the Bank of America’s redesign project. They decided to improve their online enrollment application for online banking. The end-goal of their UI/UX team is to use yield (or the number of customers completing the online enrollment process) as the primary metric.

They tested various design solutions and eventually ended up with a successful strategy. When they launched the new registration form, the yield nearly doubled. It became easier for customers to sign-up, and the company ended up with more clients.

Consistency across company divisions helped General Electric Co. save an estimated $30 million after investing in a common software platform for the company. They built processes and tools that helped support the company’s UX practices.

They made sure that their software user experience matched their reputation for hardware engineering. Today, GE is quietly the 14th largest software developer by revenue.

Efficient design helped increase Workflow productivity for Cathay Pacific when they worked with a design consultancy to create Travel Desk, a one-stop online portal for staff travel.

Travel Desk helped reduce the call-center volume and made employee services more efficient. Plus, it helped increase productivity for the ground staff, reducing the time it requires to process listings and employee travel benefits.

Continental Office, a furniture company, increased its website traffic by 103% and its new contacts by 645% when they decided to refresh Brand Perception through engaging user experience.

Their UI/UX team integrated buyer personas with relevant content marketing to provide a more immersive user experience. This project started as a simple need to update their 6-year-old website. It ended up spiking their numbers in a positive way year-over-year.

You can cause the same trend for your company when you decide to invest in good user experiences. The ROI may come in the form of savings, renewed brand recognition, productivity increase, and so much more.

UI/UX is not just about creating designs that work. Inevitably, the result of investing in user experiences is to build your company’s credibility with your customers. When a brand cares about its users, it builds customer loyalty, which then drives word-of-mouth referrals.

For any questions or conversations please fill out the form below. 

Data, Automation & AI Featured

AI and the Classroom: Machine Learning in Education


For years schooling has been typified by its aspect of the physical grind on the part of both students and their teachers: teachers cull and prepare educational materials, manually grade students’ homework, and provide feedback to the students (and the students’ parents) on their learning progress. They may be burdened with an unmanageable number of students, or a wide gulf of varying student learning levels and capabilities in one classroom. Students, on the other hand, have generally been pushed through a “one-size-fits-all” gauntlet of learning, not personalized to their abilities, needs, or learning context. I’m always reminded by this quote by world-renowned education and creativity expert Sir Ken Robinson:

“Why is there this assumption that we should educate children simply according to how old they are? It’s almost as if the most important thing that children have in common is their date of manufacture.”

But as the contemporary classroom has become more and more digitized, we’ve seen recent advances in AI and machine learning that are closing in on being able to finally address historical “hand-wrought” challenges – by not only collecting and analyzing data that students generate (such as e-learning log files) when they interact with digital learning systems, but by pulling in large swaths of data from other areas including demographic data of students, educator demographic and performance data, admissions and registration info, human resources information, and so forth.

Quick Review: What is Machine Learning?

Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look Machine learning works especially well prediction and estimation when the following are true:

-The inputs are well understood. (You have a pretty good idea of what is important but not how to combine them.)
-The output is well understood. (You know what you are trying to model.)
-Experience is available. (You have plenty of examples to train the data.)

The crucible of machine learning consists of capturing and maintaining a rich set of data, and bringing about the serendipitous state of knowledge discovery: the process of parsing through the deluge of Big Data, identifying meaningful patterns within it, and transforming it into a structured knowledge base for future use. As long as the data flows, its application is endless, and we already see it everywhere, from Facebook algorithms to self-driving cars. Today, let’s examine machine learning and its implementation in the field of Education.

Application of Machine Learning in Education


A few years ago, Sotiris Kotsiantis, mathematics professor at the University of Patras, Greece presented a novel case study describing the emerging field of educational data mining, where he explored using students’ key demographic characteristic data and grading data in a small number of written assignments as the data set for a machine learning regression method that can be used to predict a student’s future performance.

In a similar vein, GovHack, Australia’s largest open government and open data hackathon included several projects in the education space, including a project that aims to develop a prediction model that can be used by educators, schools, and policy makers to predict the risk of a student to drop out of school.

