Student Experience Data Framework Service
Overview
Fit Score
Settle Intelligence
Settle helps teams find, evaluate, and respond to public RFPs. We continuously surface new opportunities, score them against your company strengths, and draft proposal responses so you can focus on the work that wins business.
SUMMARY
KEY REQUIREMENTS
BUDGET
EstimateCONTRACT DURATION
TIMELINE
RFI Issuance Date: March 19th, 2026
Deadline for Questions from Vendors: April 2nd, 2026 at 3:00 pm MT
RFI Responses Due to Public Purchase: April 9th, 2026 at 3:00 pm MT
Potential RFP Issuance: June/July 2026
Program delivery within 8 weeks
QUESTION DEADLINE
Issuing Agency
Laramie County Community College
Organization overview and procurement intelligence available on paid plans.
DESCRIPTION
The government authority in Cheyenne, Wyoming is seeking a vendor to provide a complete, production-ready Student Experience (SX) data framework service. The goal is to transition from isolated, point-in-time research projects to a comprehensive, strategic data collection program that delivers a unified view of the student journey, incorporating qualitative, quantitative, historical, and real-time information.
The selected provider will offer guidance on program assessment, vision development, and roadmap creation to strengthen institutional capability, as well as recommend organizational structures and processes to sustain SX excellence beyond the initial pilot. The framework must include a functional data platform that fully integrates with existing systems, such as Ellucian Colleague ERP, Canvas LMS, and EAB Navigate CRM. Initial rollout will be in English only to streamline the pilot implementation.
The solution should enable seamless integration with Student Information and Learning Management Systems, linking student sentiment with demographic and academic performance data. Built-in AI and Natural Language Processing tools are required to classify student feedback by topic, sentiment, and emotional effort automatically. Additionally, the service should use predictive modeling to identify at-risk students, detect friction points within the student experience, and highlight avenues for optimization.
Source attribution
This Settle analysis is based on the issuing organization’s public RFP listing.