The main aim of predictive analytics is to go beyond descriptive statistics on what has happened previously to give an estimation of what will happen in the future. The results generated by a predictive model will be used for better decision making and producing new insights.
Predictive models work by using existing data sets to extract information and this is then used to ascertain predict patterns for future outcomes. What one should keep in mind is that predictive analytics does not ‘predict’ the future; it gives us a view or rather a reliable forecast of what to expect with a suitable space for risk assessment.
Why you should to Predictive Analytics?
According to “How Predictive Analytics Helps Cox Communication Tune in to Customers,” (Forbes.com; Jan 2015), a Forbes Insights case study sponsored by SAP, using predictive analytics to gain a deeper insight into consumer trends has proven to be successful for many companies including Cox Communications, the third-largest cable entertainment and broadband provider in the United States. The telecommunications industry is prone to high customer turnover since switching costs are slim to none. Companies operating in this industry are looking for new ways to differentiate themselves from competitors in order to retain customers. Predictive analytics might be a solution that will allow them to better understand and retain customers and acquire new ones more effectively.
As clearly understood from this case study, predictive analytics helps businesses and organizations be closer to ever-changing customer fads. By relying on past information of a popular trend, we can build a model to assess the changes that can possibly take place within a definitive time period.
Top five reasons why companies want to use predictive analytics?
According to a research analysis by The Data Warehouse Institute (TDWI), the top five reasons why companies want to use predictive analytics are to predict trends, understand customers, improve business performance, drive strategic decision-making, and predict behavior.
Limitations of Predictive Analytics
Although predictive analytics can help foresee future risk factors, one should keep in mind that it is not a crystal ball to show an organization everything it should know. There will be many grey areas where it is better to focus on evidence based rather than intuition based data to take a logical decision. The few times predictive analytics can fail are when there is a data fallacy.
- Incomplete data, missing values or lack of a substantial part of the data, could limit its credibility. Make sure you’re looking at a time frame that gives you a complete picture of the natural fluctuations of your data; your data should n’t be limited by seasonality.
- When using data from surveys, bear in mind that people don’t always provide exact information. People may not be dishonest as much as self-conscious, but the data is still skewed.
- Data collected from different sources can vary in quality and format. Data collected from sources such as surveys, e-mails and the company website will have different attributes and structures. Data from various sources may not have much compatibility among data fields. Such data requires major preprocessing before it’s analysis-ready.
- Data collected from multiple sources may have differences in formatting, duplicate records, and inconsistencies across merged data fields. One needs to spend a significant amount of time cleaning such data — and even longer validating its reliability.
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