Blog Post:When we look at the current landscape for marketing professionals, it is easy to forget that many of the tasks we currently undertake did not exist 20 years ago. Marketers were not concerned with social media or SEO. The ever-increasing role of technology in marketing has made it tough to truly understand what a marketer’s role will look like in another decade. One thing we know for sure is that machine learning will play a big part in how marketing changes over the next several years. Machine learning is already playing a part in data-driven marketing strategies, and we will see that role continue to expand over time. What is Machine Learning? Machine learning seems to have many connotations and is often associated with artificial intelligence (AI). As movies have taught us through the years, AI seems to imply that machines will one day replace humans. Fortunately for all of us, that is not the goal with machine learning. Instead, we are hoping to create systems that can solve problems for marketers more quickly than they could for themselves. To do that, though, marketers still need to determine what the problems are, what the parameters are for solving them, and which datasets will be used. Machine learning can then optimize and refine possible solutions based on the data provided and patterns within that data. However, for any of this to be possible, a marketer still needs to first create the strategy for the program. Perhaps, it is easier to convey this idea through an example. Imagine asking your phone a question. The phone is programmed to quickly find a solution for you based on a specific set of data from which it can pull; it cannot pull information from just anywhere. In addition, it cannot decide to use inappropriate language, tell you that your question is dumb, or refuse to answer. The machine is learning based on data provided, but it is not thinking for itself; marketers set the parameters for how they want customer interactions to play out. How Does Machine Learning Impact Digital Marketing? Over time, we expect to see machine learning travel less of an AI path and more of an intelligent assistant (IA) route. In its current state, machine learning does not have the ability to think for itself. We do not necessarily want it to do that; we would lose the beauty of creativity that humans bring to the table. What would be a beneficial improvement is to see machine learning progress from its current state — where we ask it to complete one very specific task, as in the phone example above — to a state in which it can learn to identify and predict our needs. Again, perhaps an example would be helpful. If every time you ask your phone to find a restaurant for you, you immediately ask it to also request an Uber, IA solutions would learn this pattern, and eventually, when you ask for a restaurant, your phone will ask if you would also like to request an Uber. This is still based on a limited set of behaviors and data points. However, like any good assistant, a phone with true intelligent assistance will be capable of learning to give you exactly what you want — even before you ask for it. So that leads us to the question, “how does this impact digital marketing?” In the marketing world, we are inundated with data. IA technology can help us to process what is statistically relevant in that data and why it occurred. In the very near future, we can program it to do this proactively. This type of advance in IA technology will allow digital marketers to be even more proactive when acting on real-time insights. What is the Future for Machine Learning? When it comes right down to it, machine learning is already here. So what do we expect to see in the near future? Machine learning will begin to be less of a one-to-one interaction in which you ask one question and get one specific answer. We will first see it evolve into learning your preferences and patterns, and from there, it will become more proactive. IA technology will recognize questions that you might not be asking but should be. In the longer term, we will see machine learning make it possible to take into account external factors, such as the weather and competitive analysis, to understand both the micro and macro factors influencing or causing an event. We will be able to understand why customers chose us over a competitor on Black Friday — whether it was because our newsletter went out before theirs, our product was better, or our pricing was more attractive (or, hopefully, all three). These types of insights that include external factors give us access to even more insights that we have never before been able to understand. Machine learning truly has the ability to revolutionize the way we go to market and how we understand our customers. Being able to implement this technology quickly and efficiently will put many companies at the head of the pack. Getting your data and marketing strategy in line now will allow you to be on the edge of the machine-learning curve. Author: Date Created:January 27, 2016 Date Published: Headline:Hey, Data-Driven Marketing, Ready for Machine Learning? It’s Ready for You. Social Counts: Keywords: Publisher:Adobe Image:https://blogs.adobe.com/digitalmarketing/wp-content/uploads/2016/01/AdobeStock_73374886-e1453517278876.jpeg

When we look at the current landscape for marketing professionals, it is easy to forget that many of the tasks we currently undertake did not exist 20 years ago. Marketers were not concerned with social media or SEO. The ever-increasing role of technology in marketing has made it tough to truly understand what a marketer’s role will look like in another decade.

One thing we know for sure is that machine learning will play a big part in how marketing changes over the next several years. Machine learning is already playing a part in data-driven marketing strategies, and we will see that role continue to expand over time.

What is Machine Learning?

Machine learning seems to have many connotations and is often associated with artificial intelligence (AI). As movies have taught us through the years, AI seems to imply that machines will one day replace humans. Fortunately for all of us, that is not the goal with machine learning. Instead, we are hoping to create systems that can solve problems for marketers more quickly than they could for themselves.

To do that, though, marketers still need to determine what the problems are, what the parameters are for solving them, and which datasets will be used. Machine learning can then optimize and refine possible solutions based on the data provided and patterns within that data. However, for any of this to be possible, a marketer still needs to first create the strategy for the program.

Perhaps, it is easier to convey this idea through an example. Imagine asking your phone a question. The phone is programmed to quickly find a solution for you based on a specific set of data from which it can pull; it cannot pull information from just anywhere. In addition, it cannot decide to use inappropriate language, tell you that your question is dumb, or refuse to answer. The machine is learning based on data provided, but it is not thinking for itself; marketers set the parameters for how they want customer interactions to play out.

How Does Machine Learning Impact Digital Marketing?

Over time, we expect to see machine learning travel less of an AI path and more of an intelligent assistant (IA) route. In its current state, machine learning does not have the ability to think for itself. We do not necessarily want it to do that; we would lose the beauty of creativity that humans bring to the table. What would be a beneficial improvement is to see machine learning progress from its current state — where we ask it to complete one very specific task, as in the phone example above — to a state in which it can learn to identify and predict our needs.

Again, perhaps an example would be helpful. If every time you ask your phone to find a restaurant for you, you immediately ask it to also request an Uber, IA solutions would learn this pattern, and eventually, when you ask for a restaurant, your phone will ask if you would also like to request an Uber. This is still based on a limited set of behaviors and data points. However, like any good assistant, a phone with true intelligent assistance will be capable of learning to give you exactly what you want — even before you ask for it.

So that leads us to the question, “how does this impact digital marketing?” In the marketing world, we are inundated with data. IA technology can help us to process what is statistically relevant in that data and why it occurred. In the very near future, we can program it to do this proactively. This type of advance in IA technology will allow digital marketers to be even more proactive when acting on real-time insights.

What is the Future for Machine Learning?

When it comes right down to it, machine learning is already here. So what do we expect to see in the near future? Machine learning will begin to be less of a one-to-one interaction in which you ask one question and get one specific answer. We will first see it evolve into learning your preferences and patterns, and from there, it will become more proactive. IA technology will recognize questions that you might not be asking but should be.

In the longer term, we will see machine learning make it possible to take into account external factors, such as the weather and competitive analysis, to understand both the micro and macro factors influencing or causing an event. We will be able to understand why customers chose us over a competitor on Black Friday — whether it was because our newsletter went out before theirs, our product was better, or our pricing was more attractive (or, hopefully, all three). These types of insights that include external factors give us access to even more insights that we have never before been able to understand.

Machine learning truly has the ability to revolutionize the way we go to market and how we understand our customers. Being able to implement this technology quickly and efficiently will put many companies at the head of the pack. Getting your data and marketing strategy in line now will allow you to be on the edge of the machine-learning curve.