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Customer segmentation using machine learning


By Pablo Martin, Artelnics.

Customer segmentation is the process of dividing a customer base into groups of customers (known as segments) that share similar features in specific ways relevant to marketing, such as age, education, interests and spending habits. It enables companies to find the most appropriate customers for their purposes and invest their resources more beneficially. It is an economical and fast way to increase customers and effectively manage existing ones.

It has been proved in numerous cases that artificial intelligence can select potential clients much better than traditional methods. These operations are very useful in the marketing ambit, so they can determine with considerable accuracy which of these costumers are promising candidates of a specific marketing campaign or an acquisition of insurance.

To carry out this analytical process we use neural networks, computational models that consist on the architecture of the brain. These are complex data management structures whose most important feature is their capacity to learn from data. These techniques have accomplished great success in fields from marketing to engineering.

Touch

DESCRIPTIVE ANALYTICS
Discover common characteristics between clients in order to divide them into segments.
Analyze
DIAGNOSTIC ANALYTICS
Discover the reasons that make that a product fits better one segment than others.
Location
PREDICTIVE ANALYTICS
Predict which the product is going to be bought by each client to make more efficient campaigns.
Benefits
PRESCRIPTIVE ANALYTICS
Increase the conversion rate of your marketing campaigns.

The selection of segments of the customers is not always as obvious as dividing them by a single variable such as age or gender, but the segments are determined by a set of different variables. As a consequence, a good quality of the data and the proper tools to analyze of them must be the first step of the analysis.

Churn prevention picture

Neural Designer is a software that uses neural networks to analyze large datasets in order to develop predictive models according to the characteristics of the customers.

Neural Designer provides the mathematical methods to analyze all the variables of the customers dataset, establish relations between them and design the predictive model. Once the model has been trained, it can be also tested in order to evaluate if it can be used in the production phase, that is, if it allows to improve the conversion rate for future campaigns.

The whole process could be summarize as: collecting the relevant data about customers, analysis of the trends and common characteristics and habits and obtaining a predictive model capable of dividing customers according to their needs.

Activity diagram

Related solutions:

> Risk assessment.
> Churn prevention.
> Sales forecasting.