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Salesforce Education Global

Pearson

Customer Service Automation

Pearson needed to answer learner questions, returns, and order requests more intuitively as customer expectations shifted toward conversational support across chat and voice.

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2,500 calls expected to be handled per month
225k questions answered since August 2025
70% of questions resolved in Pearson’s UK Schools division

Reported impact

Business outcome

Pearson reported 225k questions answered since August 2025, 70% resolution in its UK Schools division, and a voice-support design expected to handle about 2,500 calls per month.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

Pearson built a learner support layer on Agentforce to give students, teachers, and administrators self-service help across digital channels. The Pearson Virtual Assistant handles FAQs, returns, order tracking, invoices, and account questions in web and mobile chat, while Agentforce Voice extends the same experience to phone support. Requests are routed to specialized subagents that use trusted enterprise context rather than generic answers: Service Cloud stores the knowledge articles and support content, and Data 360 joins order and customer context so each interaction reflects the learner's current situation. For order-related questions, the assistant can also reach data held in Google BigQuery through zero-copy access. When a request falls outside the agent's scope, the conversation is handed to a human rep with the full context preserved so support continues without forcing the learner to start over.

Data flow

Learner questions enter via chat or voice, Agentforce routes each request to the right subagent, Service Cloud and Data 360 provide knowledge and customer context, BigQuery contributes order data, and resolved actions or escalations are passed to human reps with the conversation state intact.

Solution architecture

4 components · 3 layers
  1. Storage
    • Data 360 Unifies enterprise and behavioral data so teams can answer learner questions, process returns, and track orders across chat and voice with current context.
  2. Serving
    • Agentforce Voice Extends agentic support into voice channels used to answer learner questions, process returns, and track orders across chat and voice.
    • Service Cloud Stores cases, knowledge, and service context that supports teams as they answer learner questions, process returns, and track orders across chat and voice.
  3. Orchestration
    • Agentforce Runs the AI agent workflows that answer learner questions, process returns, and track orders across chat and voice.

Architecture clues

  • Agentforce routes requests to specialized subagents instead of one generic bot.
  • Conversation context is preserved when cases escalate from self-service to a human rep.
  • Zero-copy access to Google BigQuery is used for order-related answers.

Evidence from the source

2,500 calls expected to be handled per month
225k questions answered since August 2025
70% of questions resolved in Pearson’s UK Schools division
Pearson keeps learning on track with support from Agentforce Agentic support uses trusted context to answer questions, process returns, and track orders across chat and voice, with service reps available when needed.
Transparency Evidence & methodology 100/100 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 value100/100
  • 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 scale10/10
    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
    4 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. Salesforce

    Pearson customer story

    First-party
    https://www.salesforce.com/customer-stories/pearson/
    Source type
    Vendor-published customer story
    Published
    Sep 26, 2025
    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 Sep 26, 2025 · Salesforce · 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.

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