Customer targeting using machine learning

Targeting in marketing is a strategy that divides a large market into smaller segments to concentrate on a particular group of customers within that audience.

The analysis of age, gender, interests and others features from clients can be used by companies to target specific customers.

This allows us to design marketing campaigns with a higher conversion rate.

Here we explain how to use machine learning and Neural Designer to build conversion models and optimize marketing campaigns.


  1. Objectives.
  2. Benefits.
  3. Approach.
  4. Results.
  5. Conclusions.


Customer targeting consists of identifying those persons that are more prone to buy a specific product or service.

The selection of customers is not always as evident as dividing them by a single variable such as age or gender. Indeed, the segments are determined by a combination of different variables.

Customer targeting requires analyzing numerous features in specific ways relevant to marketing:


These techniques have accomplished great success in selecting potential clients much better than traditional methods.

Benefits for the customer:


Discover common characteristics between clients to divide them into segments.


Discover the reasons that make that a product fits better one segment than others.

Benefits for the company:


Predict which the product is going to be bought by each client to make more efficient campaigns.


Increase the conversion rate of your marketing campaigns.


The way in order to target customers is to create a model based on features from customers.

Neural networks can model the correct one given these variables and detect which customers are interested in your products and services.

The following graph illustrates a neural network for customer targeting.


As we have explained before, the aim of customer targeting is to increase the conversion rates.

The next example is about a company that wants to sell its products. In the following plot we can observe the positive rates without and with the model.

We can see that without the model only 20% of total customers are interested on the products. However, after applying the model, we have selected the customers that are more likely to be interested and we can see that 40% of them are truly interested.

In case, the company has doubled its sales with the model.


In conclusion, customer targeting enables companies to find the most relevant leads and invest their marketing resources more efficiently.

It is an economical and fast way to manage existing clients and acquire new ones.

Related solutions:

Related examples:

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