Predictive Analytics

Look into the future and discover new opportunities before the competition. Predictive analytics can predict possible variants of the market, identify potential risks and future opportunities. Ask us how to do it.

What is predictive analytics

Predictive analytics is an advanced technique that uses statistics, big data analysis and machine learning to predict the likely options for future development. Reliability and accuracy of forecasts, predictive models, depends on the input data and can concentrate on one or more values.

What it is used for

Predictive models will save you time and unnecessary investments which you would otherwise devote to the preparation and implementation of projects not corresponding to future development of the market or customer behavior.

  • Marketing – optimization of marketing campaigns, product portfolio and pricing (increase ROI and profit).
  • Business and behavioral targeting – displaying products and services based on the analysis of shopping behavior of users leads to a higher number of orders (increase the conversion ratio and turnover).
  • Risk Management – forecast the probability of loan repayments, payment of liabilities, client risk insurance and insured objects.
  • Financial markets – prediction of the likely future value of various assets in order to increase efficiency and reduce investment risk.
  • Fraud detection – of suspicious transactions, claims and non-standard requirements.
  • Identifying new opportunities - the identification of emerging trends, unused market segments (today often niches) and timely warnings of future declining demand.
„Over 80% of insurance companies use predictive analytics to manage risks and increase profitability.“

How we proceed

Meaningful and valuable answers can be obtained if you ask the right and well-formulated questions. Therefore, before we start to work on the development of predictive models, we must accurately describe the task to be solved, examined values, input data and project goals.

Data mining – Gauss Algorithmic

  1. Collecting data – input data can be obtained from internal and external sources with varying availability. Especially with external sources, data acquisition is often associated with complex mining.
  2. Processing and cultivating data – obtained data can take different forms and structures. From a list of emails, via logs from servers to images or posts on social networks. Prior to the analysis, therefore, we must adjust to a machine-processable format.
  3. We analyze the data – we use statistical methods and artificial intelligence to find patterns, correlations and useful information in the data.
  4. Learning from data – using methods such as clustering and machine learning we prepare the algorithms, which on the basis of historical and current data are able to estimate their likely future development.
  5. Creation, testing and deployment of predictive models - based on the learned algorithms we create predictive models, test them in full operation and subsequently put them into practice.

The environment, in which the models are generated is always changing and with it the importance of different data for the correct prediction. Therefore, it is necessary to constantly test, expand, and update sources of data, analysis and predictive models in order to maintain their reliability and accuracy.

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