A land transportation company, had significant losses in the revenues collected at the time of centralization of profits from each of its drivers in its different fleets. Additionally, it did not have optimal control of its fleets.
A management and monitoring solution was generated for its units. With this, the company has a "real time" management and monitoring where it can view at all times (GPS, unit battery level, distance in km, route tracking, other KPI's), additionally with the use of Some sensors were obtained to record the passengers who get on and off the unit, thanks to this data predictive analysis techniques were incorporated to estimate the amount that each driver has to be reporting.
A retail store had all the purchase transactions that their customers made day by day (Amount of purchase, Date, Sku's acquired, Method of payment...). The problem of this company was the ignorance of the exploitation of the data, since they only had a descriptive use..
A customized CRM solution was developed, that is to say, a value was given to each group of customers according to their purchasing behavior. In this way, a recommendation model was generated to promote cross-selling, and finally, demographic information was collected from the customer in order to have a clearer profile of each group of customers and at the same time determine the product basket of each one of these groups.
A telecommunications company had a big problem in the part of the retention of its clients, since it was increasing considerably and at the moment of doing the retention management they granted enough benefits to avoid the abandonment of the client, causing this major financial loss for the company.
A Churn solution was developed that helps them to know the probability of abandonment of each of their clients in advance, so they focus only on those who are really willing to leave the company, thus reducing the expenditure of resources allocated to this area. On the other hand, the profile of the customers with the highest probability of abandonment was identified in order to create a product or retention campaign more in line with this type of customer.
One lending organization was concerned about the increase in its past-due portfolio, since they were about to expand their service and the loans they granted were made under the supervision and in-depth investigation of each of their clients, so this was very manual and time-consuming.
A scoring solution was generated which allowed them to have a considerable level of risk acceptance to keep their overdue portfolio in a healthy percentage and at the same time provide an automatic, reliable and fast service for their clients.
A company was selling its products at a general level, that is, without considering geographical location, socioeconomic level of the population, elasticity of demand...
Therefore, it had an area of opportunity at the time of these sales.
A MarkDown solution was generated, which generates a proposal for discounts by geographical location of their stores, thus enhancing the depth of the reduction of the articles, favoring the desired business logic, either give a higher sales speed to their products or take care of the profit margin.