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Machine Learning In Marketing

Machine learning is one of the most powerful concepts in computing. The best marketers know that it’s a tool to be embraced and imaginatively applied, not to mention a lever that can be used to multiply marketing effectiveness at huge scale.

Every marketer has heard the hype about machine learning, but few have the expertise or the tools to meaningfully apply it.

They may have considered using machine learning as part of a broader customer analytics strategy, but today, just as before, their most important data is sitting in databases – some purchased from big vendors, some maintained internally – and connected by poor-performing software.

What Is Machine Learning In Marketing

Machine learning is a process by which computers can learn to perform tasks without having been explicitly programmed. It is already being used by marketers to improve online sales, automate customer service, and prioritize product development.

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The effects of this change in marketing extend far beyond the boundaries of the data centre where your business is located. The total amount of money invested in machine learning globally in the first quarter of 2019 was $28.5 billion.

Artificial intelligence (AI) is the use of computer algorithms to do things that no amount of human input could accomplish on its own. Machine learning is a subset of AI that involves using data to create an increasingly sophisticated understanding of patterns in the world around you – without having had to be explicitly programmed.

Benefits Of Machine Learning In Marketing

Digital marketing efforts are becoming increasingly sophisticated, making use of machine learning models to optimize customer engagement and lifetime value. These models are able to analyze large amounts of data to identify patterns and trends, allowing businesses to make better informed decisions about their marketing strategies.

Customers are therefore more likely to have a pleasant brand experience and stay loyal to it over time. The US deep learning software market is expected to be worth $80 million by 2025.

Data analysis and data science can help businesses to optimize marketing campaigns through advanced machine learning models.

1. Machine Learning Is Far More Powerful Than Databases

Data-driven marketing is quickly becoming the standard for most companies, especially those with some of the largest advertising budgets and customer data sets. The costs of building their own data platforms are just too high, so they have turned to big vendors who have built and maintained their databases for decades.

The problem with these relationships is that in many cases, it has led them to become blind to alternatives – not just with respect to machine learning, but also to other tools that could help them make better decisions faster. In fact, the cost of siloed data management is one reason why many major companies struggle to turn a profit.

2. Machine Learning Can Trigger Radical Operational Change

Machine learning can help you get to radical operational change much faster than you would if you were trying to do it the traditional way – which is usually a long and painful process anyway.

With a predicted market size of $75.54 billion and a projected CAGR of over 33% between 2019 and 2023, the worldwide artificial intelligence sector is expected to increase at a rate of over 17% per year.

Traditional marketing plays out in the data center, where most of the planning and decision making happens. Even with big data, this works just not fast enough to support a fast-paced marketer looking for opportunities that can be taken advantage of today or tomorrow.

Machine learning works on a real-time basis, allowing all of your marketing people and leaders to react quickly to changes in customer behavior, market conditions, or competition.

3. Machine Learning Can Make Your Marketing Team Smarter

Too often, marketers find themselves working in silos, all of them trying to solve the same problem using what amounts to very similar tools. The result is that the outcomes are often lackluster and it makes it hard for anyone to gauge whether their work has had any significant impact on the business.

The way marketers have been trained to think about data is still too simplistic to achieve the optimal results, let alone measure them. When a marketer or analyst develops a hypothesis about what’s driving changes in customer behavior, they rely on very limited data sets – maybe only one channel at a time, or only one customer segment at a time.

4. Machine Learning Can Bring Data And Insights Together

The best marketers today understand that data is everywhere, and they look for new ways to integrate insights from everywhere too. The right machine learning algorithms allow you to combine disparate data sets with software that can find patterns in the information, patterns that were not apparent before.

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As machine learning algorithms get more sophisticated, they will be able to recognize and provide answers or solutions to problems without any human intervention at all – whether it’s predicting customer behavior based on past shopping history or using a sensor in a wearable device to measure heart rate or step count to predict the probability of a walk-in visit at the retail store.

5. Machine Learning Can Help You Provide New Customer Value

When a company has a big data problem, it often turns to its marketing team for help, but the marketers haven’t been trained to think about these questions from the perspective of customers or their behavior.

They are trained to look for patterns, identify patterns, and then suggest actions based on those patterns. This is not how machine learning works, though; in fact, machine learning is designed to identify and predict new or interesting customer behavior that might have no previous relationship with any known pattern.

6. Machine Learning Can Help You Understand Your Customers’ Expectations

The more data you collect, the more you know about the people who use your products and services. You can start to get insights into their behavior, buying habits, and attitudes. You can also learn about their preferences and expectancies – what they believe will happen next.

