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<\/figure>\n\n\n\nCompanies can learn a lot from Spotify\u2019s handling of app user habits, not least its exemplary compilation and presentation of their vast amount of data into visually striking, shareworthy content that has also been harnessed to create multiple user opportunities, including artist engagement, bespoke merchandise and recommended events. Crucially, 2023 is also the first year that Spotify Wrapped can be navigated using a screen reader – meaning that blind and partially-sighted people can also take part, making the app as awesome as it is accessible<\/a>.<\/p>\n\n\n\n In a new dawn of interactivity and intelligence, web app development is traversing a transformative shift into the realm of AI and ML to revolutionise user experiences like never before. And, while concerns about data privacy persist, consumers are increasingly willing to part with their precious data if they\u2019re to be rewarded with highly tangible and valuable brand experiences – particularly when they have control over their privacy settings.<\/p>\n\n\n\n The distaste for personalised ads and creepy omniscient recommendations is a far cry from the hyper-tailored brand experiences we\u2019ve come to expect. <\/p>\n\n\n\n The entertainment industry has trailblazed in popularising personalised content. Netflix’s recommendation system is a prime example. Its algorithms analyse viewing habits (yep, they know you\u2019re a binger), preferences (and all about your morbid fascination with serial killers), and interactions to provide personalised suggestions in the \u2018Recommended for you\u2019 section, which has a significant influence over what its users watch, while driving engagement and retention. YouTube’s recommendation algorithm follows a similar format, suggesting videos based on watch history, likes, and interactions. This influences user behaviour by keeping people engaged for longer periods, leading to increased viewing time and ad revenue.<\/p>\n\n\n\n In hospitality, Airbnb\u2019s user experience has consistently out-done itself. When it comes to recommendations, the app suggests personalised accommodation options based on past searches, preferences, and previous bookings. This tailored and creative approach influences booking decisions, encouraging users to plan stays aligned with their interests and past experiences, as well as experiment beyond the typical booking parameters (a Pembrokeshire weekend in a UFO \u2018futuro styled flying saucer\u2019 is the trip you didn\u2019t know you needed).<\/p>\n\n\n\n Over in retail, Sephora’s use of AI-powered personalised beauty recommendations helps customers find products suited to their preferences and skin types. By analysing customer data and providing tailored product suggestions, Sephora drives sales and enhances user satisfaction.<\/p>\n\n\n\n In the financial world, the Albert mobile banking app uses AI, ML and data analysis to analyse and forecast users\u2019 financial situations – including income, expenses, and spending patterns – to provide personalised insights and recommendations such as optimised spending suggestions, identified areas for saving, or bespoke investment advice.<\/p>\n\n\n\n \u201cFrom the customer experience perspective, AI and ML are playing pivotal roles in personalising user experiences. AI algorithms can analyse spending patterns and preferences, leading to tailored rewards and offers. Imagine a credit card that not only rewards you for purchases but also suggests where you might want to use your points based on your past behaviour. It’s like having a personal financial advisor embedded in your card.\u201d<\/p>\nSarat Pediredla, hedgehog lab CEO and Co-Founder<\/em><\/cite><\/blockquote>\n\n\n\n Over in the health industry, Bupa\u2019s AI-powered mental health service that\u2019s offered through its Blua Health app allows users to:<\/p>\n\n\n\n Data points such as a daily questionnaire plus exercise and sleep data, are gathered through wearable devices to analyse the user\u2019s mental health. It uses AI to provide the customer with personalised content for managing their mental health and even offers therapist appointments if the customer’s mental health is consistently poor. This service has been proven to detect early signs of mental health conditions – a crucial and highly valuable tool given that, according to mental health charity Mind, 1 in 4 people will experience a mental health problem of some kind each year in England<\/a>.<\/p>\n\n\n\n What each of these brands has in common is the ability to leverage AI, data, and machine learning to create truly delightful and meaningful user experiences. By analysing user data, preferences and behaviour, these platforms consistently deliver highly personalised recommendations and profound interactions.<\/p>\n\n\n\n There’s been a noticeable shift in consumer mentality and perception when it comes to data sharing and personalised content. Initially (and understandably), there was significant security scepticism and privacy concerns surrounding the sharing of personal data. But, over time, several factors have influenced this change:<\/p>\n\n\n\n The web and mobile apps of the future are so much more than the (digitally) dust-gathering platforms that used to sit on our phones, sporadically used, sapping valuable storage space. Apps have become intelligent beyond belief, intelligently and intuitively integrating with our day-to-day lives and making a significant difference to the way we approach purchasing, learning, health, wellbeing and socialising. <\/p>\n\n\n\n As consumer expectations soar to new heights of personalisation, the future of these apps hinges on their ability to harness AI and ML to decode user behaviours, predict pain points, deliver value, and curate interactive user experiences that are not just adaptive, but perceptive to the subtlest human needs and nuances.<\/p>\n\n\n\n However, as inspiring a case study as Spotify is, it goes without saying that it\u2019s no ordinary enterprise. To cope with an excess of 1 trillion events being captured every day, the Spotify Engineering team is responsible for 2,900+ deployments per day, 34,000+ data pipelines, 3,000+ engineers and 3,200+ microservices in production<\/a>. <\/p>\n\n\n\n So, we can comfortably conclude that not only is their data engineering team much bigger than most businesses, but their IT budget is also significantly larger. That means Spotify has the resources to acquire, deploy, and customise its data engineering for a large number of transformative solutions tailored to its vast infrastructure. <\/p>\n\n\n\n For most businesses looking to achieve personalised, interactive mobile and web app experiences by integrating machine learning into its processes, an achievable solution would involve the following steps:<\/p>\n\n\n\n Define objectives and use cases.<\/strong><\/p>\n\n\n\n Data collection and preparation.<\/strong><\/p>\n\n\n\n Choose appropriate machine learning models.<\/strong><\/p>\n\n\n\n Feature engineering for personalisation.<\/strong><\/p>\n\n\n\n Model development and testing.<\/strong><\/p>\n\n\n\n Integration into web and mobile apps.<\/strong><\/p>\n\n\n\n Continuous monitoring and improvement.<\/strong><\/p>\n\n\n\n User engagement and communication.<\/strong><\/p>\n\n\n\n Cross-functional collaboration.<\/strong><\/p>\n\n\n\n <\/p>\n\n\n\n If you\u2019d like to discuss how to boost your web or mobile app’s interactivity and personalisation capabilities, get in touch<\/a> or explore our UX\/UI design services<\/a> and AI packages<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":" How AI is shaping app interactivity. As the dust settles from the annual soul-bearing digital event we\u2019ve come to love and fear in equal measures, Spotify Wrapped is a beautiful example of a web and mobile app that\u2019s leveraging data analytics, machine learning (ML), and artificial intelligence (AI) to create personalised, interactive user experiences. Whether […]<\/p>\n","protected":false},"author":2,"featured_media":12189,"comment_status":"open","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[61,153,794,790,143],"tags":[766,793,792,767,791],"acf":[],"yoast_head":"\nThe rise of personalised app experiences.<\/h2>\n\n\n\n
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Shifting consumer perceptions in ux\/ui design.<\/h2>\n\n\n\n
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<\/figure>\n\n\n\nMachine learning strategies for web and mobile apps.<\/h2>\n\n\n\n
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