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IBM Retail Italy

Computer Gross

Customer Service Automation · Sales Automation

Computer Gross needed to give thousands of B2B ecommerce customers faster product discovery, quoting, and support because its 145,000-product catalog and growing customer base were overwhelming internal teams with repetitive questions and manual quote generation.

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20% increase in quote-to-order conversion rate
25% growth in average order value
30% reduction in operational costs
35% increase in customer satisfaction

Reported impact

Business outcome

Computer Gross improved customer satisfaction, reduced response and ticket volume, raised quote conversion and order value, and lowered operating cost while creating a more autonomous B2B ecommerce journey.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

Computer Gross adopted IBM watsonx Assistant, built with watsonx.ai, as the conversational layer for a new self-service B2B commerce experience. IBM and partner teams mapped every customer interaction point and integrated the assistant into backend systems through GraphQL APIs so it could do more than answer FAQs. The assistant now understands natural-language product questions, searches a large catalog for the most relevant options, generates tailored quotes instantly based on customer conditions, and surfaces updates on orders, returns, and invoicing without waiting for human intervention. That combination turns the portal into an end-to-end guided buying and service environment rather than a static product catalog. Because customers can complete more of the purchasing journey independently, support and sales teams can focus on higher-value work while the business gains a scalable, round-the-clock customer engagement model suited to a very large reseller network.

Data flow

Customer questions flow through the watsonx Assistant interface, GraphQL APIs connect the assistant to product, pricing, and order systems, the platform returns relevant products or tailored quotes in real time, and resulting self-service actions reduce the need for manual support follow-up.

Solution architecture

3 components · 3 layers
  1. Compute
    • IBM watsonx.ai Supports the complex interaction logic and personalization behind the assistant’s business responses.
  2. Serving
    • IBM watsonx Assistant Provides the conversational AI layer that guides product search, quote generation, and service interactions.
  3. Orchestration
    • IBM watsonx Provides the broader platform capabilities that let Computer Gross deploy and manage the assistant at scale.

Architecture clues

  • Future plans include deeper CRM and analytics integration plus post-sales support automation.
  • The assistant was integrated into backend systems using GraphQL APIs.
  • The platform supports 24x7 product discovery, quoting, and service interactions.

Evidence from the source

“20% increase in quote-to-order conversion rate”
“40% reduction in support ticket volume”
“Computer Gross worked closely with IBM and their technology partners to map out every customer interaction point and seamlessly integrate watsonx Assistant into their systems using GraphQL APIs.”
“generate tailored quotes instantly based on customer selections and conditions”
“the assistant has transformed the e-commerce experience for both customers and internal teams.”
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
    6 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
    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. IBM

    Computer Gross customer story

    First-party
    https://www.ibm.com/case-studies/computer-gross
    Source type
    Vendor-published customer story
    Published
    Jul 23, 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

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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