Today’s consumers are overwhelmed by a constant deluge of advertisements and promotions. They are tired of the noise and fatigued by all the product options. They increasingly desire the personalized attention they get from Amazon, Netflix and other brands they interact with. We are living in an experience economy where mere goods and services are … Read more
From the article,”To Understand Consumer Data, Think Like an Anthropologist” in From Data to Action: a Harvard Business Review Insight Center Report, Susan Fournier and Bob Rietveld share: “Corporate social-listening efforts are typically driven by econometricians, computer scientists, and IT technicians—the people who are experts in database management. They understand digital information, but they don’t always … Read more
I find myself going back to Fabien Girardin’s excellent article, Experience Design in the Machine Learning Era, and mining it for more gold. Fabien shares: “Nowadays, the design of many digital services does not only rely on data manipulation and information design but also on systems that learn from their users. If you would open the … Read more
According to Dr. BJ Fogg, founder of the Persuasive Tech Lab at Stanford University and the Fogg Behavioral Model, persuasive technology uses seven strategies to influence behavior: Reduction – Simplify the task the user is trying to do. Tunneling – A step-by-step sequence of activities that guides the user through the behavior. Tailoring – Provide feedback … Read more
In the Harvard Business Review article, AI Won’t Change Companies Without Great UX, Michael Schrage asked the question, “As artificial intelligence algorithms infiltrate the enterprise, organizational learning matters as much as machine learning. How should smart management teams maximize the economic value of smarter systems?”: “Strategically speaking, a brilliant data-driven algorithm typically matters less than … Read more
In the O’Reilly article, Design thinking and data science: Solving problems with data necessitates a diversity of thought, Dean Malmgren from Datascope and Jon Wettersten from IDEO shares: “Problem solving not only requires a high-level conceptual understanding of the challenge, but also a deep understanding of the nuances of a challenge… Solving problems with data necessitates a … Read more
Finding the right sample size is a tradeoff between the number of participants in the study and the ability to detect problems. The larger the sample size, the more problems that get uncovered. There is however a diminishing return as fewer new problems get uncovered with each additional user. And not all problems uniformly affect … Read more