Predictive analytics (Big Data)

Predictive models utilizes bid data technology to predict the customer behavior and to find optimal decisions. Predictive analytics is based on mathematics, statistical methods, data mining and data intelligence. It matches current facts with retro-data to justify predictions and forecasts of the future. Predictive business models use patterns, developed on the basis of data over required and sufficient and are aimed to evaluate the potentials of risks and opportunities. Models identify connections among various factors to search the best solution within a context. The result of a model is the correct (most business effective) decision. The applications of a predictive model are customers’ propensity to behave, identify best goods and services to sell, understand and minimize the causes of attrition. Knowledge of customer behavior help significantly improve the bottom line and stay ahead of competition in the long-run.

Examples of insights and their implication:

Customer life time value (LTV)

  • Increase of customer LTV (how much income will a customer generate for your business);

  • Data-driven customer profiling;

  • Strategies to work with loyal clients;

  • Behavior analytics, development of recommendations, modelling marketing communications, demand generation, definition of the most effective strategies, improvement of models quality.

Media planning

The success of any marketing activity is defined by the accuracy of targeting the audience, duration and channel of the message, relevance of the appeal. For increase of accuracy level, business needs to know where and how to address the target audience. We solve it through analysis of target group concentrations within the population and the traffic. The audience is described by socio- , psycho- and demographic characteristics. The traffic is described by density and speed, time, quality. Employment of predictive models allows businesses, media and advertising agencies to increase efficiency of marketing expenses and to improve marketing communications.

Predictive modeling for retail financial services

The financial services markets (banks, insurance companies, micro-finance, collectors) is oversaturated by players and the competition for the client is huge. All the tasks are very classical: increase of acquisition efficiency, development and retention of customers, processes optimization, correct allocation of resources. All of these tasks are solved on the basis of internal and external data. The quality of conclusions and precise actions defines winners in competitive fight for the client’s purse. It is a must to make data-driven: b2c business, network management, campaign management and marketing. Predictive analytics describes and scales insights, search transparent optimal solutions and growth points, saves resources.