Reported impact
Business outcome
George Mason can now handle 90% of student cases on the spot, increased high-school applications by 9%, and replaced four separate offices with one service center.
- Customer Experience
- Productivity Improvement
SalesforceEducationCustomer Service AutomationMarketing PersonalizationData AnalyticsERP / CRMCustomer ExperienceProductivity Improvement2023 SalesforceSF#00a1e0EducationUnited StatesTo better reach students from all backgrounds, George Mason University aimed to create a more efficient, student-centered experience with data and automation. Mason used dashboards for real-time goal tracking and…1 service center replaced 4 separate officesDec 26, 20236Customer Service Automation|Marketing PersonalizationCRM Analytics · Service Cloud · Tableau0 2023-12-26 6
George Mason University
Customer Service Automation · Marketing Personalization
To better reach students from all backgrounds, George Mason University aimed to create a more efficient, student-centered experience with data and automation. Mason used dashboards for real-time goal tracking and behavior-based models to scale up recruitment.
1 service center replaced 4 separate offices
9% increase in high school applications
90% of student cases can be handled on the spot
Solution
George Mason University used Salesforce to replace fragmented student-support processes and low-scale recruiting workflows with a more automated, student-centered operating model. Service Cloud became the one-stop case and history layer for student services, replacing four separate offices and giving staff access to each student's prior issues and current context so questions about registration, enrollment, and financial aid can be handled in one place. Tableau and CRM Analytics provide real-time visibility into recruitment goals, behavior-based models, and emerging service bottlenecks, allowing the university to monitor territory performance, direct resources to problem areas, and spot students who need help even if they never reach out. The result is a single data-driven support and recruitment stack that lets recruiters scale from hundreds of contacts to thousands while improving issue resolution and student satisfaction without a comparable increase in staffing.
Data flow
Recruitment interactions and student-service cases are recorded in Service Cloud, CRM Analytics and Tableau turn that data into live dashboards and behavior models, and staff use the insights to personalize outreach and resolve student issues on the spot.
Solution architecture
- Serving
- Service Cloud Stores cases, knowledge, and service context that supports teams as they combine student-service cases and recruitment insights into one automated experience.
- Tableau Provides dashboards and analytics teams use while they combine student-service cases and recruitment insights into one automated experience.
- CRM Analytics Surfaces analytics and goal tracking that help teams combine student-service cases and recruitment insights into one automated experience.
Architecture clues
- Behavior-based models and nudge communications are used to scale recruiting outreach.
- Real-time dashboards are used to track recruitment goals and surface service bottlenecks.
- Service Cloud replaced four separate student-service offices with one case workflow.
Evidence from the source
9% increase in high school applications
90% of student cases can be handled on the spot
Mason used dashboards for real-time goal tracking and behavior-based models to scale up recruitment.
Recruiters can now reach thousands of students instead of hundreds without extra cost.
This new student experience increased student and staff satisfaction, allowing Mason to reach students in new ways and provide support to students who may not have accessed it in the past.
To better reach students from all backgrounds, George Mason University aimed to create a more efficient, student-centered experience with data and automation.
Transparency Evidence & methodology
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
How scores are calculated
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Customer reference value100/100
- Problem clarity20/20Measures whether the customer's operational problem is specific enough to compare.
- Implementation depth25/25Rewards named components, data flow, integrations, and architecture detail.
- Outcome specificity20/20Measures whether the reported benefit is concrete rather than a generic claim.
- Quantified impact25/252 metrics describe measurable impact.
- Deployment scale10/10Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
- Source authority20/25The main evidence is an official vendor-published customer story.
- Claim traceability21/2510 of 12 key fields link to a named source.
- Direct evidence20/206 direct source quotes retained.
- Date provenance10/10The publication date comes from the source page or publisher.
- Source transparency8/101 source reference document publisher, URL, authority, retrieval, and supported fields.
- Implementation specificity10/10Rewards source-backed components, data flow, quantified impact, and a specific outcome.
Traceability
Sources & provenance
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
- https://www.salesforce.com/customer-stories/george-mason-increases-automation-efficiency/SalesforceFirst-party
George Mason University customer story
Supports- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes
- Publication date
Date evidence
- Published on source page Dec 26, 2023 · Salesforce · First-partyOpen date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.
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