UI/UX Articles and Interesting Tidbits of the Week
July//26//2024
Here are some interesting finds on UI/UX of the week!
1.
Why Creating Object Maps is a Good Idea. Very insightful article from the Adobe Design Blog and author Alan Wilson, on the topic of Product Designers creating Object Maps to more effectively render product solutions that are thoroughly encompassing the entirety of a user’s expectations/tasks/desires. The author details what Objects are, alongside Attributes, Relationships, Actions, all with the intent to ultimately produce an illustration of the output that represents a fulfilling product experience. Highlight of the article includes:
“Reducing the distance between how a product functions and how people work creates better user experiences, period. It’s a daunting task, but the best way to start is by understanding the objects that users create, edit, and use within a product. It’s a bit abstract, but by creating a map of these objects you can help your team define new products or features, identify problems in an existing product, communicate complicated information, and aid in the decision-making process. This process is called object-oriented design (OOUX).”
2.
Demonstrating the Impact and ROI of Research. Another interesting article from People Nerds, which documents the conversation between Ms. Julie Norvaisas, VP of UX at dScout and Ms. Elysa Stein, Head of Financial Services UX Research at Cash App. The article is very much in tune with the one I highlighted the prior week, of how to reinforce the value and impact of Research, and its return on investment. Ms. Stein elaborates on how Cash App’s structure aggregates insights from Analytics, Voice of the Customer and other departments under a single umbrella, in order to make that data actionable into palpable strategies when it comes to product refinements. Worth reading through this great conversation. Highlight of the article includes:
“It’s a balance, but, I don’t think so. As long as we maintain the quality of our work, go deeper into synthesis and prioritize the right questions for research — the ones that really matter to the business. Of course it depends on the project. A project that is designed specifically for an outcome like driving engagement with a target audience will have more focus on rich stories, giving stakeholders an opportunity to build empathy and understanding. A project intended to uncover why metrics are dropping in an onboarding funnel may more directly answer the reasons why, backed up by customer behavior examples as proof points. Either way, in the findings deck, document or whatever format you choose, keeping the customer stories concise, engaging, and targeted so you don’t lose your audience’s attention is still crucial.”
3.
The Differences Between Conversational and Generative AI. Great article from Qualtrics and authors James Skay and Rosemin Anderson, who succinctly and substantially explain the differences between Conversational AI, its reliance on Natural Language Processing, and Generative AI, which actually produces outputs as if created by humans themselves. It’s an article that also looks at how these two resources can be leveraged in CX, including constant engagement and availabilit, enhanced personalization and customization, to name but a few. Well worth reading through. Highlight of the article includes:
“Conversational AI utilizes advanced algorithms and machine learning to respond to human inputs in a natural way. Rather than using pre-written scripts and following specific rules, the technology is able to deduce information from natural conversation prompts. To do this, conversational AI uses Natural Language Processing (NLP) to identify components of language and “understand” the meaning of the word and syntax. It can recognize grammar, spot spelling errors and pinpoint sentiment as a result. Once the conversational AI tool has “understood” the text, deep learning and machine learning models are used to enable Natural Language Understanding (NLU). This identifies the request or topic, and triggers actions as a result, such as pulling account information, adding context or responding. It can also store information on user intents that were noted during the conversation, but not acted upon (dialog management).”
