Increase ROI on-line campaign
The way to improve campaign effectiveness in the online environment is through user segmentation based on the data analysis on their prior behavior on the web. The segmentation of users is possible using newly obtained data to continuously improve and ideally to create a personal profile for each individual user. Improved targeting campaigns lead to increased effectiveness by up to 200%.
Segmentation of customers
Allocation of customers into groups according to their previous behavior on the web, social networks and other parameters is essential to our quality targeting campaign as well as communication in an online environment. Segmentation is also the first step for the personalization of product offerings, but also has other applications:
- Personalized newsletters with offers according to segment or user profile.
- Identification of credit-worthy clients.
- Detection and prevention of fraud when purchasing and making returns.
Segmentation of users is only the first step to improve the targeting campaigns. In addition to user behavior, it is necessary to analyze the product portfolio and use machine learning to develop an algorithm that will determine the displaying of individual advertisements. Therefore, targeting digital campaigns requires the following steps:
- Segmentation of users
- Analysis of the products and their similarities - the analysis of text descriptions, images, and other parameters, whose output is a virtual map of the relationships between products.
- Product and user segments - finding correlations (significant relationships) between the shopping behavior of users and products (how popular specific types or groups of goods are with different segments of users, the ratio between the total amount of their portrayal in advertising and real sales etc.)
- Machine learning and preparation of the control algorithm - based on patterns found in data analysis algorithm learns to decide what ad to display at what time and on which user sites.
- Deployment of the algorithm and its continuous updating - the algorithm is constantly updated on the basis of data on actual user behavior and product portfolio. It adapts to the real time environment in which it operates.