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The math is simple: Artificial Intelligence + User Experience = Better Findability.

Posted originally by Tanya S

It seems like we’re on the cusp of doing amazing things with chatbots and data mining, which can augment manual information architecture work and result in a better user experience overall. Automated information processing can help us identify search patterns and recommend information structures, to improve the findability of content.

How AI + UX = Better Findability

Let’s say you have a service that includes a search component. Right now, your users might be running searches and using manual filters to sort through the results, as search users are wont to do.

How do you know if they are finding what they need? You can rely on analytics to see if they are accessing what you want them to, and user research to find out if they consider themselves successful in completing their tasks. You can gauge overall user satisfaction by analyzing usage data, time on service, and ask them outright in interviews and feedback forms.

Let’s say some users have trouble searching or aren’t going where you expect from the search results. Maybe their overall experience is ok, but looking at the search terms and clicks through the search results, you can tell that there could be better results for their searches or clearer paths to the information related to their tasks.

How can you help them search better? Maybe if they used slightly more narrow terms or more comprehensive phrases, they might be able to find more relevant information to their queries. But these are theories.

You set to redesign the service, including the search, using a user-centered design approach. Awesome! And let’s throw in a little AI while we’re at it.

While you embark on your UX design process, you can use an AI system to analyze large amounts of seemingly unrelated data to help inform your design decisions. For example, you can set up your data mining tools to start collecting structured and unstructured data (analytics, search queries, and other usage data). As you identify which problem(s) you’re trying to solve for your users, you hook up an AI (like IBM Watson) to start analyzing the unstructured data.

AI Training

But how does the AI system know what to do? This is the fun part: First, it parses the data at face value and then you have to train it. AI systems can analyze large amounts of data in much less time than could be done manually and can learn in real-time. They understand context so you can help them learn what the data represents by feeding them additional information in the form of business rules, metadata and questions.

As you work through the user experience research and design phases, you continually refine the questions you ask, and it will alter the data facets it analyzes. You can ask it plain language questions like: How many people search for X? How many times does Y get served as a response? What kind of information do we have about Z? The system responds to the questions as best it can, based on its analysis of the data. The beautiful part though is that you are not limited by your ability to ask questions. The system takes your questions and the data, and, actually learns. It starts to ask its own questions. Over time, as more queries are made in the search engine, and more user analytics are collected, it can better make connections, identify trends, suggest hypotheses, and generate richer findings.

How does this help users search? If your users rely on search to find information, you can augment the quality of the search results with this data. Think better predictive search terms, more relevant search results and Amazon-like cross-topic referrals. These have the potential to make for a richer user experience, as the content your users need is served directly to them by an engine that learns from everyone who came before.

AI for IA

And how can it help design better information architectures? One of the hardest parts of information architecture is creating appropriate content groupings with labels that are meaningful to users. Artificial intelligence can help discover and propose relationships between content, by analyzing content-related data for trends: from the meaning of the words themselves to how users navigate within it or search for it to how they move through the site or app or service, and beyond. AI is capable of highlighting trends that us mere humans might not see on their own, which can become information facets or new content use cases.

What if you could combine user research with large-scale data analysis performed by your AI system to better identify relationships between content types, and improve content groupings and cross-linking? To group content and label it in a more meaningful way for your users, to offer the right related links at the right time, and to generally make your site, service or product feel more intuitive. And what if it could analyze internal and external data to help you determine how best to build both internal information structures for content managers (e.g. for your content management system) and navigation structures for end users (e.g. the menu for your site or app)?

We still need humans…

Continue reading the full article at http://uxmag.com/ai-ux-and-the-future-of-findability

Source: Artificial Intelligence, UX and the Future of Findability | UX Magazine

Rence Winetrout

Rence Winetrout

Chief Digital Officer at AAXIS
Rence Winetrout brings nearly 20 years of experience leading Multi-Channel Commerce, Marketing, and e-Business initiatives for premium, global consumer brands in a wide variety of industries, including luxury goods, apparel, footwear, accessories, sporting goods, consumer electronics, and digital goods. Rence is the CMO and SVP of Commerce Strategy for AAXIS Commerce.
Rence Winetrout

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