Artificial intelligence technologies are used in predictive analyzes, as artificial intelligence techniques have proven to be significantly more independent in decisions in the field of predictive analysis
Predictive analysis are important because they assure companies that their decisions will be made based on actual data and not just assumptions. Therefore, it reduces risks, enhances productivity and efficiency, and reduces costs.
Think that, the possibilities of your business if you were able to use predictive analysis to make data-driven decisions instead of relying on expectations
Sales forecasts, budget planning, knowledge of different risks and opportunities for growth
Improve marketing and distribution content
Used in a wide variety of industries: Business, Banking, Finance, Retail, Telecommunication and Credit Scoring
Lead generation and Identification of growth opportunities, Improved content marketing and Enhanced marketing efforts, targeted to specific customers
Data points variances measured and compared from year to year can reveal seasonal fluctuation patterns that can serve as the basis for future forecasts. This type of information is of particular importance to markets whose products fluctuate seasonally, such as commodities and clothing retail businesses. For retailers, for instance, time-series data may reveal that consumer demand for winter clothes spikes at a distinct period each year, information that would be important in forecasting production and delivery requirements
As a linear model of analysis, the time series method can also be used to identify trends. Data tendencies reporting from time-series charts can be useful to managers when measurements show an increase or decrease in sales for a particular product or good
The time series method is a useful tool to measure both financial and endogenous growth, In contrast with financial growth, endogenous growth is the development that occurs from within an organization's internal human capital that can lead to economic growth. The impact of policy variables, for instance, can be evidenced through time series tests
