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As the amount of data that internet marketing service experts have to deal with grows every year, their workloads become more complex. In particular, while about ten years ago, data came to be gradually limited in quantity, it now has turned into Big Data. One of the most effective technologies for its processing, analysis, and application is machine learning (ML).
Machine learning solutions are used for business promotion for companies from various industries giving companies insights into customer behavior trends and enabling them to build efficient and accurate business models, as well as enhance new product development. We’ll talk about machine learning in digital marketing in more detail below.
Modern marketing is always about large amounts of data that needs to be processed quickly and efficiently. This can be data about users, their behavior, KPI values, number of sales, seasonal trends, feedback from customers in social networks (in particular, their tone of voice), demand for specific products and services, sales of competitors, and many more information types collected from multiple sources. Doing this manually is difficult and time-consuming, and when speed is critical, marketers must look for tools to help streamline these tasks. This is where machine learning comes in handy.
Specifically, machine learning in digital marketing provides effective business decision-making based on big data. Taking into account the fact that, formally, the job of a marketer is to form hypotheses, test them, and obtain certain conclusions about their effectiveness based on this, here machine learning can significantly simplify and speed up the implementation of this chain of actions. At the same time, marketing specialists get a deeper understanding of unobvious trends and hypotheses for company growth. After all, for example, there can be just a couple ideas or several dozen ideas for marketing campaigns, and to check the effectiveness of each of them by manually analyzing the data, a marketer may not succeed within the allowable time frame.
Therefore, with properly implemented machine learning, the marketing department of the company spends less effort on routine work related to the analysis of large amounts of data while improving, not reducing, the quality of the results of this work.
If we talk about the initial stages of marketing activities, companies usually use ready-made ML-based tools. As for the typical tools based on machine learning, you may be familiar with Google Analytics, Semrush, Unmetric, Grammarly, and others. At a certain stage of their growth, they need to resort to implementing custom solutions tailored to their unique business processes and needs. Thus, they manage to achieve the maximum level of automation and top results in implementation of digital marketing tactics, which is often unattainable in the case of standardized ML-based solutions. This is exactly where NIX can support you by developing custom ML products adapted to your individual business needs. Below, we’ll talk in detail about how to use machine learning for marketing to help your business make a huge leap forward.
Let’s talk about the benefits in more detail so that you understand whether it’s worth using machine learning for marketers.
Nevertheless, machine learning marketing automation can bring invaluable benefits by reducing the number of repetitive and monotonous tasks, for example, collection of analytical data, sending personalized messages, SMM, setting up advertising campaigns, etc., and thereby freeing up their time for core and more specific activities to perform, which computers still cannot master. Also, because a large amount of data is processed very quickly, marketing automation significantly reduces the number of errors that often occur due to the human factor.
Personalizing the approach to customers always causes improvements in the quality of service. Indeed, machine learning allows companies to create unique customer experiences that generate loyal customers and, as a result, boost sales and increase customer lifetime value. You also will be able to spend less money and time on acquiring new customers, as well as retaining existing ones.
Not understanding your target audience often leads to poor results, even with the most well-designed marketing campaigns. The fact is that, for some types of business—in particular, those that provide a large range of goods or services—it can be a difficult task that requires detailed processing and analysis of large amounts of data (sometimes from disparate sources). In turn, machine learning allows you to analyze potential customers quickly, identifying patterns that can be difficult and time-consuming for real people to notice. Thus, marketing campaigns become tailored to specific customers, which increases their effectiveness.
Nowadays, the quality of content is determined not only by the experience of the specialists who create it but also by analytical data obtained by collecting information about what has already worked or hasn’t worked for specific groups of customers. And this is where machine learning can be extremely effective.
Indeed, instead of spending dozens of hours analyzing previous marketing campaigns, marketing departments get the opportunity to focus their efforts on optimizing what brought the best results and thus ensure stable and predictable growth for companies. At the same time, thanks to machine learning, marketers gain a more comprehensive understanding of what will be of interest to their target audience. So, based on this information, they get the opportunity to improve the quality of content by choosing more relevant products and topics that will be useful to customers.
And finally, let’s talk about forecasting, which is one of the biggest strengths of machine learning in marketing. Indeed, the analysis of large amounts of data from different sources, whether it’s user activity on competitor websites, the results of previous marketing campaigns, or, for example, interactions with customers at offline points of sale that need to be compared with online sales—machine learning can help you with this. Based on this data, ML-based tools create the most accurate forecasts of business metrics, including customer demand, purchases, lifetime value, and more, making it much easier to plan your actions.
