Using Machine Learning For Ad Targeting, Customer Behavior And Experience, Stats Calculation And Prediction
As a marketer, we need to have a better understanding of the technologies for the present and future. Natural Language Processing (NLP), Voice Search, Virtual and Augmented Reality, Virtual Assistants, Artificial Intelligence and Machine Learning, are to name a few.
Let's take one of these crucial developments, machine learning and delve deeper into it and find out just how is it reshaping marketing.
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We’re living in an era of digital transformation. There is only one constant. "CHANGE".
That change is brought forward by everyday advancements in technology and innovation. Even, smartphones are invention from the past and things are moving fast.
It’s no secret that marketing and everyday business tasks are being completely transformed by technology. Machines, artificial intelligence (AI), and intricate algorithms are becoming the new way for businesses to get ahead of their competition, and one huge way is through a combination of machine learning and marketing.
Machine Learning and AI Insights
Whether you’re organizing data or improving the customer experience, machine learning and AI go hand-in-hand. They are absolutely critical in creating the ideal customer experience with the highest growth rate possible for your business. Machine learning takes the extra step of doing tasks that, while incredibly necessary, are nearly impossible for the average human to do on their own.
For example, machine learning is a great way to improve the consistency and effectiveness of your business. In fact, SalesForce reports that over 57% of buyers will expect companies to know exactly what they need before they even ask by 2020, something AI and machine learning can do. On top of that, Gartner predicts that 85% of customer interactions will be not be managed by a human by 2020 as well.
Machine learning is a huge trend that isn’t slowing down anytime soon, and one of the biggest tips to using machine learning in marketing is to actually use it in your marketing strategies. Going deeper into this, while machine learning is a huge plus in business, we also have to consider deep learning, a broader form of machine learning that is taking a leap into the marketing world.
Machine Learning and Deep Learning
Machine learning and deep learning are two very similar concepts. As a matter of fact, deep learning is actually a subset of machine learning. Artificial intelligence, machine learning, and deep learning all go together to create the best marketing and customer experience for your brand. To use one, you need the others as well.
To better understand the differences between these two types of learning, consider that deep learning is much more powerful than machine learning in the sense that it can deal with larger data sets; conventional machine learning is best used for smaller and more simplified sets of data. Deep learning can tackle complex types of data and better understand they are saying, essentially getting a “deep” understanding of it.
Supervised vs Unsupervised Machine Learning
Embedded content: https://bigdata-madesimple.com/wp-content/uploads/2018/02/Machine-Learning-Explained2.png
Like we mentioned, deep learning is a part of machine learning. Something else these share is that the “learning” can be supervised or unsupervised depending on the type of data sets and tasks being done. Both supervised and unsupervised machine learning algorithms are central when it comes to developing your business to be the best it can be.
A differentiating factor between them is that supervised machine learning is used when the desired outcome is known and there is a correct answer. These kinds of algorithms are mainly used to map inputs to outputs. That is to say, when you want a system to learn a function for a sample of inputs and outputs.
Unsupervised machine learning, on the other hand, is when the desired outcome is unknown and you want a system to learn the inherent structure of a data set so it can then infer trends from it. In this way, unsupervised learning is especially helpful in the marketing world, a place where customer segmentation and related tasks are used day in and day out.
To better understand the differences of these two, think about it like this:
Unsupervised learning is like learning without a teacher, figuring it out on your own and being creative.
Supervised learning is having a teacher there with you, helping you find the answers you’re looking for along the way.
Both of these learning models are used widely in a variety of businesses across many industries, but like we said, when it comes to marketing, unsupervised machine learning is completely reshaping everything as we know it.
"If machine learning is a pack horse for information processing, a neural network is the carrot that draws the horse forward."
Deep learning and neural networks | theconversation.com
How Is Unsupervised Machine Learning Reshaping Marketing?
Machine learning in sales and marketing is an efficient way to find out more information about the customer and target them as best as possible. There are countless examples of machine learning in marketing, specifically unsupervised. For instance, things like organizing and clustering data into unlabeled clusters is what makes unsupervised machine learning so unique.
While supervised learning uses labels to put things in their place, unsupervised is more on its own and figures things out with much less data, effectively becoming a much more powerful tool. With it, companies can uncover trends that they would not have been able to catch otherwise.
Unsupervised learning is also often used for a variety of things like finding specific customers, catering to specific groups, and learning actionable customer insights. Let’s take a look at some other examples so we can actually learn how to apply machine learning to marketing:
1. Unsupervised Machine Learning Makes It Easier To Predict Stats and Behavior
Predicting the future is nothing short of legendary. With unsupervised machine learning, data can be analyzed and clustered into organized sets of information without any prior labeling, giving companies insights they need. Look at it this way: do you always have complete data sets about things like customer behavior? Probably not. In these cases, cases where there’s simply not enough information for normal algorithms to “learn,” unsupervised learning can metaphorically fill in the blanks and draw its own conclusions. For example, you may know that a particular page in your website has both a high bounce rate and conversion rate. Normally, you wouldn’t be able to pinpoint exactly why unusual behavior is being exhibited, but with unsupervised learning you can. It can look at the data you do have, analyze it, and discover “hidden” stats and knowledge within it.
2. Unsupervised Machine Learning Helps Prioritize Ad Targeting Through Segmentation
Ad targeting a very big part of what makes ML in marketing so great; you can use it to cater to specific customers who are more likely to covert. By ensuring your audience receives ads and content that they actually want to see and interact with, ad targeting functions as the next best thing to reading minds, and unsupervised learning can make it even stronger through segmentation. For instance, like we touched on above, you don’t always have as much information on your users as you’d like. Here’s where unsupervised learning comes into play. It can explore the data you do have, and then find similarities and patterns you or anyone else on your team would have missed. Knowing this, you can improve segmentation and split your users into groups that will respond better to certain messages.
3. Unsupervised Machine Learning Improves The Customer Experience
Without customer insights, your marketing and ad creation efforts would be solely based on guessing. Machine learning for brands, specifically unsupervised, is essential to organizing data into groups based on what a customer is like and what they interact with, as well as figuring out how to interact and further your relationship with them. In other words, unsupervised learning allows you to better understand your audience and know how to market to them at the best of your abilities, which means that you can offer them a better customer experience as a whole. As we all know, whether to turn to one brand over another is usually determined by your experience with them, and if you can offer them a better experience than anyone else, you know you’ve got them.
Machine learning offers an avenue for gathering data that helps us create tailored marketing strategies. When we narrow the scope and focus on unsupervised machine learning, this means gathering hidden data that leads to quality, actionable insights. For example, whereas supervised learning needs explicit labels of inputs and outputs to function, unsupervised learning bypasses this and can draw conclusions from data that’s much more ambiguous and harder to classify.
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About the Author:
Hannah Gabaldon is a content creator with a passion for digital and influencer marketing. She works for Aumcore, a digital marketing agency that focuses on creating the best paid advertising campaigns for their clients.