Today’s advertising and marketing demands we familiarise ourselves with all things AI. From beauty to banking, a myriad of industries and brands are unleashing the potential of artificial intelligence to provide users with an unparalleled consumer experience. At stake for marketers is a wide array of benefits such as virtual assistants, curated recommendation lists, self-generating content and interactive ads to help stay ahead of the game.
Being out of touch will just not do, as you will end up missing out on significant technological advancements. One needs to be able to speak the language of technologists, or you will find yourself left in the dust. If you’re a marketer with a paltry knowledge of computer science and data-related terms, fret not. With this handy checklist of basic AI terms, you won’t find yourself sweating bullets next time you’re staring at tech documents.
Chatbots: Otherwise known as chatterbots, interactive agents or simply bots, chatbots are computer programs which are utilised in messaging apps and websites. Chatbots are capable of a variety of activities, from simply providing information to more complex activities like booking events, planning schedules and sending money. An integral part of customer relations, chatbots employ text, images and voice to dispense their services.
Machine Learning : Machine learning is a data analytics process by which computer systems use algorithms to “learn” experiences and information in a way which mimics human behaviour. As the computer gains access to more information, thus “learning” new facts, the better it can be deployed in providing tailored consumer experiences.
Data Mining: Data mining is the process by which computers extricate, analyse and generate patterns and insights from mountains of raw data. Used for trend and database analysis, revenue adjustment, and customer relationship management, data mining is crucial to a range of industries, such as healthcare, education, banking, customer services, and marketing research.
Deep Learning: Deep learning falls under the umbrella of machine learning and uses artificial neural networks to constantly process and analyse massive amounts of data. Like its name suggests, deep learning involves highly complex structures. Applications such as automatic text and handwriting generator, self-driven cars and automatic colourisation all involve deep learning
Unstructured Data: Like the name suggests, unstructured data lacks any definite data model or structure. This type of data tends to be in text form, but can contain figures such as dates and numbers. Pictures, social media information, website content are all examples of raw or unstructured data.
Dynamic Pricing : If you’re a regular user of ridesharing apps such as Grab and Uber, you would be familiar with the phenomenon of surge prices. This is a great example of dynamic pricing, a fluid pricing system which responds to demands and supply. A fully automated process, dynamic pricing uses complex consumer data to provide optimal prices.
Clustering: Clustering breaks down large chunks of data into smaller, digestible groups based on similar nature or content. This algorithm is based on the human behaviour of classifying objects in specific categories. The division of music and content according to genre in popular streaming apps Spotify and Netflix is an example of clustering.
Natural Language Processing : Computers do not understand human languages unless Natural Language Processing (NLP) technology is used. Translation and voice recognition services such as Google Translate and Siri are examples of programs which deploy NLP in interacting, analysing and learning from human text and speech.
Neural Network : This biologically-inspired computing process is modelled after the workings of the human brain, and assist AI products in carrying out tasks such as image, speech and voice recognition and generation. Often applied in Natural Language Processing, neural networks are used in popular applications such as Snapchat, Microsoft Word and Facebook.
Semantic Analysis: Borrowed from linguistics, the term refers to language-building while accounting for cultural considerations. A high-brow version of Natural Language Processing, semantic analysis helps machines gain a deeper context of abstract human language such as idioms, metaphors and figures of speech. Semantic analysis helps machines to generate automated content.
As marketing becomes increasingly AI-driven, it is imperative that executives and decision-makers keep themselves abreast of emerging terminologies. While this list is by no means exhaustive, it is a fundamental primer for those wishing to educate themselves and take their brands to the next frontier of marketing.