Predictive Modelling

What is Predictive Modelling?

Predictive modelling uses statistics to predict the final results. Most often, the particular event that one desires to predict is in the future, but predictive modelling can be applied to any unknown event, irrespective of when it will occur.

Why opt for Predictive Modelling with Trugo?

Many companies have discovered that a huge amount of data is left behind by their customers; this data, when obtained from websites and physical stores, can be used in improving a company's performance.

Trugo uses predictive modelling to extract valuable information from this company data, the extracted information is used to develop predictive models that advance company growth, through the efficient delivery of products and services.Trugo’s predictive modelling gives an accurate overview of any question and aids in creating forecasts for our clients. To have a competitive advantage, it is necessary to have an insight into future events and results that challenge key assumptions.

Applications of Predictive Modelling

  • Customer relationship management


Predictive modelling is broadly utilized in data mining and analytical customer relationship management to create client level models that portray the probability that a client will make a specific move. These activities are typically marketing, customer retention, and sales-related.

  • Uplift modelling


Uplift modelling is a strategy for modelling the change in probability brought about by an activity. Typically, this is a marketing action, for example, a proposal to purchase a product, to utilize a product more, or to re-sign an agreement.

  • Health care


Predictive modelling helps to identify patients at high risk of readmission. Initially, the hospitals zeroed in on patients with congestive cardiovascular failure, but now the program has been expanded to include patients with pneumonia, diabetes, and intense myocardial infarction.

  • Algorithmic trading


Predictive modelling in trading is a modelling process cycle wherein the likelihood of a result is predicted by using predictor variables. Predictive models can be built for different assets like currencies, stocks, commodities, and futures. Predictive modelling is widely used by trading firms to trade and devise strategies. It uses advanced mathematical programmes to evaluate indicators on open interest, price, and other historical information to find repeatable patterns.

Benefits of Predictive Modelling for Businesses

  • Understand Customer Needs: By leveraging predictive modelling, businesses can get an in-depth and precise picture of who their customers are and what they need. Companies can predict future trends associated with different niches and then develop a product that will help the audience in that niche solve their problems.

  • Mitigate Risk: Predictive modelling reduces the number of business risks by getting insights into the products, like the success of the latest products, acquiring an overview of companies they are dealing with, or assessing the demand for something in the future to detect new opportunities.

  • Cost Reduction: A lower risk accounts in lower costs, through this, companies avoid failures in the future that cause financial losses. Furthermore, by analyzing future trends, they are prepared to take better steps towards performing on an optimal approach and reducing costs.