Predictive models utilize Big Data technology to predict customer behaviour and to find optimal solutions. Predictive analytics is based on mathematics, statistical methods, data mining, and data intelligence. It matches current facts with retro data to justify predictions and future forecasts. Predictive business models use patterns developed using data over a period of time to assess potential risks and opportunities. Models identify connections among various factors to search for the best solution within a context. The result of a model is the correct and most business-effective solution. Predictive models can be used to predict prospective customers' behaviour, identify the most popular products and services, and understand what drives customers when they leave and avoid it, etc. Knowledge of customer behaviour helps to significantly improve the bottom line and to stay ahead of the competition in the long run.
How to implement and use examples of Predictive Analytics:
Customer lifetime value (LTV)
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Increasing customer LTV (how much income a customer will generate for your business);
Data-driven customer profiling;
Loyal client management strategies;
Behaviour analytics, developing recommendations, modelling marketing communications, generating demand, defining the most effective strategies, and improving model quality.
Media planning
The success of any advertising campaign is determined by how accurately the target audience can be chosen, the timing and media of the message, and the relevance of the product offered. To increase the accuracy level, a business needs to know where and how to address the target audience. We solve this through analysis of target group concentrations within a population and traffic. The audience is described based on sociological, psychological, and demographic features. Traffic is described by density, speed, time, and quality. The employment of predictive models allows businesses, media, and advertising agencies to increase the efficiency of marketing expenses and improve marketing communications.
Predictive modelling for retail financial services
Financial services markets (banks, insurance companies, microfinance, collectors) are oversaturated with players, and the competition for our clients is huge. All tasks are ultimately classical: increasing acquisition efficiency, developing and retaining customers, processing optimization, and allocating resources correctly. Internal and external data are used to solve all of these tasks. The quality of conclusions and precise actions defines the winners in a competitive fight for the client’s budget. This is what must be made data-driven: b2c business, chain management, campaign management, and marketing. Predictive analytics describes and scales insights, searches for transparent optimal solutions and growth points, and saves resources.
