With all the increasing buzz around artificial intelligence (AI) becoming more and more prevalent in the advertising and marketing world. But what does AI in marketing mean and how is it being used by marketers?
In this post, we explore what benefits AI has for marketers as well as provide some real-life examples of how AI is currently being used within marketing.
What is AI?
A simple definition of AI is any machine or computer system that simulates some or other aspect of human intelligence. Today, however, the term encompasses such a broad range of different meanings in different contexts, that a precise definition is becoming increasingly difficult to tie down (and in all honesty, beyond the scope of this post!).
As well as including various subfields - such as machine learning (ML), computer vision (CV), and natural language processing (NLP) - the term AI is often used to describe a number of very different computer systems that, while certainly sophisticated, may or may not accurately be described as ‘intelligent’.
Nonetheless, the most important aspect to understanding AI is that it consists of a fundamentally different approach to creating software. While traditionally programmers would explicitly code the desired outcome, the aim of AI is for a system to learn and improve by itself without the need for a specific set of instructions to accomplish the goals they have been given.
How is AI used in marketing?
For marketers, AI unlocks a whole new world of possibilities by leveraging technologies that can be applied at each step of the conversion funnel. As such there are already numerous use cases for AI in marketing.
For example, using models of perception through augmented/virtual reality (AR/VR) that use computer vision to enhance engagement between brands and consumers and have a better understanding of sentiment analysis. Or, using models of thinking through the use of natural language processing (NLP) to create chatbots that improve customer experience.
Or yet again, using machine learning algorithms and neural networks to crunch large amounts of data coming from the ever-increasing sensors and signals to predict patterns and gain a competitive advantage. Possibilities are endless and are truly only limited by our imagination and technical understanding.
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What is the benefit of AI for marketers?
Any situation in which companies have lots of data on trends and behavior with an objective to predict an outcome and/or recognize a pattern is a potentially good use case for AI.
Thus, one of the key benefits of AI for marketers is processing and analyzing vast amounts of data to uncover trends or anomalies so marketers can optimize campaign performance. Further, sophisticated AI platforms are now able to provide recommendations to marketers on how best marketing teams can optimize their campaigns as well as provide significant cost savings.
There is also evidence that employing AI capabilities has made some businesses more resilient. A study by the Business Development Bank of Canada (BDC), for example, found that businesses that leveraged AI in their web stores through recommendation engines and better targeting led to more than 50 percent of their sales coming from online sources. Arguably, those were the brands that outperformed others during the pandemic, with 39 percent reporting that they were able to maintain or increase revenue. Therefore, a strong data-driven approach managed to increase revenue through the pandemic for some businesses.
These are some key advantages that AI can enable for marketers:
- Better understanding of the customer by taking a larger array of data points into context
- Faster time to insights by processing large amounts of historical data that uncover patterns for future predictions.
- Significant cost savings by running prescriptive analytics that recommend budget shifts toward top-performing campaigns
- Streamlining operations by automating redundant tasks and freeing up time for higher-value work
- More personalized and engaging messaging through dynamic content.
Related articles:
- What is a Cohort Analysis (and how can marketers use it)?
- What is Marketing Mix Modelling (and how can it help with marketing attribution)?
- Data vs Metric vs KPI vs Report
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