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Salesforce Retail global

Pandora

Customer Service Automation · Marketing Personalization

Pandora needed to bring its high-touch in-store storytelling, gift guidance, and service quality into digital channels while coping with seasonal support spikes and a fragmented ecommerce architecture.

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22% of total sales handled through Commerce Cloud
45K conversations a month
60% of cases deflected
net promoter score increase of 10%

Reported impact

Business outcome

Pandora improved digital recommendation quality, automated a large share of routine service interactions, and created a platform for loyalty and ecommerce growth that still feels personal to shoppers.

  • Customer Experience
  • Productivity Improvement
  • Revenue Growth
  • Scalability

Solution

Pandora is building an agentic commerce and service stack that recreates its in-store experience online. Agentforce powers Gemma, a personal shopper agent that uses Commerce Cloud product and order history together with Data 360 context to ask about the occasion, recipient, and budget, then recommend jewelry in Pandora's own tone. The same platform also supports Clara, a service agent that answers order-status and care questions by combining Service Cloud with Commerce Cloud fulfillment data. MuleSoft connects the broader commerce environment so personalized recommendations and service answers are grounded in the right customer and order context, and Loyalty Management extends that same data to promotions across key markets. By unifying commerce, service, and loyalty on one Salesforce-centered platform, Pandora can personalize more interactions without recreating the rigid chatbots and fragmented systems it wanted to leave behind.

Data flow

Product, order, and shopper data from Commerce Cloud and connected systems is unified through Data 360 and MuleSoft. Agentforce uses that context to power Gemma and Clara, while Service Cloud supports hand-offs and Loyalty Management turns the same customer view into personalized promotions.

Solution architecture

6 components · 4 layers
  1. Ingest
    • MuleSoft Connects commerce data and downstream systems inside Pandora's digital architecture.
  2. Compute
    • Agentforce Runs Pandora's shopping and service agents for guided recommendations and automated support.
  3. Storage
    • Data 360 Supplies unified customer and interaction data used to personalize recommendations.
  4. Serving
    • Commerce Cloud Provides product, order, and storefront context for ecommerce interactions.
    • Service Cloud Gives service agents access to shopper history and case context.
    • Loyalty Management Turns customer data into personalized loyalty offers across multiple markets.

Architecture clues

  • Clara uses fulfillment data from IBM Sterling through connected Commerce Cloud workflows.
  • Gemma uses order history, product data, recommendation signals, and research insights to personalize shopping guidance.
  • Pandora is moving toward a composable PWA Kit-based storefront architecture.

Evidence from the source

"Clara is already handling 45K conversations a month, deflecting 60% of cases, and a net promoter score increase of 10% indicates that customers are more satisfied with the service they're receiving."
"Commerce Cloud handles 22% of total sales and provides a scalable platform for Pandora's global ecommerce operations."
"To deliver tailored recommendations, Agentforce draws on data across Pandora's ecosystem."
"With Agentforce, they built a personal shopper agent named Gemma that's designed to help customers choose the perfect piece of jewelry."
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
    3 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
    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

    Pandora customer story

    First-party
    https://www.salesforce.com/customer-stories/pandora/
    Source type
    Vendor-published customer story
    Published
    Oct 8, 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 Oct 8, 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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