To many of us, machine learning might seem like just another trending concept in the industry. However, this technology has taken over operations and is here to stay. When you interact with a chatbot or get preferences online based on your hobbies, these are your basic examples of interactions with artificial intelligence and machine learning. Their scope has increased beyond and is actively used in today's marketing strategies. Here’s everything you need to know about Google’s reaction to AI content.
Today's advertising industry is constantly evolving, making it difficult for brands to keep up. In addition, innovations in the digital space are changing how people converse with brands. Companies use this to their benefit by analyzing data and creating marketing strategies and advertisements tailored to individual preferences. Personalized advertising campaigns are paving the way for a cookieless future, where marketers will have to find more methods to reach out to their consumers with or without data about them.
Machine learning is a branch of artificial intelligence whose distinct feature is that it doesn’t directly provide solutions to an issue but gives training solutions to apply the needed solutions. Machine learning reduces the tedious task of going through heaps of unstructured data. It provides valuable insights from the same data brands can use in their marketing campaigns, especially advertising.
Machine learning in advertising is a process where the technology takes information, analyzes the same, and provides results that can enhance the quality of work. The insights gathered from the collected data can be used by marketers to personalize content, target the right audience, and influence media buying, amongst many other ways.
(Image source: nvidia.com)
In the ongoing deep learning vs. machine learning debate, the following differences between both will improve our understanding of the two subsets of artificial intelligence:
- Machine learning requires more human intervention to get the desired results. On the other hand, deep learning is challenging to set up but needs minimal intervention later.
- Machine learning is less complex and can be run on conventional computers. However, deep learning requires proper hardware and resources to operate smoothly.
- Machine learning can be set up quickly, but the quality of results cannot always be trusted. Although deep learning takes a lot of time and hard work, it provides guaranteed results instantly and improves quality when more data is available.
- Machine learning needs structured data and uses traditional algorithms. Deep learning incorporates neural networks that can accommodate vast amounts of unstructured data.
- The general public is practically using machine learning. Deep learning targets complex and autonomous programs, like driverless cars or robots performing surgery.
Machine learning is an extension of artificial intelligence. We understand artificial intelligence as a science that makes machines imitate human thinking capabilities. Past experiences assist devices in making predictions for the future, helping companies formulate campaigns well ahead of time.
Machine learning analyzes historical data and behavioral patterns without the help of proper human interaction. As a result, tasks and processes involving methodical steps can be streamlined through machine learning technology. With such technology, companies can save a lot of resources, especially time and money, by automating most processes. This further enables employees to focus on other business problems.
The role of machine learning in marketing is that it allows marketers to make decisions quickly based on the available big data. Some notable benefits of machine learning in marketing are:
- Improves the quality of data analysis
- Enables marketers to analyze more data in less time
- Helps in quickly adapting to changes and new data
- Automates marketing process and other routine work
- Simplifies the key operations of the marketing industry
Marketers aim to opportunely bring the right product in front of the right customer. Timing is vital here, and opportunities don’t come as quickly as presumed. This is why marketers narrow categories and cater to more specific niches, never to miss opportunities. Machine learning is used to help marketers get more accurate with personalization and targeting.
With machine learning and artificial intelligence, advertisements are getting more relevant and delivering higher investment returns. Some of the techniques how marketers are using machine learning to create advertising campaigns are:
(Image source: Ranktracker)
Predictive targeting is a technique where machine learning predicts a person’s future decisions based on historical data and behavioral patterns shown in the past. The data is used to foresee how a person would react to the advertisement. It could be engaging with the product or purchasing it in the spur of the moment. Predictive targeting tools help marketers create customer personas and target those sections that are in sync with the advertisement.
One of the best ways to improve a person’s buyer journey is by recommending products based on their likes. However, the relevancy of the advertisement could be subjective depending upon the individual's mindset. But it takes the guesswork out of the process. If the person doesn't engage with the promotions, they are most likely uninterested in the product. For example, if there is a specific genre you watch more on Netflix, machine learning will automatically recommend shows and movies that come under that genre.
(Image source: Ranktracker)
The most significant development in the recommendation process is that marketers use machine learning to move from explicit feedback to implicit feedback. Explicit feedback was dependent upon the information supplied by the customer, like their preferred brands to shop from. However, implicit feedback makes recommendations to understand the intent and behavioral signals.
With more specific recommendations, developing advertising campaigns has become uncomplicated. Machine learning enables marketers to predict what a person will buy even before they know about the product's existence. The behavior towards recommendation is being analyzed in real—time now. The future of machine learning is that historical data and reactions to recommendations will impact advertising campaigns.
Even though the goal of machine learning in advertising is to personalize and target the consumer at the appropriate time, there are other benefits to this. Ad personalization will create a better relationship between the company and its audience. You can also improve brand safety and brand awareness by improving the trust factor. A word of caution here is to advertise only in those places where things are safe and positive.
The most significant benefit machine learning gives marketers is that it speeds up the decision—making process, especially in advertising. Since your decisions will be based on data analysis, machine learning does the analysis quicker than you could manually. As a result, all your advertising decisions will be based on well—researched data, not just a hunch.
One—size fits all concept is a thing of the past. Machine learning has created a clear path for marketers where preferences, likes, dislikes, behaviors, and patterns are deeply analyzed. Soon, we can expect more advancements in machine learning, which can improve the process through which marketers create advertising campaigns.