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Recommendation Engine Helps In Easier Decision Making

By Author: Michel Smith
Total Articles: 21

In this technique one specific area is chosen and data mining is done for all its historic data. Then all useful information is extracted from it which is productive. All the data which is collected is then used to make future predictions which help in committing the same mistake again.
There are many tools which can be used for predictive analysis mechanism which are provided readily by many companies. Some common industries which make use of this technique are – financial domain, marketing, insurance, telecommunication, retail, travel etc. All these industries would try and get all the information and reaction of market of past data and then before launching a new scheme they would consider it.
This helps them in many ways such as – improves customer response, company growth which results in more profit. Predictive analysis is not just done manually or by someone sitting in front of the computer. There is a proper logic applied to the data collected and then a proper way of reading the output. All the past data is collected from various sources and consolidated in one particular location in one format. Then logic is applied on the data and certain output is generated. The output is well studied and useful information used for decisions is extracted. This helps the higher management people in implementing new rules and regulations or launching new schemes or products. For e.g. If a bank wants to launch a new loan scheme then it would gather all the customer responses from past of similar schemes and then make sure that the drawbacks of those schemes are eradicated in the new scheme. This is another term which works along with predictive analysis which is known as recommendation engine. This means that when the data has been well studied, it would try and suggest you with some possible options which would help in achieving your goals.

These options are suggested based on the output values entered in the program and would be based on all the past and current data collected. Recommendation engine also works on explicit and implicit data collection techniques in which data is collected from all sources. Explicit data is more about asking people to give feedbacks and implicit data is more about observing the trend of the product and reactions of the people.

These mechanisms when work together makes the decision making process very simpleand help in taking more productive and useful decisions. The output suggested by the technique would always have a logical reason and data supporting it.

Michel Smith is the author of this article. For further detail about Predictive Analytics and Recommendation Engine please visit the website.

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