Machine learning is where technology platforms learn by themselves to do something rather than being instructed. It can be used for applications like data-mining that is, examining large amounts of data to try and find previously undiscovered relationships within it. It can also be used for the recognition of images, language processing and advanced systems. Gaming is also a popular application of machine learning.
Artificial Intelligence (AI) however, is the ability of computers and machines to learn tasks normally associated with humans. This includes language, problem solving, perception and most importantly, decision making. True artificial intelligence resembles human behaviour in the sense that it reacts to what it learns then knows and making connections based on that new knowledge. The more it learns the more intelligent it gets. In order to understand AI better, let us look at the areas it is based on and how this is categorised. This can be illustrated in the following way.
The combination of mathematics, science, psychology, language, technology etc. can help to provide outcomes which not only make sense of complex information but save valuable time. In reality, achieving true AI is still many years away whereas machine learning is already established and is the closest thing we have to AI.
AI will help companies better interact and respond to its target audience whether B2B or B2C. This will save time in not having to think about and test different scenarios of potential marketing outcomes.
Deep structured learning is similar to machine learning based on algorithms which look at replicating high level representations of data. This type of social semantics is likely to be used more in the future on social media networks by companies in order to study social behaviour to look for trends. This in turn can then be fed back for marketing purposes to identify products and services which might be attractive to particular groups of people.
Microsoft tried this with its computer programme AI ‘chatbot’ which tried to simulate intelligent and learned conversation. It created ‘Tay’ and opened a Twitter account to test the interaction on social media. However, within hours of it being launched ‘Tay’ began to communicate abusive and controversial messages and was taken offline.
Although ‘Tay’ was clearly not advanced enough yet to interact at this level, it does show that this type of AI is closer to becoming a reality on a large scale in the future. This demonstrates the risk involved in delegating this type of activity to as well as keeping a check on how well machine learning and AI are able to deal with the quality of an experiment like this.
AI will help to companies to understand and disseminate the marketing message across your audience segments, personas and demographics. It will decide the best way and time to reach someone on social media. It will also be aware of where someone is in a particular area of a country or region.
The increase in areas like AI changes the landscape in marketing terms and the balance between traditional and digital marketing. Areas like mobile video will add to a potential customer’s ability to access brands in an interactive way. The future of digital marketing is about how to improve techniques in order to deliver exciting and relevant products and services.
Knowing which types of technology to invest in and how to get the best out them is a challenge that companies face in order to achieve their growth potential. Getting this right will help to increase brand awareness, customer loyalty and improve sales performance.
Digital Marketing for Business Growth covers topics that need to be addressed so that plans to achieve sales growth can be implemented successfully.
Posted on October 25 2019 by Julian Clay
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