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Call it invisible technology, machine learning is not just for the movies anymore. Machine learning and A.I are going to be colossal especially for our forward-thinking event tech partners. So how will machine learning influence audience behaviours?
First things, first. Let’s make some key definitions.
Machine learning VS. AI: what's the difference?
Although AI and machine learning are related; they are not the same thing. AI is a branch of computer science attempting to build machines capable of intelligent behaviour, while Stanford University defines machine learning as “the science of getting computers to act without being explicitly programmed”. You need the AI researchers to build the smart machines, but you need machine learning experts to make them truly intelligent.
Machine learning focuses on the development of computer programmes that can teach themselves to grow and change when exposed to new data. It that sense, it’s self-learning. Based on data that’s fed to or collected by the computer, the computer can make new decisions by itself to improve on efficiency and achieve its goals.
For example, suppose you were searching for ‘touch associates' on Google but accidentally typed ‘torch assiates’. After the search, you'd probably realise you typed it wrong and you'd go back and search for 'touch associates' a couple of seconds later. Google’s algorithm recognises that you searched for something a couple of seconds after searching something else, and it keeps this in mind for future users who make a similar typing mistake. As a result, Google 'learns' to correct it for you.
As you can see, is already a part of our daily lives. When it comes to events planning, we can say that machine learning has some peripheral applications already. Yet in true touch style we imagine the future. Based on cases of current machine learning applications, we can predict three future uses and benefits for the communications industry.
1. Predict audience decision-making
One of the most popular applications of machine learning right now is for prediction and decision-making. Computers can read more and analyse quicker than human minds can. We can use machine learning algorithms to continuously learn and adapt to changing behaviours in real-time to make real-time predictions.
For a long time, we have relied on traditional behavioural targeting to reach audiences. As technologies improve, more businesses are turning to predictive audience targeting, fuelled by customer intent data, to drive higher performance by more accurately identifying users that are most likely to engage.
For example, Event App users will display similar behaviours when using the App. Predictive targeting tools that act on pooled datasets capturing these various behaviours can be exponentially powerful due to allowing the use of additional insights on the audience, outside of what they know from interactions taken on their own App. This is key to positive user engagement and improving operational efficiency during event development.
2. Opinion mining the preferences of attendees
Opinion mining helps organizations to determine public opinion on a certain product or topic, based on analysis of text - usually audience comments, social media posts and online conversations.
In short, opinion mining tells you how your audience feels, likes, dislikes or don’t cares. In this way, opinion mining has its advantages for clients who want to identify what their audience wish to experience. Will your future conference participants want to hear about AI applications or UX design? How popular and impactful were last year’s talks and workshops, and which ones should you repeat or improve upon? Opinion mining can help gain insight into areas such as these, giving predictive wisdom for audience engagement down the road.
3. Achieve greater audience segmentation
Data allows us to discover more about our audience giving us a better understanding of how best to engage with them. Data analysis can help suss out new clients. For example, you may be able to segment who on your mailing list is new in the industry and interested in audience engagement. Machine learning can make this segmentation process automatic and seamless.
Machine learning is undeniably useful, but will is erase the need for the human touch in the communications and engagement industry? Our conclusion is a resounding no. Humans have always developed new technologies to save time. Machine learning and artificial intelligence operate the same way. By having artificial intelligence and machine learning handle the mundane, repetitive, and tedious jobs, we can give our people the extra time needed to be creative, innovative, and add a magic touch to their work.