Summary
A global development consulting firm realized that its employees were responding to requests for proposals (RFPs) without referencing previous relevant proposals. This created an expensive inefficiency, with employees spending extra time recreating proposals from scratch when there may have been very similar successful proposals they could draw from and work off.
The consulting firm hired Evalueserve to solve this RFP process issue. The client implemented our AI-powered Publishwise platform, which uses a recommendation engine to assess RFPs and source relevant past proposals. Publishwise also has a search feature that allows users to search the knowledge database. The platform improved knowledge management on a global level and ultimately helped boost sales at its satellite offices.
The Challenge
Due to their work with government agencies and non-profits, the consulting firm receives numerous RFPs and, therefore, needs to submit high quantities of proposals. While the client’s proposals had resulted in business successes, their RFP process was inefficient, and they were not making the most of previous proposals. By not using any centralized catalog of past proposals, they were losing the advantage of building off and borrowing from them, a lost opportunity to save employees valuable time.
The firm’s multiple business units and satellite offices were contributing factors in their difficulties with knowledge management and the RFP process. The company structure and geographic distance had led to knowledge silos, as the different business units and offices did not have open lines of communication with each other.
To resolve RFP process challenges, the client asked Evalueserve to make proposal knowledge readily available for everyone in the company, regardless of their office location or line of business.
Our Solution
We implemented our Publishwise proposal content management platform for the client and their specific needs. Publishwise helps users identify current RFP needs and locate any relevant past proposal content. For example, a user working on a water conservation proposal would want to see other water conservation proposals their colleagues had put together.
Publishwise identifies past proposals in two ways: through AI-powered recommendation engines and a search function on the platform.
The recommendation engine works by having a user upload the RFP a prospective client sent them. The recommendation engine will analyze that RFP, however long it may be, and promptly recommend the related proposals in the proposal catalog. One great thing about the recommendation engine in Publishwise is that it makes it so that users don’t even need to know what they’re looking for – the platform will figure it out for them. The recommendation engine AI uses natural language processing, machine learning, and deep learning techniques, as well as topic clustering, multi-level sorting mechanisms, and similarity assessments to achieve its results.
Alternately, if a user knows what they’re looking for, they can search for that term using the search function to pull up all related proposals in the knowledge base.
Business Impact
- Improved sales at smaller satellite offices, such as their office in Nigeria.
- Removed silos and allowed users to share knowledge globally across lines of business.
- Enhanced search efficiency with the advanced recommendation engine. Users don’t have to be sure what they’re looking for – the platform will find relevant proposals on its own.