The first thing to start with the list of benefits of machine learning in marketing is the prospects for comprehensive cost reduction, from speeding up work processes in the marketing department to choosing more effective marketing strategies based on data-driven decisions. You can also gain a more holistic understanding of your target audience’s wishes and needs to make your new offerings to potential customers more personalized and with fewer rejections.
Don’t forget about automation, which in almost any business niche can speed up the execution of work tasks that employees previously had to perform manually. Taken together, these improvements reduce the number of actions performed incorrectly or ineffectively, increasing the return on investment and positively affecting the costs of creating and implementing marketing strategies.
Now, a few words about the dilemma of choosing between ready-made and custom products based on machine learning. Due to their cost efficiency in the short term, ready-made tools are good in the initial stages of business formation. But when it comes to business scaling and more specific business needs, here it makes sense to think about developing custom solutions, as ready-made ones become difficult to integrate into processes with increased complexity in usage, and low flexibility regarding your business needs.
Custom development of ML solutions for marketing activities enables achievement of end-to-end automation, and the limitless possibilities of predicting user intentions and desires provide an excellent basis not only for saving existing resources but also for increasing the overall profitability of the business. Therefore, having an experienced development team at your disposal, a custom product created by them can quickly take your business to new heights. If you’re interested in collaborating with such a team, or just want to consult on the need for incorporating machine learning into your marketing processes, please, contact us.
The main goal of implementing machine learning in any business industry is to quickly and efficiently obtain data that can later be used to improve the effectiveness of business strategies. However, in reality, this advantage is very abstract and doesn’t fully describe the full potential that this technology can bring to companies. That’s why we decided to compile a list of the most relevant uses of machine learning in marketing.
Analytics in marketing plays a leading role: it can be useful both in evaluating the effectiveness of previously taken measures to promote the company’s goods and/or services and in drawing up the necessary conclusions based on raw data about an audience that doesn’t yet interact with a specific brand.
On the other hand, given the enormous amount of data that has to be processed, doing it manually can be very time-consuming and inefficient. As for tools based on machine learning in marketing, they can greatly simplify these tasks, allowing marketers to identify even non-obvious patterns and helping make decisions that don’t lie on the surface.
With an overabundance of goods and services, it’s important for companies to offer their customers – potential and real – something more than just a high-quality assortment. Indeed, as advertising in the media space appears more and more often and competition between brands gets tougher, it becomes more challenging to hook potential buyers, which results in marketing costs increasing. Machine learning in marketing helps brands reduce these expenses by providing up-to-date data on the demographics and behavioral patterns of individual audience segments, making advertising campaigns more personalized and tailored to the needs of a particular customer.
As for machine learning in digital marketing, tools based on this technology help marketers quickly identify the needs and pain points of their audience, which results in the creation of customer-centric content that embodies exactly what these customers want to receive. Moreover, such content can be generated by special ML-driven bots, eliminating the need for businesses to resort to expensive copywriting services and speeding up the process of filling websites.
Demand for some goods and services is significantly affected by the weather conditions that surround potential customers, and this unobvious pattern should be taken into account if you want to increase your sales level.
Some companies use these dependencies to their advantage, and some neglect them,completely in vain. Wherever you are in the adoption stage of this type of marketing, machine learning will help you implement it more reactively and with less overhead.
We’ve already discussed the importance of proper segmentation of customers, and the correct use of data about these customers plays an essential role in the correct implementation of this task. In particular, when this data is too large, and for some categories of customers, it overlaps, it can be quite a difficult challenge to classify them into one category without a thorough analysis. In turn, specialized tools based on machine learning help marketers cope with this challenge in just a couple of clicks.
Email marketing, despite the notion of declining effectiveness due to improved filters in email services, is still actively used by companies to retain existing customers and increase repeat sales.
At the same time, marketers have to personalize the content of email newsletters as much as possible so that the next letter is at least read and, at the most, influences the customer to complete targeted actions for the benefit of the company that sent it. The manual collection of data required for personalization is ineffective due to its large volume. And this is where machine learning for marketers can come to the rescue, as the tools based on it will be able to cope with the tasks assigned to them in a few moments.
Today, smart chatbots—that is, chatbots based on machine learning—are one of the most powerful tools for improving customer service and optimizing the overall effectiveness of marketing efforts. They provide real-life-like interaction of customers with the brand, advising them on certain products or services, providing discounts and offers that will be of most interest to them, etc. The machine learning behind these chatbots helps collect and analyze this historical data very quickly and efficiently without requiring manual intervention from marketers.
A/B testing is a technique used in marketing to compare several versions of a website’s pages or certain marketing artifacts to understand which one works better—whether it’s an email to encourage a customer to make a purchase or a digital advertising banner. Previously, such testing was carried out manually, but today this process can be significantly accelerated thanks to machine learning-based tools.
