Oferta

Predictive Modelling

Added on:
Author:
17 November 2020
robert

Anticipate the “future” based on data

Predictive models, built by the experts of Data Science Logic, allow prediction of future consumer reactions and events, based on historical data. Advanced analytical algorithms discover patterns contained in a huge number of examples, counted in hundreds of thousands or even millions.

The prediction model built in this way can be used for consumer life style forecasting.

In which areas can predictive modelling be used?

Our models predict what kind of offer is most suitable for a particular consumer. This allows you to communicate with those who are really interested in your offer and invest your communication budget in areas where they will bring the greatest return. This way you avoid “spamming” consumers for whom the offer is not attractive.

Our predictive models predict the probability of purchase by a specific consumer, influenced by marketing communication. The variables such as communication channel (sms, email, digital advertising), its content and time of sending are taken into account. This allows us to tell you when it should be sent, to whom, how and what should be sent. This translates into measurable savings: there is no need to spend money on text messages that will not translate into sales and it is not worth offering a discount to a consumer who is already convinced to buy anyway.

The predictive models also predict at what stage of the shopping path the consumer is and what is the probability of moving to the next stage, under the influence of a specific stimulus. By combining consumer data from multiple sources, it is possible to precisely control the messages sent to the customer.

The predictive models are the “core” of many solutions built by Data Science Logic. They are used in the processes of distribution network optimisation, sales forecasting, in anti-churn models and in optimising marketing campaigns.

Benefits:

  • Anticipating consumer behaviour in advance
  • Forecasting future phenomena
  • Better decisions based on reliable forecasts
  • Understanding the factors influencing business-relevant phenomena (e.g. customers churn)
  • Introducing changes in the organisation and business processes based on the conclusions of the model
  • Possibility to simulate different future scenarios

Clients: Leading marketer from retail industry