Machine learning will help marketers take advantage of these insights by creating a better understanding of customers’ experiences throughout the customer lifecycle, from discovery to retention to advocacy – all of which are important for both collaboration and for innovation.

7. Machine Learning Can Help You Make More Informed Decisions

By 2027, the global deep learning market is expected to be worth $44.3 billion, growing at a CAGR of 39.2%. Marketers are as as, if not more, answerable to their superiors than IT departments.

The more marketing data and insights you have, the faster your insights become and the better decisions you can make. Machine learning will help marketing decision makers take advantage of this knowledge by making better trade-offs and providing smarter next steps.

8. Machine Learning Can Help You Drive Profitability And Growth

Like any other business function, machine learning needs to be driven by measurable results and accountability. If the marketing team has been given a clear objective, such as improving market share, increasing sales, or driving profit, then machine learning can be used to provide those answers.

Even better, machine learning can help you discover the secrets of the marketplace that will allow you to provide these value-added capabilities at lower cost.

Risks Of Machine Learning In Marketing

Machine learning can be used to identify patterns in customer data, and this information can be used to target marketing messages more effectively. By using machine learning to analyze data, businesses can make better decisions about how to allocate their marketing resources.

$117.19 billion is the estimated size of the global machine learning market by 2027, with a projected CAGR of 39.2%. The use of data in the marketing industry is expanding and changing as well.

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Natural language processing is a form of artificial intelligence that is helping marketing teams to glean high quality data from sales teams and data scientists. This data can then be used to create more targeted and effective marketing campaigns.

1. Machine Learning Can Lead To More Misleading Results

One of the risks of machine learning is that it can make even bad data look great. If machine learning doesn’t know, for example, that a particular person is sick, or that a particular person has given up on a certain product or service, then all the analysis will be based on false assumptions. This is why it’s critical to have good quality data in the first place.

2. Machine Learning Can Supply Misleading Results

Brand managers and marketing analysts who start using machine learning without understanding how and why their AI software works are walking into danger territory. Good machine learning software should have a way to indicate which actions are likely to have the most impact on the business.

3. Machine Learning Can Be Gamed By Marketing Leaders

This is another way of saying that marketing team members may not be able to make objective decisions based on their analysis even if they are using the right data. If they are only looking at data that supports a particular action point, they can’t request funding for something else or present an alternative course of action.

If you know that machine learning is going to provide you with answers, you may find yourself more interested in how those answers get determined and less interested in understanding why someone would ask for one thing versus another.

4. Machine Learning Can Become Overly Dependent On Technology

A new technology can make a difference in the way a marketing team does work, but it has to be an efficient and productive tool. If it’s not, it might not be worth using. A machine learning algorithm needs to be designed carefully and implemented by well-trained marketer who has a clear understanding of its strengths and limitations.

5. Machine Learning Can Lead To Biased Outcomes Based On Limited Or Inappropriate Data

Bad machine learning uses bias in its algorithms – for example, if you have no idea about the data sets that have been used to train it. Another example is if the algorithm is trained on data that is no longer accurate.

6. Machine Learning Can Be A Risk To Your Job Security

Any decisions you make based on machine learning are likely to affect your job security. If you have a poor performer as a colleague, your manager might use machine learning to find out why and take action – even if it’s not warranted.

A marketing analyst who relies on machine learning, but doesn’t understand how it works or how it impacts other parts of the organization, could be looking for an exit strategy that isn’t there.

7. Machine Learning Can Be A Threat To Your Users’ Privacy

The anticipated market size for artificial intelligence (AI) hardware is $87.68 billion, with a CAGR of 376.0% from 2019 to 2026.

If machine learning is used for personalization, you might have to give up some of your customers’ data. This isn’t likely to happen unless you’ve worked very hard on the legal side of things.

8. Machine Learning Can Make Marketing More Susceptible To Hacking Attacks

The rise of AI has created an environment in which even minor security mistakes and failure points can lead to data breaches and privacy concerns. If your machine learning algorithms are unsecured or poorly designed, you might be running a high-stakes game of hacking roulette.

Final Note

It’s now clear that machine learning is not just a trend, it’s here to stay. If the technology industry is anything to go by, we will see more and more machine learning applications in most industries in future.

Clearly the opportunities are huge but so are the risks. Marketers should take time to understand how they can use machine learning before they jump on this bandwagon, otherwise they may find themselves as part of the collateral damage.

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