Machine learning, a form of artificial intelligence, allows computers to analyze and interpret big data to provide accurate predictions. The more data the algorithm gets, the faster the algorithm “learns”. Integrating machine learning into marketing campaigns becomes a tool for finding the exact target audience and increasing engagement.
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Which promotion channels are most profitable for the product in a particular context? How long will the advertising campaign last? At what point is it best to launch it? With these questions the promotion of any product or service begins. Traditionally, such global decisions are calculated manually by advertising and marketing experts, but the use of AI/ML helps optimize this process as well.
Machine learning tools are also effective for media planning i.e. calculating the conversion each selected promotion channel will bring you. The service collects statistics on advertising sources and spending on them. As a result, the machine makes a forecast, on the basis of which it is convenient to build models and make a plan of expenses.
When a marketing campaign is lined up, one of the most subtle and difficult to predict stages begins, which is communication with customers. The scope of AI and machine learning applications in this area is quite extensive. For example, algorithms can “tell” potential customers which product they should choose. Therefore, if a company has enough data, it is better to delegate such work to a machine that can easily analyze thousands of accounts and suggest the most relevant product to a visitor of your website or app.
Summing up, machine learning is an opportunity to replace human labor with machine learning. What are its advantages?
- Much more data is processed in less time. Humans are not physically capable of processing the amount of data that a computer can easily process. Moreover, the data is usually constantly updated, which makes the processing continuous.
- Response time. The machine tests any hypotheses much faster and, once the algorithm is implemented, produces solutions in real-time.
- Marketing processes are automated and do not require constant human intervention. Moreover, the longer the machine works on a particular task, the more successful its solutions become and the higher the conversion is.
- The machine takes into account an incredible number of factors on the basis of which it makes a decision. The parameters it has to rely on can be constantly changed depending on the task.
- The use of machine learning reduces personnel costs and the cost of attracting customers.
- Now machine learning is actively used in online business. For example, chatbots of websites are getting smarter and have more and more conscious dialogues with humans. The robot reacts to a customer’s appearance on the site and analyzes their actions or interacts with other programs. Based on the users’ behavior on the site, the machine offers them necessary information and solutions to their problem.
What can machine learning be used for?
Globally, the technology is most often used for three main tasks:
- It is the process of assigning an object to a particular class based on its characteristics (the simplest example – assigning according to gender);
- It is determination of the value of an object parameter on the basis of available data (forecasting demand for a certain product, rise or fall of prices);
- It is a search for independent groups (clusters) and their characteristics in the entire set of analyzed data (for example, the division of letters in an email on the subjects: “work”, “study”, “personal”, “spam” and so on).
So, how can classification algorithms help us? First, they are an important tool for creating look-alike segments.
Whereas a decade ago we only knew the social and demographic profile of our consumers, today we have data about how they spend their leisure time, where they go shopping, what they buy, and how much they pay for it. And it is machine learning that helps us connect that data and use it to improve the effectiveness of advertising.
Stages of Implementing Machine Learning in Business
The introduction of machine learning technologies into a particular business follows two scenarios.
The first scenario is the introduction of the algorithms that have already been tried many times, optimized, and have shown good results. For each customer’s problem, we already have ready-made solutions with a certain percentage of accuracy and success. Each model has its own probability of performing the task, so that even the standard scheme of actions is suitable for improving the results.
The second scenario is a more flexible and elaborated algorithm, which a machine must follow to solve the tasks of a particular client. This scenario involves not just a set of tools that have proven themselves in practice with other customers, but a deep and detailed elaboration of the model for a particular client and its tasks.
Forecast of machine learning development
Technological progress is happening at a much faster rate than before. If technology reduces time, labor, and money costs, why not use it? Five years from now, the widespread deployment of machine learning will seem as much the norm as it is now for us to use farm machinery instead of animals.
The fast rhythm of life requires more responsiveness and flexibility from businesses, and the responsiveness of a machine whose algorithms can be adjusted at any time is very quick.
The advantage of using machine learning is obvious. If you have already collected some data, why not analyze it with the machine and start applying it wisely and successfully.