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NVIDIA Commercial and Professional Services Global

BEN Group

Fraud Detection · Marketing Personalization · Real-time Analytics

BEN Group needed a scalable way to analyze huge volumes of media, creator, and audience data so brands could place product integrations and influencer spend more effectively while also detecting fraud and improving return on advertising investment.

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100,000 Instagram posts analyzed per week
32% lower customer acquisition costs for some clients
39% higher conversion rates for some clients
4x faster analysis of terabytes of content

Reported impact

Business outcome

BEN analyzes media data 4x faster than before, processes 100,000 Instagram posts per week, helped some clients cut customer acquisition cost by 32 percent, and increased conversion rates by 39 percent while making influencer and product-placement campaigns more targeted and efficient.

  • Cost Reduction
  • Revenue Growth
  • Risk Reduction

Solution

BEN Group built its media and influencer analytics workflow on NVIDIA DGX A100 so the company could process far more video, image, text, and audio content than human analysts could review. The DGX A100 platform lets BEN ingest terabytes of unstructured data, analyze around 100,000 Instagram posts per week, and train customized algorithms that predict impressions, views, clicks, and conversions across film, television, streaming, music videos, and social platforms. The same AI layer identifies fraudulent influencers and bot activity, helping clients avoid wasted spend while prioritizing creator partnerships that match campaign goals. By accelerating both model training and large-scale content analysis, BEN turned influencer and product-placement planning into a data-driven recommendation system rather than a manual judgment exercise.

Data flow

Structured and unstructured media data-such as video, audio, images, text, audience signals, and influencer activity-is ingested into DGX A100-backed AI pipelines. BEN's models then score creator and content opportunities, predict funnel outcomes, and flag suspicious behavior before recommendations are delivered to brand clients.

Solution architecture

1 components · 1 layer
  1. Compute
    • NVIDIA DGX A100 Provides the accelerated training and analytics infrastructure BEN uses to process terabytes of media data and train campaign-optimization models.

Architecture clues

  • Customized client algorithms are trained on DGX A100
  • Fraudulent influencer and bot activity are identified as part of the same analytics layer
  • The platform analyzes images, text, video, and audio together
  • The system predicts the full sales funnel from impressions through conversions

Evidence from the source

BEN Group uses NVIDIA DGX A100 to analyze terabytes of data 4x faster than before.
In some cases, the technology has allowed BEN's clients to experience a decrease in their customer acquisition costs by 32 percent and an increase in the conversion rate by 39 percent.
The AI technology can even identify fraudulent influencers and bot activity.
The team can analyze 100,000 Instagram posts per week, derive insights, and even create customized algorithms for their clients.
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
    4 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. NVIDIA

    BEN Group customer story

    First-party
    https://www.nvidia.com/en-us/case-studies/revolutionizing-the-decentralized-digital-world-of-media-and-entertainment/
    Source type
    Vendor-published customer story
    Published
    Aug 2, 2023
    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 2, 2023 · NVIDIA · 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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