Application and Limitations of Predictive Analysis

Advanced analytics classified into different branches, one is Predictive analysis which deals with perceptions about the future event. By using different techniques like data mining, modeling, statistics, and artificial intelligence, this analysis could produce results about future events. Organizations use this analysis to interpret their future position. It helps the organization to forecast future market trend, risk and opportunities. At early stages, it was only used by banks to check about the customer default but with the passage of time is being used by Companies as well as other departments like health care, police, etc. widely.

Applications of Application and Limitations of Predictive Analysis

Applications are rules and regulations imply a task to get evaluated results. Statistical modeling is used to understand internal and external value of an organization in predictive analysis. This analysis helps the decision makers to take decisions about organization. Application of predictive analysis leads an organization from financial to retail industry. While making decisions, decision makers uses unstructured and structured data derived from different source like customer feedbacks, call centers and relevant websites (Graham, Ferrier and Huettman).

Limitations of Application and Limitations of Predictive Analysis

Predictive analysis apply to detect risk and threats that could be avoid to take wrong  decisions. For example, If we have a glance on a big marketing point like Amazon, there are a lot of other product similar to the item which one you purchase. Many items cover area which is about to sale in next moment but not sold yet, therefore automatically it anticipate the future selling target which helps to locate these items at best location.

Algorithms of Application and Limitations of Predictive Analysis

Predictive analysis also covered some dark side. Data or information gathered from different resources may have some redundancy or may be information could be incomplete which could leads to the wrong forecasting. People usually do not provide proper information or provided information could vary from time to time which also cause a misleading factor (Brohi, Pillai and Kaur).

Predictive Modeling for Customer Behavior and Application and Limitations of Predictive Analysis

As above discussed, organization like Amazon identifies three classes of predictive models:

·         Cluster models - That deals with customer segmentation which includes behavioral cluster, items-based cluster and brand etc.

·         Propensity models - Used for the purpose of true picture of customer behavior.

·         Collaborative recommendations – Recommend different product, services and other variety of commodities.

Qualify and Prioritize Leads

·         Predictive Scoring

·         Identification Models

·         Automated Segmentation

Targeting the Right Customers at Right Time of Application and Limitations of Predictive Analysis  

Target the right customer at right time with right offer and that the most common application used in predictive analytics (Ponder, Carter and Flemons).

References of Application and Limitations of Predictive Analysis

Brohi, Sarfraz Nawaz, et al. "Accuracy Comparison of Machine Learning Algorithms for Predictive Analytics in Higher Education." International Conference for Emerging Technologies in Computing 285 (2019): 254-261.

Graham, C. H., et al. "New developments in museum-based informatics and applications in biodiversity analysis." Trends in ecology & evolution, 19.9 (2004): 497-503.

Ponder, Winston F., et al. "Evaluation of museum collection data for use in biodiversity assessment." Conservation biology 15.3 (2001): 648-657.