Springboarding from these two examples, IBM’s Chalapathy Neti recently shared IBM’s vision of Smart Classrooms: cloud-based learning systems that can help teachers identify students who are most at risk of dropping out, why they are struggling, as well as provide insight into the interventions needed to overcome their learning challenges:

The system could also couple a student’s goals and interests with data on their learning styles so that teachers can determine what type of content to give the student, and the best way to present it. Imagine an eighth grader who dreams of working in finance but struggles with quadratic and linear equations. The teacher would use this cognitive system to find out the students learning style and develop a plan that addresses their knowledge gaps.

Process efficiency: Scheduling, grading, organization

Elsewhere, several Machine Learning for Education ICML (international machine learning conference) workshops have explored novel machine learning applications designed to benefit the education community, such as:

-Learning analytics that build statistical models of student knowledge to provide computerized and personalized feedback on learning the students’ progress and their instructors
-Content analytics that organize and optimize content items like assessments, textbook sections, lecture videos, etc.
-Scheduling algorithms that search for an optimal and adapted teaching policy that helps students learn more efficiently
-Grading systems that assess and score student responses to assessments and computer assignments at large scale, either automatically or via peer grading
-Cognitive psychology, where data mining is becoming a powerful tool to validate the theories developed in cognitive science and facilitate the development of new theories to improve the learning process and knowledge retention
-Active learning and experimental design, which adaptively select assessments and other learning resources for each student individually to enhance learning efficiency

Existing Platforms

Recently, digital education venture capitalist Tom Vander Ark shared 8 different areas where leading-edge platforms are already leveraging machine learning in education:

1. Content analytics that organize and optimize content modules:
a. Gooru , IBM Watson Content Analytics

2. Learning analytics that track student knowledge and recommend next steps:
a. Adaptive learning systems: DreamBox, ALEKS, Reasoning Mind, Knewton
b. Game-based learning: ST Math, Mangahigh

3. Dynamic scheduling matches students that need help with teachers that have time:
a. NewClassrooms uses learning analytics to schedule personalized math learning experiences.

Grading systems that assess and score student responses to assessments and computer assignments at large scale, either automatically or via peer grading:
a. Pearson’s WriteToLearn and Turnitin’s Lightside can score essays and detect plagiarism.

5. Process intelligence tools analyze large amounts of structured and unstructured data, visualize workflows and identifying new opportunities:
a. BrightBytes Clarity reviews research and best practices, creates evidence-based frameworks, and provides a strength gap analysis.
b. Enterprise Resource Planning (ERP) systems like Jenzabar and IBM SPSS helps HigherEd institutions predict enrollment, improve financial aid, boost retention, and enhancing campus security.

6. Matching teachers and schools:
a. MyEdMatch and TeacherMatch are eHarmony for schools.

7. Predictive analytics and data mining to learn from expertise to:
a. Map patterns of expert teachers
b. Improve learning, retention, and application.

8. Lots of back office stuff:
a. EDULOG does school bus scheduling
b. Evolution , DietMaster.


As the modern classroom becomes more and more digitized, we are able to gather myriad sets of data. The trick is, of course, being able to purpose it. The prize at heart of machine learning is knowledge discovery, the process of parsing through the deluge of Big Data, identifying meaningful patterns within it, and transforming it into a structured knowledge base for future use. In this article, we’ve seen examples utilizing machine learning in the education sector for prediction, scheduling, grading, and organization. We’ve also listed existing education-related platforms that use a machine learning component.

What does it mean to me?

Big Data have swept into every industry and business function and are now an important factor in production, alongside labor and capital. In a decision making system, the bigger the data, the higher the likelihood is of making good decisions. The time is now for organizations, in education or otherwise, to research how a cost-efficient machine learning component can transform your operational output. For more information, Check out this detailed guide by Jesse Miller on the amazing benefits of technology in the classroom and suggestions on ways to incorporate technology in the classroom.

“Parents are continually exposed to new technology via their children. Whether it be iPad App usage tricks, to the advent of robotics competitions, and perhaps now “new ways of thinking” as a result of interaction with Machine Learning based educational environments. Siloed educational content may give way to a topology of learning experiences.” O. Liam Wright – CEO, True Interaction

True Interaction produces custom full-stack end-to-end technology solutions across web, desktop and mobile, integrating multiple data sources to create a customized data solution. True Interaction can determine the most optimal means to achieve operational perfection, devising and implementing the right tech stack to fit the specific school and or district need. True Interaction pulls together disparate data sources, fuses together disconnected silos, and does exactly what it takes for school data systems to operate with high levels of efficiency and efficacy, ultimately leading to improved student achievement outcomes.