Case Study

Business challenge: You invest money, effort, and time to create an offer for your customer, but the customer eventually tells you that he is not interested after all. You call the customer again after a while, with a new promotion and he rejects you again with the sentence “where were you a month ago when I bought a competitive brand?”. How do we know WHAT, WHEN, and HOW to offer our customers, whether it is a basic or an additional offer?
Solution: A data-based approach that allows us to create a unique approach to each of the customers, instead of general campaigns, so that the customer feels valuable. Customers who feel valuable become loyal and they can even become brand ambassadors.
Benefits: Increased efficiency of the marketing budget, customers who feel valuable, reduction of the number of missed opportunities.

Business challenge: No matter how many users or clients we have, each of them deserves attention, in a unique way and to a deserved extent. However, we need to optimize the approach and treat all clients through a limited and balanced number of profiles (segments) that will represent their overall diversity and whose total number will provide space for efficient communication with limited costs.
Solution: By applying advanced algorithms on both demographic and behavioral (user) data, we clearly see the profiles that stand out and at the same time fully cover the entire client portfolio.
Benefits: In this way, we gain insight not only into which clients need a different approach, but also into which clients deserve more attention in general, because they have greater strategic importance. This approach involves not only the development of a segmentation model based on integrated data that gives a complete picture, but also the whole mechanism of simple scoring of users (both new and old), each month, in terms of which segment they will belong to. Each client transition from one segment to another stands out separately. It is important to point out that each client has a segment score, no matter how little information about him we have at the time.

Business challenge: When an unwanted event occurs, we are trying to save as much as possible. This process is not simple at all, because it is necessary to detect the anomaly and eliminate it efficiently in the sea of information that we have.

Solution: The anomaly detection system implies a pre-prepared solution for detection and prevention so that an unwanted event does not occur. To detect anomalies, we use prediction methods, scenario analysis, multivariate analysis, pattern recognition in data series.

Benefits: Reduction of the number of unwanted events, savings by early detection of harmful events, savings by system optimization.

Business challenge: Employee turnover is a problem that we often encounter in modern business. This phenomenon usually causes a large cost to the company because it is necessary to cover the costs of the employee’s departure, the overtime hours of someone who took over his job, the costs of recruiting a new employee, his training, and many other things. On average, a company must invest between four weeks and three months to train and introduce a new employee to the business. It often happens that the same new employee leaves the company in the first year, which represents an additional cost. The situation is even more delicate when it comes to consulting companies and smaller companies, where there is a deterioration in client satisfaction due to frequent changes of consultants or key account managers, with whom the client is used to working.

Solution:  Based on internal data and specialized questionnaires, we create a model that predicts the probability for each employee to change the company.

Benefits: This solution helps the company to predict employee turnover and take proactive measures to prevent employee departures, therefore, saving company funds.