Predictive Analytics

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Contents of the article
Predictive analytics is a forecasting technique that uses data analysis methods to identify relationships between changes in past indicators and determine their future behavior. Using this tool allows businesses and investors to adjust the use of resources to take advantage of possible future events. Predictive analytics can be used to reduce risks and improve business efficiency. We will discuss how it works below.

Application areas


Predictive analytics looks at current and historical data patterns to determine the likelihood of them recurring in the future. This analytical tool is used in many areas of human activity.

Budgeting: Businesses use this tool instead of models that are based on assumptions and guesses to improve the quality of financial planning and accurately forecast the company's budgetary needs.
Predicting customer actions. This method also allows companies to obtain effective information about customers by predicting their future actions. This information is used to improve the quality of goods and services. In addition, forecasting can be used when developing incentives for customers.
Reduced Costs: Using predictive analytics allows businesses to conduct effective marketing campaigns, helping to reduce costs and increase campaign profitability.
Forward planning. One of the main areas of application of predictive analytics is forecasting the future success of a new product, especially when historical data is insufficient for forecasting.
Investment risk forecasting. Using this method, businesses or investors evaluate wordpress web design agency he prospects of startups and assets, and determine whether a buyer or business partner matches their interests.
Predictive analysis in medicine helps to establish a patient’s predisposition to various diseases based on lifestyle data.
Predictive analytics is an effective forecasting method, the benefits of which are quite obvious: its use implies a deeper analysis that allows making the right decision, increasing the company's competitiveness, and identifying existing opportunities, risks, and threats to the product. Using predictive analytics, a company gets the opportunity to manage risks more effectively, plan its actions more accurately, anticipate and respond to changes occurring around it in a timely manner.




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The most accurate forecast


Using predictive analytics, you can confidently forecast many events.

Seasonal increase in demand. Seasonality of demand is repeated from year to year and depends on the time of year, temperature, public holidays and other factors. Seasonality begins and ends at approximately the same time, but with slight differences.
Sales growth. Predictive analytics can be used to forecast sales growth in a certain period of time. Based on sales data, for example, for the previous year, and having analyzed it, it is possible to determine the approximate volume of product sales and its growth.
Production indicators. Using predictive analytics in the industrial sector, it is possible to optimize machines and develop technologies based on the collected data, as well as evaluate the future effect of the introduction of new technologies, predict possible defects and distribute the load. Many global companies also use predictive analytics in the energy sector, forecasting demand and the amount of energy that needs to be produced for different consumers.
Operating procedure


Building a forecast based on the principles of predictive analytics consists of the following stages:

Identifying the objectives of the analysis and defining the scope of data that will be required for the analysis.
Data collection. For the most accurate forecast, it is important to use different sources, but to avoid errors in analysis caused by incorrect values, the obtained data must first be transformed into a uniform form.
Analysis using formulas and software tools. There are ready-made solutions for analysis, but if necessary, companies can create or order software for their needs.
Modeling. At this stage, factors that influence the indicators are identified and a predictive model is built. This work is usually performed by artificial intelligence.
The final stage, where the accuracy of the forecast can be assessed. The resulting model is processed using the received data to create a forecast.
Predictive analytics typically consists of three phases: data collection, data analysis, and then modeling the final forecast. Let's look at each of these stages in more detail.

Data collection and analysis


It is recommended to use several data sources to collect information. Personalized methods are used for each business. However, the basic data required to collect information is as follows:

Quantitative indicators. For example, data on the number of attracted clients and sales volumes.
External factors. Data on the level of competition of the company, as well as the economic and political situation in the country and the world.
Internal factors. Number and workload of personnel and qualifications of employees.
Economic indicators. The volume of income and expenses in the past, the profitability of the company.
The age of clients, their level of income and needs, as well as other situational parameters.
Analysis is about finding new information and identifying factors that are necessary to make rational decisions from structured data. Effective analysis requires having as much information as possible to make the most objective conclusions.

In particular, for effective analysis it is necessary to classify information into groups according to certain characteristics: to diagnose the dependence of the final results on the initial data, to determine the connection between events and the time distance between them, and to calculate the total number of deviations from the given norm.

Modeling
This is the final component, which is to create the most accurate forecast. At this stage, having the required amount of collected data, the analyst needs to set a goal and understand what kind of forecast needs to be obtained and for what period of time.

This could be a profit forecast for a year/month or a market demand forecast for a specific month/season. Then you need to choose the forecast model type: statistical or mathematical.

Many programs have been developed to use predictive analytics, each with its own features, level of complexity, and degree of user-friendliness. These tools may differ in their overall functionality: one program is suitable specifically for model development, another for working with data, and a third copes with all tasks, including both model development and data interpretation.