Peacock-Travel (Big Data Projects)

2019: Development of an analytical module for the business management system

The analytical company Marketing Logic has completed a project to develop an analytical module for the Peacock-Travel business travel management system. Machine learning saves up to 40% of the cost of business travel.

The travel management system automates the key processes in travel organization: selection and ordering of tickets, booking of accommodation, organization of meetings and complex events. The system allows you to establish and use the most profitable algorithms and rules for selecting dates, time, mode of transport, departure and destination points for employees whose activities are related to work in various cities.

When integrating with the business processes of the client company, artificial intelligence is trained on the most effective scenarios for using working time and makes recommendations on the advisability of sending an employee on a business trip, depending on his work profile, travel costs and achieved results. The geologics principles that form the basis of the module allow the system to optimally calculate combinations of home and guest cities and build routes for each employee, taking into account the maximum efficiency for the business. As a result, the organization's expenses for business trips are reduced to 40% without reducing results.

In any of the classic tasks of marketing, sales growth, network management, corporations are faced with the tasks of optimizing the movement of employees, limiting the feasibility of travel. The use of machine learning allows you to more accurately define travel policy rules, reduce travel costs without loss in the company's business indicators: sales, market share and level of coverage. We plan to combine the developed system with the organization's travel plan, which will completely avoid human participation in choosing and ordering business trips and further save by early booking and optimizing travel policy, "said Dmitry Galkin, managing partner of Marketing Logic and expert in the field of Big Data and geomarkets.

Everyone knows that previously reservations are usually more profitable than sudden purchases. Machine learning on various cases of quality made it possible to see how much: when organizing a trip for one week, the cost of travel is on average 30% lower than when organizing for one day, more previously booking saves up to 40%, since the booking prices do not change until the date of actual payment by the client. Analytics, forecast models and clear financial and mathematical planning allow us to organize business trips to become even more profitable for our users, "said Pavlin-Travel CEO Vladimir Pavlin.

In addition to significant budget savings, the travel management system facilitates the exchange of documents and the approval process: invoices and closing documents are directly sent to the company's accounting department, and the travel budget is coordinated with management.