Accenture Education Australia

Queensland University of Technology

Customer Service Automation

Queensland University of Technology needed to attract and retain prospective students more effectively, but fragmented engagement processes and slow responses made it harder to guide people through study choices, enrolment, and lifelong-learning pathways with timely, personalized support.

17 additional domestic outreach call campaigns
69% decrease in response times
8,000 proactive student outreaches compared to 2023
84% boost in proactive student engagement

Reported impact

Business outcome

QUT improved the timeliness and relevance of student engagement, gave advisors better context for each interaction, streamlined professional-learning registration, and created a scalable digital platform that supports enrolment, retention, and service differentiation.

  • Customer Experience
  • Productivity Improvement

Solution

Queensland University of Technology and Accenture built a student-engagement platform on Salesforce that unifies relationship, data, and AI capabilities for the learner journey. The implementation started with a CRM foundation based on Salesforce Education Data Architecture so QUT staff could capture student interactions from first enquiry through enrolment and alumni engagement. Accenture then added Salesforce Data Cloud and Einstein AI to enrich that profile with faster enquiry handling, personalized recommendations, and better advisor guidance. Because the platform connects multiple systems, lifelong learners can register for professional education without re-entering the same information repeatedly. Advisors use the combined data and AI layer to prioritize outreach, answer questions sooner, and tailor guidance around course choices and student life. The result is a single service ecosystem that combines operational efficiency with more human and timely student interactions.

Data flow

Prospective-student and learner interactions are captured through the CRM foundation built on Salesforce Education Data Architecture, then unified through Salesforce Data Cloud. Einstein AI uses that profile and cross-system context to support advisor outreach, enquiry handling, and personalized recommendations, and the resulting guidance is delivered back through staff engagement workflows and learner registration experiences.

Solution architecture

3 components · 2 layers
  1. Compute
    • Einstein AI Adds AI-driven enquiry management and personalization so advisors can respond faster and tailor support.
  2. Storage
    • Salesforce Education Data Architecture Provides the CRM foundation that captures a complete view of student interactions across the learner lifecycle.
    • Salesforce Data Cloud Unifies student data from multiple systems so staff can act on timely, personalized insights.

Architecture clues

  • QUT later introduced Data Cloud and Einstein AI more broadly for service enhancement.
  • Salesforce Data Cloud and Einstein AI are used for enquiry management and personalization.
  • The CRM platform covers the journey from initial inquiry to alumni engagement.
  • The platform supports cross-system registration without repeated data entry.

Evidence from the source

"Lifelong learners now benefit from a streamlined registration process, allowing them to enroll in professional education without repeatedly entering data across multiple systems."
"QUT and Accenture have launched a next-gen customer engagement platform focused on personalized experiences for students using Salesforce's Data Cloud and Einstein AI."
"This collaboration has resulted in an 84% boost in proactive student engagement"
"This system allows staff to actively connect with students from initial inquiry through to enrolment and alumni engagement."
"the 69% decrease in response times means staff can answer enquiries from future students sooner."
Transparency Evidence & methodology 95/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

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 value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Accenture

    Queensland University of Technology customer story

    First-party
    https://www.accenture.com/en/case-studies/technology/technology-connected-campus-queensland-university
    Source type
    Vendor-published customer story
    Published
    Aug 28, 2024
    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 Aug 28, 2024 · Accenture · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.