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  5. ›SCHOLARSHIP ASSESSMENT-2023

Awarded contract·Stephen F. Austin State University·Texas

SCHOLARSHIP ASSESSMENT-2023

Won by Ruffalo Noel Levitz, LLC$300kSingle Award
Contract source

Awarded vendors

  • Ruffalo Noel Levitz, LLC

Description

This contract awards Stephen F. Austin State University a one‑year consulting study to evaluate the effectiveness of its institutional scholarship program and to develop data‑driven recommendations for enrollment, retention, and tuition‑revenue optimization. The consultant will conduct price‑sensitivity analysis, award‑strategy optimization, FAFSA simplification impact modeling, benchmarking against peer institutions, affordability assessments, and dual‑credit evaluations, delivering an interim report by January 15 2024 and a final report by October 2024, along with predictive models and process‑improvement recommendations. • Scope of work includes on‑site and remote analysis, predictive modeling, and actionable recommendations for scholarship mix and pricing strategy. • Evaluation criteria are weighted (25% qualifications, 25% methodology, 15% timeline, 15% references, 20% financial proposal). • Contract term is October 2023 – October 2024 with up to three optional one‑year extensions, and the award was made to Ruffalo Noel Levitz, LLC.

Attached files

Key dates

Bids closed
9/28/2023
Contract modified
9/22/2023
Starts
10/1/2023
Ends
10/1/2024Renews
Duration
1 year

Last updated

Details

Award type
Single Award
Procurement
Full and Open Competition
Industry
Consulting Services
NAICS
541618
Solicitation №
B2400248

Contract value

$300,000

The solicitation does not disclose a contract value. Based on the scope—comprehensive data analysis, predictive modeling, two major reports, and a year‑long engagement—and typical consulting rates for specialized higher‑education analytics (often $150‑$250 k per year for firms of this caliber), a reasonable ballpark is $250,000 to $350,000. Assuming a mid‑range rate and the extensive deliverables, an estimate of roughly $300,000 is plausible. Factors influencing the estimate include the need for on‑site data access, the requirement for statistical modeling expertise, and the optional extensions that could increase total spend.

AI estimate from the award documents

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