Optimization of marketing expenses in the bank

Aim

To optimize marketing expenses while maintaining efficiency.

Context:

The bank from the TOP-10 in Russia, against the background of lagging advertising efficiency from growing marketing budgets, applied for a solution based on data analysis and geo-analytics. Prior to the implementation of the solution, advertising costs were evenly distributed among all advertising activities, and the costs did not lead to a noticeable increase in the number of new customers.

Solution

To reduce marketing costs and increase its effectiveness, the Marketing Logic team retrospectively analyzed all types of advertising placements in the bank over the past 3 years. All data was digitized, supplemented with financial results in the context of each department and type of product.

We have implemented the Atlas business-analytical geoinformation system in the bank. It allowed us to manage all marketing activities in the regions of presence, including automated calculation of the results of attraction for each carrier in each city. In addition, we have implemented support for the "marketing capture of territory" technology in the bank, that is, the allocation of priority zones around the branch network, where it is necessary to strengthen marketing investments due to external and / or internal factors.

In cooperation with the bank's experts, the most detailed digital portraits of clients were compiled. Based on them, we have prepared an interactive map with the allocation of clusters with a high concentration of "good" customers of the bank, both for advertising purposes and for the potential transformation of the office network. Prepared and trained on the entire set of data, the neural network itself offers marketers the optimal set of the most effective promotion channels, including outdoor advertising, the Internet, radio and television. At the same time, neural network takes into account all possible factors that affect the effectiveness of advertising – including those that a person usually does not include in the calculation due to large amounts of information. The system accumulates data on the history of advertising placements and analyzes them using machine learning methods, which further increases the accuracy of each subsequent forecast.

Result

As a result of using the geomarketing approach, big data analysis and machine learning, the bank's team and Marketing Logic have significantly increased the effectiveness of attracting customers, taking into account the individual characteristics of each region and individual office. Across the bank, the cost of attracting new customers decreased by 40%. The Bank saved 27% of the budget on marketing, online promotion and reach advertising, which in absolute numbers amounted to more than 300 million rubles a year at a cost of 10 million rubles.