Search engine optimization (SEO) techniques were previously only done by hand. However, for more detailed data, marketers today can use ML tools to make the results of their actions more accurate and faster, especially when it comes to collecting and analyzing data from multiple sources. For example, machine learning can help with understanding queries (intent), personalization, and voice search. Therefore, marketers can focus more on user experience and provide high-quality content for customers.
Today’s customers spend a lot of time on social media, and these platforms can be effective tools to retain them and generate interest in specific products. In particular, social networks can become one of the main sources for collecting analytical information that helps to identify behavioral patterns and create effective marketing campaigns based on it. Machine learning in digital marketing can easily handle this task.
To get a positive response from potential buyers, contextual advertising should use as much data as possible about their behavior and preferences. From a technical point of view, this information is available to marketers through cookies and other digital indicators, which are extremely difficult to analyze manually. In turn, machine learning for marketing helps extract the right data in the blink of an eye and present it to marketers in a readable form.
The main task of effectively building recommender systems is to compile them from products that are most interesting at a particular moment to a particular user. At the same time, when a company has a customer base of thousands of such users, manual approaches to building these systems cease to work since the only decisive factor—the speed of transforming needs into relevant product selections—is unattainable. To solve this problem, you can effectively use machine learning tools that will do their jobs for the entire customer base in minutes, regardless of size.
Machine learning can be used for lead analysis and classification, behavior analysis, forwarding to the next targeted action, etc. Therefore, instead of worrying about analyzing data about each lead every month, you can devote yourself to more highly-intelligent tasks and leave this chore to machine learning-based tools that do it for you in a matter of seconds.
Now let’s talk about the machine learning marketing use cases implemented by world-famous digital giants who gained the loyalty of millions of customers.
Let’s start our list of machine learning marketing use cases with Netflix. Each of us is probably familiar with this streaming platform. In fact, in addition to launching its own series and feature films, a kind of jump in its popularity occurred due to the introduction of machine learning in marketing analytics to automatically compile media content that will be of interest to a particular user. This personalization was achieved through the analysis of historical data about the user’s previous browsing within the platform. As a result of implementing this feature, the company saves about a billion dollars annually by reducing the churn of users and increasing their retention rates.
A great use case of machine learning adoption is Amazon marketplace, which uses machine learning in marketing analytics wherever possible, from recommendation systems to business metrics analysis. As a result, Amazon’s sales increase every year, which confirms the fact that Jeff Bezos is at the top of the lists of the richest people in the world.
As the next use case, Pinterest has long used machine learning for marketing to tailor its email marketing campaigns to specific users based on their previous activity on the platform. Also, machine learning serves Pinterest as a smart tool for planning optimal mailing times, allowing the company to achieve the maximum rate of completion of targeted actions by members of the platform.
Unlike many other digital platforms, Spotify uses machine learning for marketing analytics to prevent its users from unsubscribing by identifying appropriate behavioral patterns that lead to this. As a result, depending on the pattern, the platform takes a number of actions (these may be offers of subscription discounts or something else) to re-attract users. Thus, the company manages to maintain consistently high user subscription rates, which helps increase its profits continually.
Airbnb also decided to bet on personalized recommendations based on machine learning and greatly benefited from it. Using insights from users’ previous search history, Airbnb’s smart algorithms generate new offers that are most likely to be valuable to these users. Thus, the personalization of offers has helped the company increase its profits often.
As you can see, machine learning for marketing analytics is invaluable in solving the strategic and tactical problems that marketers are engaged in, doing it with increased speed and accuracy, which was unattainable before. Along with the complexity of implementing custom ML solutions due to the need to quickly collect, correctly segment, and instantly import disparate data into actionable insights, they can bring companies a new level of income.
If you’re interested in implementing machine learning for marketers in your company, feel free to contact us. We’ll provide you with the best experts who will deeply dive into your business processes and make them more efficient than ever through end-to-end machine learning marketing automation and, in particular, development of custom ML-based solutions. You can also learn more about our use cases in machine learning development
The core advantages of machine learning in marketing are:
Most often, machine learning in marketing is used to analyze data about customers, their behavior, the results of marketing campaigns, changes in various marketing KPIs, etc.
Machine learning for marketing improves the accuracy of marketing predictions by finding patterns in data and making better predictions. Specifically, it can help with demand forecasting, customer purchase predictions, lifetime value estimation, and so on.
Machine learning for marketers can be used to find specific patterns in user activities. This helps them correctly predict future behavior of these users and quickly optimize their commercial offers.
Julia is an experienced IT consultant who helps both large organizations and dynamic startups across domains create software products that meet their specific needs and rise to emerging challenges. She is committed to providing effective solutions that speed up and improve our clients' business processes in the best possible way.
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