RFP360’s approach to search optimization

When you’re responding to an RFP or other type of information request, being able to efficiently find the right response to a question is vital. At RFP360, we use a multipronged approach to optimizing search results.

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First, we leverage a natural language processing (NLP) service to give structure to the unstructured. In a sea of text, which words matter most? That’s where NLP can help. 

RFP360’s Knowledge Library leverages NLP capabilities to automatically recognize key phrases in your questions and answers from previous proposals and as you update existing content within the Library.

NLP is only one area of machine learning that can optimize the search experience. We also pay attention to how you are using your content and leverage those insights to help prioritize the responses we present to you. We consider things like:

  • How many times has the response been used to answer a question?

  • How many different proposals has the response been used in?

  • When was the response created, updated and reviewed?

We continue to learn from how you leverage content and use those insights to improve the search experience.

Tags are also an important part of the search experience and can be established for a specific proposal as well as specific question and response pairs captured in the Knowledge Library. 

We all know that content needs structure to deliver the greatest value. Tags allow you to categorize your content beyond the specifics of the question or response. 

You may want to add a tag to denote a market segment, product line, type of customer, division of the company, or any other designation important to your business. Tags help you keep your content organized and find information quickly using search filters.

For more information about search, check out the following help article:

What Boolean Search Operators Are Supported in the Knowledge Library?

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