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IBM Media and Entertainment Germany

BurdaForward

Customer Service Automation · Sales Automation

BurdaForward wanted an automated assistant that could improve customer service and buying advice across a large catalog of reviewed products without requiring the company to hand-build and maintain complex dialog logic for every product path.

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10x faster selection process than before
more than 1,000 products
~15% increase in customer satisfaction

Reported impact

Business outcome

BurdaForward shortened the path from product question to tailored recommendation, improved customer satisfaction, and created a more scalable conversational sales model for a large product catalog.

  • Customer Experience
  • Revenue Growth
  • Scalability

Solution

BurdaForward partnered with IBM Client Engineering and IBM Expert Labs to pilot a conversational buying-assistant architecture that turns its product-review and recommendation content into a mobile- and chat-first experience. The solution combines IBM watsonx Assistant, IBM Watson Discovery, and IBM watsonx.ai. In the implemented workflow, watsonx Assistant runs the semi-structured conversation with the user, watsonx.ai converts natural-language requests into API request filters and helps steer the dialog dynamically, and Discovery supports the retrieval layer behind the product-question workflow. This design lets the team guide shoppers toward suitable products without manually modeling every action path, while still grounding responses in BurdaForward's recommendation assets. Because the architecture can onboard hundreds of products, the pilot established a reusable pattern for conversational commerce across the publisher's digital portfolio instead of a one-off chatbot.

Data flow

A shopper starts a chat-based product query, watsonx Assistant manages the dialog, watsonx.ai converts the user's criteria into API filters, Discovery-backed content helps narrow the candidate products, and the assistant returns recommendations that move the user toward purchase.

Solution architecture

3 components · 2 layers
  1. Compute
    • IBM watsonx.ai Transforms natural-language intents into API request filters and helps adapt the dialog without heavy manual modeling.
  2. Serving
    • IBM watsonx Assistant Runs the conversational interface that guides users through product questions and purchase-oriented choices.
    • IBM Watson Discovery Supports retrieval over BurdaForward's product-review and recommendation content during the advisory workflow.

Architecture clues

  • Natural-language questions are converted into API request filters rather than being handled with fixed menus alone.
  • The design was built to onboard hundreds of products without complex action modeling.
  • The dialog is semi-structured so the team can guide users without authoring every branch manually.

Evidence from the source

"The company was looking for a scalable conversational AI solution that could cover more than 1,000 products."
"The selection process is 10 times faster than it was previously"
"customer satisfaction increased by around 15%"
"guide the dialog dynamically, reducing the modelling effort"
"uses watsonx.ai to generate API request filters from natural language"
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
    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

    BurdaForward customer story

    First-party
    https://www.ibm.com/case-studies/burdaforward
    Source type
    Vendor-published customer story
    Published
    May 14, 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

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

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