Data Science + Design Thinking: a Perfect Blend to Achieve the Best User Experience Data Science + Design Thinking: a Perfect Blend to Achieve the Best User Experience
  It’s one thing to rely on artificial intelligence, machine learning, and big data to make your product smarter.  And, quite another to build... Data Science + Design Thinking: a Perfect Blend to Achieve the Best User Experience

 

It’s one thing to rely on artificial intelligence, machine learning, and big data to make your product smarter.  And, quite another to build a product that’s so intuitive and easy-to-use that your customer falls in love with it.

That’s the beauty of data science + design thinking.

It’s something we think a lot about here at Intuit. That’s why I’m excited to share our “design for delight” methodology for developing AI/ML-driven software and services that help consumers, self-employed individuals, and small business owners make better financial decisions. This approach to creative problem-solving, developed at the Stanford University d.school, has been broadly adopted by world-class design firms like IDEO and many of the world’s leading brands like Apple, Google, Samsung, and GE.

During my talk at the ODSC East 2019 conference on April 30th, I’ll provide a firsthand look at how we apply three design thinking principles – grounded in deep customer empathy – to achieve the best user experience.

 

1. Develop deep customer empathy – To begin with, it’s essential to cultivate a keen understanding of the problem we’re trying to solve by diving deep into the customer experience.  This means observing and engaging with users to get an unfiltered view of their challenges and pain points when using our products in their own work environment. With deep customer empathy, design thinkers naturally begin to set aside their own biases and assumptions to define the problem we’re trying to solve in human-centric terms, rather than framing it in technology-centric or company-centric terms.

2. Go broad to go narrow – Only after gathering, analyzing, synthesizing and defining the customer problem, can we begin to look for solutions. This phase in the process is all about stimulating free thinking, expanding the problem space, and generating lots of ideas for exploration.

What are a few key customer pain points—a dozen or two—in need of a solution? Which of them can be solved effectively with artificial intelligence, and why? Will the resulting experience be simple, intuitive, and emotionally rewarding for the customer? If we can’t say yes with confidence, we move on to the next candidate, settling for nothing less than the best.

Data Science + Design Thinking: a perfect blend to achieve the best user experience

3. Conduct rapid experiments with customers – At this point, we begin rapid prototyping and A/B testing. By putting prototypes into the hands of actual customers, we can see whether people behave, react, and respond the way we expected, and find opportunities to do better with the next build. Rapid experiments keep our project rooted in the real world.

 

Intuit has spent many years applying lessons learned from blending data science and design thinking to deliver awesome experiences to customers of our portfolio of AI/ML-driven software and services: QuickBooks, TurboTax, and Mint. During my talk at the ODSC East 2019 conference this month, “Data Science + Design Thinking: a Perfect Blend to Achieve the Best User Experience,” I’ll delve deeper into our “design for delight” methodology and share real-world examples to illustrate its benefits.

Michael Radwin

Michael Radwin

Michael Radwin is a Vice President of Data Science at Intuit, responsible for leading a team dedicated to using artificial intelligence and machine learning models for security, anti-fraud and risk. Prior to Intuit, Radwin was VP Engineering of Anchor Intelligence, which used machine learning ensemble methods to fight online advertising fraud. He also served as Director of Engineering at Yahoo!, where he built ad-targeting and personalization algorithms with neural networks and naïve Bayesian classifiers, and scaled web platform technologies Apache and PHP. Radwin holds an ScB in Computer Science from Brown University.