The objective of this paper is to research, modeling and forecast the call-center workload that depends from all media and marketing activities.Data mining approachand machine learning technologies help to clearly identify and distinguish the impact factors on the feedback of potential customers (both posi-tive and negative), determine whichcommunication channels to use to increase inflow of queries. The model for forecasting of effectiveness of media invest-ments and as a result managing of Return of Marketing Investments (ROMI) based on hourly data for all calls to Call Center, media and marketing indicators and macroeconomic factors for banking sector in Ukraine for the period 2013-2018 years was built. Authors used such machine learning technology as eco-nometric modeling (regression analysis) for key metric “Incoming Calls to the Call Center”. Data Science technologies help to forecast and manage calls flow with average error that is less than 10%. Article describes how to increase the effectiveness of advertising campaign by 8% in thefirst 2 months and achieve potential growth of conversion rate by 58%, compared to the standard market level. This article contains the key stages of implementing data mining approach, directly in the processof machine learning and dwell on the important technical aspects of the implementation of forecasting models.