Machine learning for marketers: how to increase company profits

At the disposal of modern marketers a huge arsenal of digital tools, everything is here: from analytics systems to sophisticated programmatic platforms and various cloud solutions. On the other hand, the amount of data generated by users is growing like an avalanche. Their source is the behavioral factor in the network and the communication of users in the digital world. To navigate this flow of information, marketers need specialized solutions that are able to collect user data, process and present in an easy-to-analyze form. Here, marketers are helped by artificial intelligence and machine learning. Machine learning (ML) is an artificial intelligence subdivision that uses algorithms that can be independently trained, that is, they do not need to be specially programmed for a specific task.

According to the MIT Technology Review, 60% of companies in one form or another use ML in their business.

According to analysts, 2017 was the year of intensive development and implementation in the ML business. This technology is now at the peak of the Gartner technology maturity curve - it means that in the near future ML will develop rapidly, and companies will invest in these technologies. Computerworld magazine has taken the ML main place in the list of the most valuable skills of employees. First of all, marketers are interested in the possibilities for personalized involvement of users, which ML algorithms can provide. They allow you to do three main things:

  1. Work with Big Data and perform advanced audience segmentation;
  2. Conduct predictive analytics of customer behavior;
  3. Make recommendations for adjusting actions in real time.


Netflix, using predictive analytics to improve recommendations for site visitors, involves them in more active use of the service. If you have ever used the site Netflix, then you probably saw there section of the recommended series. All recommendations are provided to site visitors using machine learning algorithms, which analyze your preferences and “understand” which categories of films you like the most. The product recommendation system on e-Bay works in a similar way.

A startup from the United States IdealSeat , uses ML and deep learning to create the most comfortable viewing experience at matches. The service analyzes many parameters that viewers can choose when ordering tickets: you can decide where you want to sit: in the shade or in the sun, in a fan or family area, and so on.

Predictive analytics

Using ML, companies can predict when and why the buyer will contact them. This allows you to personalize communication with customers and plan the cost of maintaining a support service. For example, a company can analyze a person’s musical preferences, form a pattern of his consumer behavior, and calculate the value of his average bill in his stores. In principle, you can even identify what kind of purchases and for what amount Beatles lovers will make, and what average check ABBA fans will have.

Sberbank's experts have already learned to identify and predict patterns of behavior of cardholders. For example, a bank can distinguish between different activities of cardholders and reduce them to three main patterns: car purchases, furniture purchases or repairs, and treatment costs. Depending on this, offer appropriate programs to your customers.

Big Data and flexible pricing

ML technologies optimize prices depending on the quantity of goods, sales trends and other factors. Today, 63% of users expect from the sites of companies, and especially online stores, personalization based on previous actions. As an example, we can recall the personalization mechanisms on

Using Big Data analysis algorithms, marketers can use historical data and statistics to build forecasts. Mobile analytics services, for example, Amazon Mobile Analytic s or Google Cloud Machine Learning, are already successfully using this.

Segmentation and ad targeting

With ML, you can predict conversion depending on external factors and automatically adjust bids in context. Now the development has received training with reinforcements: the initial model is not laid into the contextual advertising system. She begins to interact with the environment and receive feedback. The system adjusts its actions on the basis of its own feedback quality assessments. For example, if contextual advertising is launched in the field of banking products, then this parameter may be the interest to the client of the bank's offer.

Lead Qualifications

ML algorithms can identify prospective users who are ready to purchase with the maximum probability. To this end, sales and marketing specialists must jointly develop their own criteria for assessing the “promising” contact. For example, the algorithm analyzes language patterns, selects words that increase the involvement and growth of clicks. Then you can make a list of trigger words, with which marketers will write advertisements.

The world is changing very quickly, today's consumer is waiting for a unique service, he thinks that everything will happen instantly, personalized and in any convenient channel. ML here is a great help and a powerful tool for marketers. However, in order for everything to work, it is not enough just to buy and install specialized software. To implement an AI or ML in an organization, it is necessary to create new or reformat existing processes: first of all, work on incoming data logistics and develop common standards for their processing in real time.

In fact, there are two large groups of user data: information that companies themselves can collect (average bill, types of purchases, etc.) and information directly from connection channels (interests, age, and other data on the user's social activity). Together, all these data will give a complete portrait of the user and a picture of his preferences. This array of information will be the “food” for ML algorithms.

What to do after the data is collected, processed and analyzed? The second most important stage of working with ML algorithms is to use the obtained forecasts in practice, to draw up detailed maps of who, what, where, when and how they will buy.

Future ML

In theory, ML and AI can become quite self-contained and anthropomorphic entities: turn into Skynet or into heroes from the Black Mirror series. Recall a couple of recent cases with chat bots. First, when the bots developed by the Facebook team, after a brief conversation with each other, invented their own language. The creators who could not decipher this neo-language, decided to urgently close the project. The second case is a little older: a chat bot launched by Microsoft on Twitter eventually became a tough racist. A day after launch, Microsoft removed the most provocative statements of the bot.

However, the real state of affairs shows that domestic business should not be afraid of raging AI. He would learn how to correctly launch contextual campaigns and create user-friendly sites.

ML algorithms and methods of its use in marketing and business, of course, will develop. We have identified the following growth points of these technologies:

Improving the mechanisms for collecting and preparing customer data. Today, one of the main limitations of using ML in business is the low level of data quality. The information is often fragmentary and fragmented: for example, for one group of users age is known, and for another - consumer preferences. Improving data quality means increasing the efficiency of the ML algorithms.

Improving ML efficiency in business. Now using AI is beneficial only to very large companies. According to various estimates, the business efficiency of ML application is increased by only 2-3%. This opens up a lot of room for future joint efforts of marketers and developers.

The development of systems for collecting customer data. Before ML starts with analytics and forecasting, you need to accumulate a large array of information for analysis, and for this it needs to be collected, cleaned and segmented. There is a lot of room for customer data collection systems and their various collaborations.

Does the AI ​​do it all by himself?

No matter how much marketers dream of a super service with a single “Bablo” button, this is unlikely to ever become a reality. Algorithms and calculations will never replace the marketer himself. ML is just a tool, albeit a powerful one, that needs to be properly used. In our foreseeable future, the machine will not be able to understand the client and form his need for a product or service.

The data obtained using AI and ML, ultimately interpreted by a living specialist. His professionalism, ability to correctly identify key variables that affect the result, and determine the final effect of the use of algorithms ML.


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