Jul 23 - 5min readThe Changing Face of AI in Product DevelopmentBy Launchbase

Artificial Intelligence is continuously transforming the world we live in. AI in the various sectors like business is being used across various industries such as e-commerce, healthcare, finance and various others.

AI features include Natural Language Processing, Computer Vision and Machine Learning (ML). AI can be a game-changer for developing digital products. At Launchbase, we believe that the most interesting of these features that we can include in implementing in digital product development is what will make your product stand out from the crowd and bring your company forward into the future.

Customer Segmentation

Customer segmentation consists of dividing customers into groups based on mutual characteristics. Companies can then market to a specific targeted group and run personalised campaigns. AI-powered segmentation enables automatic updating of segments and scaling processes. It is thanks to AI algorithms, systems can analyse data without any presumptions, and is able to spot correlations that humans could overlook. Businesses can then find hidden patterns and narrow down customers based on the collected information only.

Customer segmentation is mostly used to send suitable emails, present personalised offers and run the most accurate ads. An example of a digital product that makes use of this is Play24. It is a mobile app that generates plans based on customer profiling, which uses information about users to suggest appropriate offers.

Recommendation of Products

AI-fuelled product recommendations can be used in all kinds of digital products, including streaming and ecommerce products. ML models correlate gathered information and make predictions based on it. A system can start recommending items once it has been trained on customer preferences and the offered products. Examples of this, like advertisements or within digital products, making it an effective method for upselling and promotion.

One of the most popular examples is Netflix, which suggests shows and movies based on what other users with similar interests have watched. At least 75% of videos watched are a result of recommendations. Thanks to these mechanisms, users become engaged with the content and often renew their subscriptions based on this.

Image Recognition

One of the most popular cases of computer vision is image recognition. This is the process in which an AI algorithm identifies an object in a digital image. This tech can enhance many features like visual search options as an example. Some online stores like BooHoo, allow customers to find their desired items faster thanks to its visual searches. Consumers can upload an image to receive similar products back as a result. Image recognition can be widely applied to digital products.

An example of this is the Flutter mobile app Planter which uses advanced object recognition to identify species of plants and then advise users on how to take care of them properly. Planter’s classification model is based on a neural network and is trained via transfer learning. Also, the classification is run solely on the user’s advice, which improves the product’s performance. This is how AI features identify objects based on photographs and, in this case, guide the user about required types of soil, fertiliser and watering instructions.

Text Chatbots and Voice Assistants

Bots can enhance the user experience in many ways. First off, AI-fueled assistants and text chatbots can help solve customers’ problems and answer their questions faster than human agents. Another possibility is to use bots for conversational commerce. Shopping assistants can ask for consumers’ preferences to recommend the most suitable products for them. Conversational commerce can also refer to chatbots in live chats or all kinds of messaging apps. Brands would increase engagement and trust by using chatbot personalities, which can be revealed in the avatar, bot’s name and a language style that expresses the brand’s voice.

Businesses can take advantage of voice assistants provided by Amazon, Google or Apple. Thanks to integration with Alexa, Siri and Google, users can interact with these digital products to get customer support, shop online, book flights, order food and other services.

For example, PZU which is an insurance group in the CEE region, provides an Insurance Assistant that supports the mobile purchase of travel policies. Customers can interact with a conversational interface to find tailored offers quickly thanks to Natural Language Understanding. Another great example of chatbots in digital products are Timesheets. This is a time-tracking solution which is integrated with Siri, Alexa, Google Assistant, Google Chat and Slack, to provide an excellent conversational experience. Users can log time on their tasks easier and faster, and therefore boost workflow.

Facial Detection

Facial recognition is an AI-based biometric feature that allows for the verification and identification of a person from a digital video or image by analysing unique features, such as facial shapes and textures. This tech can be applied to various digital products. Facial recognition becomes helpful when it is implemented in increasing security of digital products.

When it comes to facial detection, some of the most popular apps that take advantage of it are Instagram and Facebook. These social networks provide filters that help to engage community when publishing stories. Face detection and augmented reality (AR) enable users to add effects to their stories. Spark AR, which is software delivered by Facebook to creators, can identify three different expressions and track hand gestures.

Text Generation

AI-based text generators can replace human writers for creating articles, poems and other kinds of text. Neural text generators require a vast amount of data to analyse to predict human-like suggestions. AI Dungeon, a limitless text adventure game, is an extraordinary example of neural text generation.

AI Dungeon uses a massive deep neural network to deliver an engaging experience. Players decide themselves what to do next instead of choosing from options given by the developers.

Auto-corrections and Auto-suggestions

These features would be necessary nowadays in many digital products. As tech is embraced in our lives, AI comes in handy to speed up various processes like typing. Google Search takes advantage of an autocompleted AI features to suggest the most probable phrases, so that users can find what they are looking for faster. It’s especially important for mobile experiences, since typing on small screens can be challenging. Another example is SwiftKey, which is an intuitive keyboard that learns from the user and suggests appropriate words. Users can switch between different languages and still get adequate corrections.

The growth of artificial intelligence is driving a whole new class of mobile app possibilities. AI has been influential in app development for several years already, beginning with Apple’s Siri, and it has the potential to advance much more in the coming years.

Machine learning has moved out of its infancy, and users now want flexible algorithms for seamless and intuitive experiences. The new availability and advancement of AI and machine learning are causing a revolutionary shift in the way that developers, businesses, and users think about intelligent interactions within digital products.







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