Artificial Intelligence (AI) is no longer a vision for the future – it is already here and is having a significant impact on how businesses can improve their practices. One big way that AI is helping businesses is in user experience and design.
With AI, businesses can seamlessly adapt their website design to comply with changing trends and behaviours, increasing usability and improving the overall user experience. Users are demanding more from their online interactions as competition for their attention becomes stiffer, and your website needs to accommodate this desire.
AI can help businesses give the user what they want by focusing in on two significant trends in website and landing page design: images and personalisation.
AI And Images
Images are essential to website design as they can help captivate the user’s attention. AI can help make data-based decisions about which visuals will work best for specific users to improve their overall experience.
With AI technology, you no longer have to categorise images based on the image name. AI can detect pattern structures within an image and recommend related images that contain similar pattern structures. This offers a new level of optimisation based on user behaviour.
Customise landing pages by offering tailored image suggestions of products and services that match a user’s previous purchases or product page interactions. These related images can be given priority to populate the page, improving the user experience by showing them items that are likely to be of interest to them.
This AI feature will only become more prominent in the future, as websites will have the power to filter through incredibly large data sets and recognise user patterns on individual and holistic levels. Individuals who meet specific pattern criteria can then be categorised into groups whose website experience will include select images pertaining to their category.
A great application of this technology can work in tandem with the a site like the Food Network website, as users can be recommended recipes from images that correspond to those images. This process can be optimised, all based on previous searches as well. For instance, if the user searches for gluten-free recipes, AI algorithms can then suggest images of gluten-free food on the website in tandem with recipe recommendations.
While this example deals with a specific niche, the technology is more far-reaching. AI can actively aggregate individuals (based on their particular user patterns and characteristics) into larger, similar groups and optimise website image recommendations to them.
Visual Sentiment Analysis
Visual sentiment analysis is a new way to classify and study our emotional responses to visual stimuli, such as video and images. It does this by trying to understand and decipher the dense, high-level content of visual data.
In creating computer vision algorithms to study this high-level content, models try to determine a classification of whole videos and images, as well as local regions within the visual stimuli. In essence, this technology identifies signals in images that help to convey the overall sentiment of the image.
AI is evolving to understand which images convey particular visual sentiments, so it can then categorise the images based upon their classifications. These images can then be used to match with users who best correspond to seeing these types of images.
In the future, websites can give the best personalised visual experience to its users by matching image categories to specific user interactions and then populating the page with those tailored images.
Adobe is creating a design-focused AI assistant called Sensei. This assistant gives design recommendations regarding the best layouts, colours, images, and image sizes for website pages. It offers suggestions based on machine-learning that are specific to your website category or function.
The AI will also automate specific processes and small tasks, such as image recognition techniques or small photo editing details like cropping. Automation is not just relegated to design considerations; meta-data and photos in its client database (CMS) can all be optimised through automated recommendations, with the assistant even suggesting elements to include.
Sensei, which is being billed as “human-augmented design“, will not re-imagine an entire interface and make complicated UI or UX decisions. However, it can help quickly determine which photos and text content belong on what pages, depending on different user segments.
The platform also allows enterprises to generate customised content and helps to battle creative bottlenecks like content variation and image selection, all with the intention of providing the best user experience.
AI And Personalisation
Consumers are more inundated with information than ever before. If you want users to engage with your website or purchase your products or services, you need to be able to provide them with a personalised web design that anticipates their needs and caters to their tastes.
As the name suggests, AI predicts future actions by building customer behaviour analytics based on data collected from user page visits, item selections, and website interaction behaviour. Essentially, this AI uses past interactions to recommend things like preferred destinations, merchandise, and more.
AI can also predict future purchase patterns discerned from previous ones. This can help businesses provide the easiest paths to purchases as they anticipate the needs of the user. The more accurate the predictions get, the more insights businesses will be able to draw from them, and the better the user experience they will be able to offer.
In the future, predictions can help customise web design to suit the user. It can offer predictions on colour, image container sizes, creative web page asset shapes, font preferences, and more to help better tailor your website to each visitor type.
Search bars are a great addition to any website as they provide more relevant results than standard navigation can. Search bar users are looking for information or shopping with intent – they are not on your site to browse; they actually know what they want.
Having a user-friendly search experience produces happier shoppers or visitors and creates more significant potential for returning customers. Semantic search improves search accuracy, and therefore increases user satisfaction, through a deeper understanding of user intent and context.
Many big businesses see the value of investing in website search technology. For example, Etsy invested in Blackbird Technologies, a machine-learning company specialising in user behaviour analytics for search recommendations. Google is also leading by example through its interactive query completion template, which answers questions at scale in real time. Both these technologies help optimise user search intent for a more natural and accurate user experience.
These technologies employ natural language processing and machine learning to analyse large volumes of consumer opinions and reviews. AI can take insights from these comments and help retailers identify and position the best products to satisfy customer search intent over search description.
Content and Layout
In the future, AI will ideally expand to completely individually curated websites. Instead of focusing on recommendations and suggestions, AI would be able to present a user with content fitting to their interests directly upon arriving at a website.
Machine learning can already program a computer or application to learn data that it can then use to customise the user experience. As a result, users spend less time searching for what they are looking for and more time engaged in what interests them. For instance, with AI, when you arrive on a landing page, it would already be showing articles or blog posts that cater to your interests, specific to your industry or business.
This also applies to website layout. Adaptive user interfaces use machine learning to detect the device, operating system, and platform that a person is using to browse that website, and will adapt accordingly to give the best experiences on those platforms.
This can also apply to specific sections of a website, which are restructured to provide the best user experience. A great example of a company using this technology is Netflix with their “because you watched” suggestion feature. Because the suggestion model shows a clear indicator for the recommendation to its audience (because you watched “X” program, we recommend watching “Y” program), it successfully works to bring in more viewers to those suggested shows.
Netflix understands that not only personalising a piece of content, but also presenting it accurately to a specific user, can affect their attention. This is why they have multiple images for their show title covers — some covers might work better with certain users than others. Using adaptive user interfaces, Netflix also changes the layout of the homepage for each user, session, and device they use to attract more views.
By giving people more control over the outputs of machine-learning algorithms, they can select, sort, and re-organise those outputs in ways they can understand, or that are more natural to them. This gives your algorithm more accurate information to use when changing layouts, providing recommendations, and personalising content for better user experience.
Through analysing sentiment behind images and matching it to user interest, predicting user paths, and rearranging layout and content in adaptive user interfaces, AI is significantly improving the user experience.
(Lead image: Depositphotos)