HP INCFollow

Abstract

According to projections from IDC, 80% of worldwide data will be unstructured by 2025. For many large

companies including, its reached the critical mass already. Unstructured data, such as RFPs, creates a

unique challenge for organizations wishing to extract insight and use it to make strategic decisions [1]. We

created a stacked machine learning algorithm based on Natural Language Processing (NLP) to extract

insights from different RFP documents, we named the algorithm CaRRIE for Critical RFPs Requirement

Insight Engine. CaRRIE is able to analyze how similarly-matched an RFP is to different an organization

service offerings thus identifying gaps in it’s current portfolio of service. CaRRIE will help an organization

understands whether its services would fit the request in the RFP documents hence they can align their

current services to the request hence win more deals. CaRRIE was built using a classifier and if we input

any services, we can see a score breakdown for each word and an overall average score for that services.

Higher scores are produced when the services is closely similar to the RFP document and lower scores

means that RFP document requires services that the organization doesn’t have. Having equipped with these

insights, an organization will understand the customer needs better.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 License.

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