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Machine Learning for Marketers: How to Boost Your Company’s Revenue

Nowadays, marketers can benefit from a huge selection of digital tools, from analytics systems to advanced programmatic platforms to cloud solutions. At the same time, the volume of data generated by users during their online behavior and communication is growing like an avalanche. To navigate this flow of information, marketers need clever solutions for collecting, processing, and visualizing user data. This is where artificial intelligence and machine learning technologies come into play. Machine learning (ML) is a part of AI technologies that uses self-learning algorithms. The best thing about such algorithms is that they don’t need to be programmed to perform a specific task.

Machine Learning for Marketers: How to Boost Your Company’s Revenue

According to MIT Technology Review, MI solutions are used by 60% of businesses.  

According to analysts, year 2017 has been marked by a growing use of ML in the business world. This technology is currently at the peak of the Gartner Hype Cycle, meaning that businesses will be heavily investing in ML in the near future. Computerworld put ML on the top of its list with the hottest IT skills. Marketing specialists are mostly interested in what ML solutions can do in terms of personalizing customer engagement. ML algorithms allow to:

  1. Manage Big Data and implement advanced customer segmentation;
  2. Use predictive analytics to forecast customer behavior;
  3. Provide recommendations for adjusting the company’s strategy in real time.

Recommendations

Netflix uses predictive analytics for enhancing their customer recommendations and encouraging users to engage with the service on a deeper level. If you’ve visited the Netflix website, you must have seen the section with recommended TV shows. Did you know that the recommendations you see are generated by machine learning algorithms? By tracking and analyzing your behavior on the website, ML algorithms identify your cinematic preferences. The eBay platform uses the same recommendations system.

IdealSeat, which is an American ticketing platform, is using ML and deep learning for providing the best user experience. When selecting the perfect seats for the game, the user can choose between the sunny side and shadow side, fan zone and family zone, etc.

Predictive analytics

By tapping into ML possibilities, businesses can predict when and why a customer will contact them. This allows companies to personalize customer communication and plan costs for maintaining a customer support team. For example, by analyzing the user’s music preferences, a company can identify their behavioral patterns and calculate the average bill. In fact, it’s not that hard to find out how much money and on which products, say, a Beatles fan will spend!

Sberbank is yet another fine example of a business making a good use of ML solutions. Sberbank employees have learned to identify and predict behavioral patterns of card holders. For example, Sberbank tracks their client expenses and classifies them into three main categories: car purchase, furniture purchase/renovation, and medical treatment. Depending on the category, the bank can propose to the customer the loan program that suits their needs.

Big Data and flexible pricing

ML technologies can adjust prices based on the quantity of products, sales trends, and other factors. Today, 63% of users are expecting that companies — and online stores in particular — personalize their customers experience based on their past behavior. For example, most of us have engaged with the customization mechanisms used by booking.com, a major platform for booking apartments and hotels.

Thanks to algorithms for analyzing Big Data, marketers can use archive data and statistics for making accurate forecasts. This approach is successfully applied by such mobile analytics services as Amazon Mobile Analytics and Google Cloud Machine Learning.

Ad segmentation and targeting

Moreover, ML algorithms allow to predict a conversion rate based on a variety of external factors. Nowadays, an increasing number of context advertising systems aren’t based on a fixed model anymore. The system simply starts to interact with the environment and receives feedback. Then the system adjusts itself based on how it assesses the quality of the feedback. For example, if we take context advertising for banking products, the criterion can be the level of the client’s interest in the banking services offered to them.

Lead qualification

ML algorithms can help businesses detect leads that are likely to covert to paying customers. To be able to use that function, sales specialists and marketers need to work together to develop criteria for determining whether a lead is ready to make a purchase. For example, the algorithm can analyze language patterns and select the words that enhance customer engagement and encourage clicks. Based on that information, the marketing team can create a list of trigger words to be used in advertisements.

The world is changing at a lightning-fast speed. The modern customer is expecting a fast and unique service with a high level of personalization. With that said, businesses aim to meet their customer’s expectations. ML algorithms help companies a lot in achieving this goal. However, purchasing and installing smart software is not enough to make things work. To implement AI or ML solutions, an organization needs to rebuild the existing processes or create new ones. First and foremost, it needs to polish the logistics of inbound data and develop unified standards for processing such data in real time.

User data can be classified into two main groups: the data collected by companies (average bill, types of purchases, etc.) and the data received directly from communication channels (interests, preferences, age, social status, etc.). Putting these data together allows companies to create detailed customer profiles and get to know their audience. ML algorithms later use these data to personalize customer experience.

Okay, the data have been collected, processed and analyzed. What’s next? The second step in applying ML algorithms is to actually use the predictions you’ve made. In other words, you need to create detailed maps saying who will buy which products, when they’ll buy them and how they’ll buy them.

The future of ML

Theoretically, ML and AI technologies have all chances to become autonomous and anthropomorphic “creatures”, such as Skynet or the characters from “Black Mirror” TV series.  Let’s talk about the recent alarming events featuring chat bots. The first case has to do with Facebook’s chat bots that invented their own language. Unable to decipher what the bots were talking about, the developers decided to suspend the project.  The second case features Microsoft’s chat bot that started to post racists comments on Twitter. Just one day after the project launch, Microsoft had to intervene and delete some of the bot’s harshest remarks.

However, this doesn’t mean that businesses need to be afraid of using AI for enhancing their performance. In the next few years, ML algorithms will be getting a strong foothold in marketing and business. Below we outlined the main focus points to pay attention to.

Improving mechanisms for collecting and processing client data. Low data quality is currently the biggest obstacle that stands in the way of using ML technologies in business. The data are usually random and fragmented. For example, let’s take two groups of customers, Group A and Group B. For Group A, the company knows the average customer age but lacks information on customer preferences. For Group B, it’s vice versa: customer preferences are known but there is no data on the average age. By improving data quality, a business can boost the effectiveness of ML algorithms.

Enhancing business performance. Today, only big enterprises benefit from using AI technologies. According to different estimations, ML solutions only give a 2-3% boost to business performance. Both marketers and developers have to work hard to make this number grow and make MI available to small and medium businesses.

Building systems for collecting client data. Before using ML technologies for analyzing data and making predictions, a company first needs to collect, filter, and segment a big bulk of data. This calls for highly functional and intuitive systems for collecting client data.

AI will do all the work?

Although marketers all over the world would like to finally get hands on the service with the only button saying “Money”, this is hardly going to happen. Algorithms and computing system will never be able to replace a marketing specialist. ML technologies are just a tool (although a powerful one) in the hands of a competent marketing team. Currently, there are no technical possibilities for creating a program that would be able to understand the customer and get them interested in a product or service.

The data received by using AI and ML technologies are interpreted by humans, whose expertise and skillset are the main factors determining the success of ML algorithms.