The data quantitatively reflects the history of a company. Predictive models are the answer for extracting knowledge from the past and using it to better address business decisions in the present.
The predictive model
This term refers to the use of data, statistical algorithms and machine learning techniques to identify the probability of future results based on historical data.
Statistics, as well as Machine Learning and Artificial Intelligence, develops models to build forecasts, but also has as its objective the quantification of the uncertainty of the forecast, which derives from the variability of the phenomenon under analysis. These models are becoming increasingly complex as, since the Big Data Revolution began, the data that drives them is increasingly complex, both in terms of volume and in its nature and dimensionality; no longer just numbers or vectors of numbers, but also images, signs and layouts, texts. Once it has been possible to describe and model the domain of the variables involved, we move on to the forecasting part, the heart of these models because it is able to unleash all the potential of the various information already in the hands of the companies.
While predictive analytics has been around for decades now, it's a more current technology than ever; More and more organizations are using predictive analytics to increase their profits and gain competitive advantage. Why right now?
- Volumes and types of data are constantly growing, as is the interest of companies in using data to obtain valuable information.
- Faster and more accessible computers.
- Easier to use software.
- More complex economic conditions and the need to differentiate yourself from the competition.
How to generate reliable predictive models?
Predictive models find numerous applications in different fields, but to build a reliable model it is necessary to adapt it to the context in which it operates through some fundamental steps: data collection, data processing and algorithm calibration.
All these steps are of enormous importance, starting with data collection, which starts with the choice of the basis of the information to be processed. A step that requires both domain expertise and technical expertise to eliminate superfluous data or integrate the chosen set with additional data.
We then move on to data processing which usually takes up most of the time in the development of a predictive model and requires strong technical expertise to clean up the data and build the variables that will be exploited by the model for predicting future scenarios.
Finally, there is the calibration of the algorithms where attention will be paid to choosing the optimal parameters to obtain the best performance in terms of model accuracy and reliability.
Who Uses Predictive Models?
Any market sector can leverage predictive analytics to reduce risk, streamline operations, and increase revenue. Here are some examples:
Banks and Financial Services, Marketing, Retail, Public Administration, Healthcare, Energy, Manufacturing.
In the energy sector, a company like Edison, in light of the new economic and regulatory scenarios (e.g. introduction of the balancing market), has for example decided to implement new and more effective predictive models for controlling the balancing between demand and supply of natural gas, especially in the short term. These innovative forecasting tools have allowed him not only to improve demand forecasts but also to estimate their variability in order to then evaluate risks associated with supply management or economic penalties linked to shortage phenomena.
Why use predictive analytics?
Companies are adopting predictive analytics to solve difficult problems and discover new opportunities. Among the most common uses are the following:
- Fraud detection: The combination of multiple analysis methods can improve the detection of patterns ("patterns") and prevent criminal behavior. Given the growing concern for cybersecurity, high-performance behavioral analysis allows real-time examination of all the actions taking place on a network to identify anomalies that could indicate fraud, security holes and advanced persistent threats.
- Optimization of marketing campaigns: Predictive analytics are used to determine customer responses or purchases, as well as to drive cross-selling opportunities. Predictive models help companies attract, retain and grow their most profitable customers.
- Improved operations: Many companies use predictive models to predict inventory and manage resources. Airlines use predictive analytics to determine ticket prices. Hotels try to predict the number of guests for a given night in order to maximize occupancy rate and increase revenue. Predictive analytics enables organizations to run more efficiently.
- Risk reduction: Credit scores, which are used to assess the likelihood of a buyer defaulting, are a well-known example of predictive analytics. The "credit score" is essentially a number generated by a predictive model that incorporates all the data relating to a person's creditworthiness. Other uses related to risks include insurance claims and collections.
The goal is to go beyond understanding what happened to arrive at a better assessment of what will happen in the future.
E-Business Consulting, a marketing agency since 2003, can help you collect first-party data, call us now and request advice!