In this section
The use of Machine Learning techniques for segmentation allows us to understand the different types of customers a company has, facilitating the development of commercial and marketing strategies more focused on the specific needs or interests of the campaign.
Objectives
- Develop personalized strategies for each customer group
- Increase precision by studying a large number of variables
- Facilitate the work of marketing departments
Developing personalized marketing strategies is a task that requires significant effort from the departments involved, as well as in-depth knowledge of the target customer and the products they are interested in. Marketing strategies will be better received the more they are focused on the customer’s needs and interests. At LISDATA, we aim to provide greater customer insight through the use of Artificial Intelligence, applying clustering techniques that will allow for better development of future strategies.
What we do
We work by applying Machine Learning techniques, such as clustering algorithms (Agglomerative Clustering, K-Means, etc.), to the various relevant variables in the sector under study. We use different data sources to select the variables to be included in the solution. We provide a tool that displays the different groups into which customers can be divided, as well as the variables that define them. We also extract a series of previously defined KPIs for each customer group, which allow us to focus a specific strategy for each group determined by the clustering.
How we do it
Selection and Analysis of Variables
Approximating customer behavior, understanding data quality, and being able to more accurately select the variables on which clustering will be performed. This dataset will undergo an ETL process so that it can be understood by the algorithm.
Clustering Algorithm
We will apply Machine Learning techniques to obtain customer segmentation based on previously selected variables, adapting to the specific needs of each group and the optimal number of segments for the data.
Obtaining Characteristics
A statistical analysis of the variables within the clusters will allow us to understand their characteristics, as well as the most decisive variables for segmentation. We will also extract the most relevant KPIs for developing a marketing strategy.
Deployment and Visualization
All information regarding customers, products, and external variables will be stored in a Data Warehouse. We will enable a dashboard that allows for intuitive data visualization for Data-Driven decision-making throughout the marketing campaign.