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Microsoft Software / SaaS United States

Yobi

Data Lake / Data Warehouse Modernization · Marketing Personalization

In today's data-driven world, understanding customer behavior and having the ability to predict customer intent through data and analytics is a paramount advantage that businesses should embrace to remain competitive. Yobi, founded in 2019 by Max Snow, Bill Wise, and Tom Griffiths, is on a mission to enable companies to compete with powerhouses like Amazon and Google and their ability to predict their customers' buying behaviors. Harnessing the power of Microsoft Azure Databricks and Azure Data Factory , Yobi is helping to democratize ethical access to customer third-party data for all.

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Customer acquisition for the History Channel increased by 500 percent.
Machine learning predictive performance for a Fortune 500 insurance company improved by 10 percent, leading to a three-times increase in website marketing conversions.
The raw data is compressed during the embedding process, preserving privacy and, in some cases, reducing storage fees by 10 times the typical costs.

Reported impact

Business outcome

Customer acquisition for the History Channel increased by 500 percent.

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

Solution

Yobi built its Microsoft solution to improve customer-personalization workflows. In today's data-driven world, understanding customer behavior and having the ability to predict customer intent through data and analytics is a paramount advantage that businesses should embrace to remain competitive. Yobi, founded in 2019 by Max Snow, Bill Wise, and Tom Griffiths, is on a mission to enable companies to compete with powerhouses like Amazon and Google and their ability to predict their customers' buying behaviors. Harnessing the power of Microsoft Azure Databricks and Azure Data Factory , Yobi is helping to democratize ethical access to customer third-party data for all. Key components included Azure Machine Learning ran model inference or heavy processing, and Azure Databricks ran model inference or heavy processing.

Data flow

Operational data flows into Azure Machine Learning, where Azure Machine Learning applies transformations and business logic before Dynamics 365 Customer Insights delivers governed insights to teams across Yobi.

Solution architecture

4 components · 3 layers
  1. Compute
    • Azure Machine Learning Builds, trains, and operationalizes predictive models and machine-learning pipelines.
    • Azure Databricks Transforms large-scale datasets and runs analytics or model-processing jobs.
  2. Storage
    • Dynamics 365 Customer Insights Unifies customer data so teams can personalize service and engagement workflows.
  3. Orchestration
    • Azure Kubernetes Service Operates containerized application and AI workloads in a managed Kubernetes environment.

Architecture clues

  • Also, Yobi and Microsoft are focused on building a scalable query engine that can support a high throughput of API requests in addition to internal research and development projects.
  • And for security and identity management, Yobi also adopted Azure Active Directory Microsoft Defender for Cloud Focusing on what's next With its current offering already returning significant results for several businesses, Yobi is preparing for next steps.
  • As Yobi continues to grow and its environment becomes more complex, the company will turn to Azure Kubernetes Service to address these challenges using a fully managed Kubernetes service.

Evidence from the source

Customer acquisition for the History Channel increased by 500 percent.
Harnessing the power of Microsoft Azure Databricks and Azure Data Factory , Yobi is helping to democratize ethical access to customer third-party data for all.
In today's data-driven world, understanding customer behavior and having the ability to predict customer intent through data and analytics is a paramount advantage that businesses should embrace to remain competitive. Yobi, founded in 2019 by Max Snow, Bill Wise, and Tom Griffiths, is on a mission to enable companies to compete with powerhouses like Amazon and Google and their ability to predict their customers' buying behaviors. Harnessing the power of Microsoft Azure Databricks and Azure Data Factory , Yobi is helping to democratize ethical access to customer third-party data for all.
Machine learning predictive performance for a Fortune 500 insurance company improved by 10 percent, leading to a three-times increase in website marketing conversions.
The raw data is compressed during the embedding process, preserving privacy and, in some cases, reducing storage fees by 10 times the typical costs.
Transparency Evidence & methodology 91/100 reference value · 87% 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 value91/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • 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 specificity8/10
    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. Microsoft

    Yobi customer story

    First-party
    https://www.microsoft.com/en/customers/story/1655233712862822640-yobi-azure-dynamics365-partner-professional-services-usa
    Source type
    Vendor-published customer story
    Published
    Jul 7, 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 Jul 7, 2023 · Microsoft · 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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