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Case StudyAIAgentic AIData Analytics10 min read

Case Study: bevlYtics in the DataToInsights.ai Ecosystem — Turning Beverage Industry Data into Actionable Insights

Turning Beverage Industry Data into Actionable Insights

In the beverage industry, manufacturers and distributors contend with data complexity: disparate data streams, fragmented sales channels, evolving SKUs and pack-sizes, and mounting pressure for real-time decision-making. By integrating syndicated retail data (e.g., IRI), distributor/depletion shipments (via systems such as VIP), and ERP system data, the DataToInsights.ai suite empowers beverage businesses to transform raw data into strategic insights. One key application in this stack is bevlYtics—a purpose-built analytics platform designed exclusively for the beverage sector. bevlYtics functions as the ingestion-to-insight engine for beverage brands and distributors: capturing distributor shipment and depletion data, syndicated retail POS data, internal ERP logistics and costing data—and layering them into actionable dashboards, automated reports and decision-making workflows. By leveraging bevlYtics through the DataToInsights.ai platform, businesses convert data overload into competitive advantage.",

Rapid SKU-Format Visibility
Within weeks of deployment the team gained clear visibility into SKU size/pack performance across channels. For example, they discovered single-serve cans outperforming 12-packs in urban convenience channels — prompting immediate production and distribution reallocation.
Distributor Performance Improvement
Dedicated dashboards surfaced under-performing regions and distributors. This enabled targeted sales management, distributor engagement and logistics support interventions, improving execution.
Faster Product Launch Insights
New product-launch dashboards revealed uptake trajectories within weeks rather than months. This enabled mid-cycle promotional or pricing adjustments instead of waiting for post-launch reviews.
Inventory & Fulfilment Gap Reduction
Real-time alerts on out-of-stock conditions and fulfilment gaps reduced episode volume and improved stock-turn. The result: fewer lost-opportunity costs and better working-capital use.
Case Study: bevlYtics in the DataToInsights.ai Ecosystem — Turning Beverage Industry Data into Actionable Insights

Solutions Implemented

Data Ingestion & Integration

  • bevlYtics ingests syndicated retail POS data (IRI/Nielsen) tracking volume, value, pricing and distribution.
  • It accepts distributor/depletion data from systems like VIP (supplier → distributor shipments; distributor → retail depletions).
  • Internal ERP data including production, inventory, costing and logistics are integrated.
  • Ad-hoc uploads from Excel/CSV files (legacy systems, regional offices, distributor partners) are accommodated.
  • The “Data Upload Engine” automatically cleanses, transforms and standardises raw data—removing manual bottlenecks, enforcing consistent SKU/pack/format definitions.\n\n

Semantic AI & Analytics Layer

A Semantic AI engine combines ML, NLP and knowledge-graph techniques to:

  • Standardise SKUs, pack sizes, region/distributor identifiers.
  • Organise hierarchies (brand → SKU → pack size → format).
  • Segment by channel (on-premise, off-premise, retail) and region.
  • Deliver real-time dashboards and alert-based workflows.

Dashboards & Automated Reporting

  • Live dashboards monitor SKU/size performance by region, distributor and channel.
  • New product-launch dashboards track velocity, region uptake and distributor traction.
  • Inventory monitoring modules detect fulfilment gaps: out-of-stock alerts, lost-sales identification.
  • Price-optimisation modules balance margin vs. movement.
  • Fully automated weekly scorecards and slide-generation streamline sales and executive review workflows.

Deployment & Workflow Integration

  • As a SaaS-based platform, bevlYtics can be rapidly deployed. Through DataToInsights.ai, deployment is enhanced with governance, data-pipeline oversight, and custom integrations (e.g., APIs with IRI/VIP, ERP systems).
  • The objective is not simply analytics, but embedding actionable workflows into daily operations.

Measurable Business Impact

The implementation delivered significant improvements across multiple business dimensions:

Rapid SKU-Format Visibility

Within weeks of deployment the team gained clear visibility into SKU size/pack performance across channels. For example, they discovered single-serve cans outperforming 12-packs in urban convenience channels — prompting immediate production and distribution reallocation.

Distributor Performance Improvement

Dedicated dashboards surfaced under-performing regions and distributors. This enabled targeted sales management, distributor engagement and logistics support interventions, improving execution.

Faster Product Launch Insights

New product-launch dashboards revealed uptake trajectories within weeks rather than months. This enabled mid-cycle promotional or pricing adjustments instead of waiting for post-launch reviews.

Inventory & Fulfilment Gap Reduction

Real-time alerts on out-of-stock conditions and fulfilment gaps reduced episode volume and improved stock-turn. The result: fewer lost-opportunity costs and better working-capital use.

Conclusion

For beverage brands and distributors aiming for growth and operational excellence, integrating bevlYtics within the DataToInsights.ai analytics stack offers a domain-specific, high-impact solution. From data ingestion (VIP shipments, IRI retail POS, ERP systems) through semantic-AI processing, to live dashboards and automated reports—bevlYtics transforms raw data into strategic actions. The outcome: enhanced operational visibility, faster decision-making, cost-reduction in analytics processes, and more targeted growth initiatives.

We recommend beverage-industry teams evaluate bevlYtics via a 90-day pilot through the DataToInsights.ai platform to validate fit, build their insight pipelines, and scale to full deployment for measurable business impact.

Sashank Dulal
Author
Sashank Dulal
sashank.dulal@cloudproai.com

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