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Artificial Intelligence (AI) is transforming the digital landscape. AI can help marketers in a number of key areas. Firstly, the automation of processes. Secondly, personalisation, which in turn can increase consumer engagement and improve marketing communications. Thirdly, data analysis. And lastly, content research and creation.
A recent survey found that 85% of marketing professionals expect AI to make a significant impact on their marketing activity over the next five years. However, only 47% of marketers say they strongly or somewhat agree they understand how to incorporate AI into their strategy, indicating a substantial knowledge gap.
So, it’s time to get our heads around the meaning of some basic AI terms.
A set of rules or instructions given to an AI, neural network, or other machines to help it learn on its own. Algorithms are what machine learning programs use to make predictions from the data they analyse. For example, if a machine learning program analysed the performance of a set of social media posts, it could create an algorithm to determine which titles get the most clicks for future posts.
Intelligent algorithms can also personalise information for consumers. This could include offers and experiences based on known information, such as location, demographics, device and past behaviours. A good example is displaying relevant information and content on a website.
Using natural-language processing and machine learning, an AI-bot can help consumers get answers without having to speak to a human. Chatbots use machine learning to detect and mimic human conversation. They are programmed to learn from previous customer conversations so they can improve over time. They can resolve issues far more quickly and have proved to be particularly effective in engaging consumers.
Digital assistants are very similar to bots, but they are even handier as they can answer questions from our voice commands. And voice-enabled digital assistants (also known as intelligent agents, virtual personal assistants, virtual intelligent assistants, automated assistants or virtual agents) can conduct conversations with a human. Examples include Apple’s Siri, Google Now, Amazon’s Alexa and Microsoft’s Cortana.
Cognitive computing systems simulate the human cognition process to find solutions in complex situations where the answers may be ambiguous and uncertain. To achieve this, cognitive systems include self-learning technologies that use data mining, pattern recognition and natural language processing (NLP) to mimic the way the human brain works.
Computer vision is an application of deep learning that can “understand” digital images. A simple example is the facial recognition that Facebook uses when suggesting people to tag in a photo. Ebay uses computer vision and deep learning for its visual search feature. It can compare images uploaded by a shopper to find products for sale on its site. Advanced visual search can identify what’s in the photo, such as a restaurant, and then supply additional information, such as the phone number, hours of operation and its rating.
This is the process of computers discovering patterns within large data sets. A good example is Amazon, who use data mining to analyse customer data and give product suggestions through the “customers who bought this item also bought” box. Data mining tools also allow enterprises to predict future trends.
Deep learning is an advanced subset of machine learning, also known as a “neural network” because it mimics the way neurons are layered in the brain. By doing this, deep learning can find complex patterns in data sets.
The ability for a program to absorb huge amounts of data and create predictive algorithms. Programs with machine learning discover patterns in data sets that help them ‘learn’ over time without being programmed. As they analyse more data, they adjust their behaviour based on what they have learnt.
Natural Language Generation (NLG) is a technology that simply turns data into plain language. In other words, this means it can look at your data and write a story from it, just like a human analyst would today.
NLS is the process of analysing language data. On a basic level, spell check in a Word document or translation services on Google are both examples of NLS. For bots, NLS enables them to understand text or voice commands. For example, when you talk to Siri, your voice is transposed into text, which can be used to query a search engine and then respond back in human syntax.
Predictive analytics helps marketers understand the likelihood of future events and the next action to take in response. For instance, it might identify that certain prospects buy a certain product and spend a certain amount after a specific sequence of activities. With this insight, the marketing team can invest in the right resources and tactics to help drive more sales.
AI-driven information filtering systems that can automatically predict user preferences and responses to queries based on past behaviour, one user’s relationship to other users, similarity among items being compared and context.
The word semantic is a linguistic term. It means something related to meaning in a language or logic. So, if a machine that has natural language processing capabilities can also use semantic analysis, that means it can understand human language and pick up on the contextual cues needed to understand idioms, metaphors, and other figures of speech. This can help with content creation and automation, very useful for blogs and ebooks.
A type of machine learning in which humans input specific data sets and goals and then supervise the process. Examples include weather prediction and spam detection. Unsupervised learning is when the machine learning program is left to find patterns and draw conclusions on its own.
Whether you’re exploring chatbots, automation, or AI-powered content creation, understanding the A-Z of AI for marketers is just the beginning. At Bombora, we help brands harness emerging technologies, from virtual event platforms to intelligent engagement tools, to amplify your marketing performance. If you’re looking to make AI a practical part of your marketing strategy, get in touch. We’ll help you cut through the jargon and unlock real results.