How Machine Learning Works in Marketing


A large proportion of the population still considers advertising to be a combination of business and art, but the advertising industry is no longer the exclusive realm of creative experts. As you may be aware, the media and entertainment industry was one of the first industries to be affected by the digital revolution. They are now able to promote everything, from high-tech stuff like Tesla cars too much simpler things like thesis writing assistance. As a result, new technology and innovations are transforming the advertising sector in profound ways.

Why You Can Use Machine Learning

Around the world, technology is most frequently utilized to accomplish three major tasks:

  1. classification — connecting a given class of things with certain attributes (the most basic example is men and women);
  2. regression — using publicly available information to deduce an object’s properties; for example, forecasting how much interest there will be in a certain product.
  3. clustering — the search for independent groups (clusters) and the features of these groupings in the complete set of examined data is carried out (for example, splitting messages in e-mail by subject, such as “work,” “study,” “personal,” “spam,” and so on).

When it comes to marketing, the first two factors are usually taken into consideration. So, how may categorization algorithms be beneficial to us in the future? Furthermore, it is a critical component in the fabrication of pieces that seem identical to one another.

With the advancement of technology, we now know more about our clients’ interests, where they shop, what they buy, and how much money they spend on their purchases compared to a decade ago. We must rely on machine learning to integrate all of this information together to make greater use of it.

Just to give you an idea: we have a small group of clients who make frequent purchases of nutritional supplements. Also, of note, is that we know exactly what these individuals do on the Internet, including the websites they visit, the activities they engage in, and the themes that they are interested in. Persons who participate in similar online conduct, according to the data we have (which has been anonymized, of course), may also be inclined to purchase vitamins based on the information we have gathered.

Hand-searching for these individuals would take an inordinate amount of time, but the algorithm can discover them hundreds of times faster. Our company now has the opportunity to expand our market sector by developing a look-alike product.

Algorithms of this type might potentially be beneficial in the identification of fraud. A set of criteria determines whether or whether your online ad was displayed to a human person or an automated computer, and the algorithm determines which option was selected. Whenever it comes to forecasting coverage, regression approaches are absolutely critical to success. The reach of a future campaign may be predicted by training a model that takes into account characteristics such as the budget amount, the distribution channels used, and the seasonality of the campaign.

How To Choose The Algorithm

Are there any well-known marketing-specific algorithms that you can recommend?

Of course, algorithms that are more popular among data scientists than others are necessary for any self-respecting data scientist to have in his or her arsenal (for example, logistic regression or “random forest»). These preferences aren’t restricted to a single industry; rather, the algorithm is selected in response to a collection of tasks that have been specified. Additional variables include the size of a dataset and the length of time it takes to develop an algorithm from start to finish.

Time is a legitimate consideration: when it comes to putting our ideas into action, we tend to choose the shortest path possible to get things done.

Every aspect of the problem becomes more interesting once you have a sample: you can design a sophisticated neural network to handle a particular problem, but if the initial data is sparse, retraining will occur very quickly, resulting in the algorithm using features that are irrelevant to the actual problem.

What Results To Expect

How effective are advertising techniques when machine learning is included in their design?

For example, one of the most ambitious aims specialists have set for themselves is to develop a single measurement system that can follow a single person’s activity across a variety of media such as television, the internet, radio, and billboards. The routes are as follows. Already, they have the capability of creating unified advertising campaigns for both television and the internet – and, of course, they owe a great deal to machine learning algorithms for this capability.

What exactly does it provide them? The ability to contact the same person in different media and precisely where he will be interested allows you to reduce the cost of contact while simultaneously increasing the effectiveness of advertising – and this is not to mention the ability to include information about purchases here (as in the case of vitamins above), which also has a positive effect on the outcome.

Examples Of Ml Usage

1. Recommender system

The core function of a recommender system is to present the user with a product in which he is most interested at the time of the recommendation.

Predictions made by the algorithm include: N things that are most likely to be purchased.

How this data is used: email and push mailings, “Recommended Products” and “Similar Products” blocks on the site, and “Recommended Products” and “Similar Products” blocks on the site. In turn, consumers receive individualized offers, increasing their probability of completing a purchase as a result of doing so.

2. Predictive targeting

All targeting methods have a similar goal in terms of the essence: to spend the budget on users who fall into the target audience while avoiding spending on those who do not fall into the target audience.

The following are the most often utilized types of targeting:

  • Segment targeting is the practice of presenting advertisements to groups of consumers that share a common set of characteristics.
  • Trigger targeting is the practice of presenting advertisements to consumers after they have completed a specific activity (for example, viewing a product, adding an item to the cart).

Predictive targeting is another technique, which involves delivering advertisements to people depending on the possibility that they would make a purchase.

The primary distinction between predictive targeting and the other forms of targeting is that predictive targeting employs all conceivable combinations of tens or hundreds of user attributes with all possible values, whereas other types of targeting do not. In addition, in other forms of targeting, a restricted set of parameters with certain value ranges are employed to achieve the desired result.

When it comes to purchasing in N days, the algorithm forecasts the likelihood that the user will do so.

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