Retail intelligence

Every retail decision, powered by real-time data from EVA

EVA brings together transaction, inventory, fulfillment, and customer data into a single reporting layer, giving operations, finance, and store teams the visibility they need to act faster.

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EVA unified commerce platform integrating Apache Kafka event streaming and Apache Parquet data formats for real-time retail data infrastructure

One source of data for all channels. Store teams, planners, and analysts all work from the same data, not exports from three different systems.

Over 45 entity types exported to your data lake. Orders, payments, loyalty, surveys, stock: everything EVA knows about your retail operation, in your infrastructure.

Works with the infrastructure you already have. Azure, S3, or Google Cloud. EVA delivers Parquet files directly. No pipelines to build and no ETL to maintain.

Cross-channel visibility

Retail Analytics and Omnichannel Reporting for Enterprise Retail

Report on omnichannel conversion, cross-channel customer lifetime value, and fulfillment performance in a single dashboard. No need for stitching data or doing exports between systems.

Give every store manager the numbers that matter, in real time

Real-time KPIs per location: revenue, conversion rate, average order value, and associate productivity. Configurable per role, updated continuously across every store you run, without manual intervention.

EVA real-time store performance dashboard showing live retail KPIs including daily revenue, average order value, active associates, and transaction count across all store locations

Compute loyalty ROI, churn risk, and lifetime value directly from your data lake

EVA's loyalty engine exports programs, tiers, badges, and customer point histories as first-class entities, enabling CLV, churn risk, and loyalty ROI analysis without manual extraction or enrichment.

EVA loyalty analytics dashboard showing loyalty program ROI at 3.4x return, customer churn risk percentage, and tier progression breakdown across Bronze, Silver, and Gold loyalty tiers

Data infrastructure

45+distinct entity types exported to your data lake

EVA exports over 45 distinct entity types: from orders, invoices, and payments to loyalty mutations, store activity, surveys, and product content. Each entity maps to its own versioned data stream, making the dataset comprehensive enough for machine learning, demand forecasting, and cross-entity analytics without additional data sourcing.

EVA retail data lake entity types including orders, invoices, shipments, stock mutations, loyalty programs, payment transactions, surveys, and 45 plus structured data entities exported for retail analytics and business intelligence

Connect to the BI tools your team already uses

Your data in your environment

EVA exposes your retail data via structured exports and API access. Ready for the reporting tools your team already works with. No new tooling required.

Parquet files to your cloud storage of choice

Structured Parquet files delivered directly to Azure Data Lake Storage Gen2, Amazon S3, or Google Cloud Storage. From there, your BI and analytics tools work with it immediately.

Schema-enforced, versioned data streams

Schema-enforced, versioned data streams

Live recommender inference on your retail data

Trained models run directly against EVA's data layer with no data movement. Order volume forecasting and retail recommendations at production scale, on your own data, not generic benchmarks.

Where data becomes action

From raw data to decisions your teams can act on

EVA's data layer does more than store and export. It powers demand forecasts, product recommendations, and plain-English querying directly on your retail data. Analysts, planners, and operations teams get answers without building pipelines or writing SQL.
EVA retail intelligence platform connecting orders, inventory, customers, products, and store data to power demand forecasting and AI-driven product recommendations for enterprise retailers
Predictive intelligence

From historical data to demand forecasts and product recommendations

EVA's data layer powers demand forecasting and product recommendations directly on your retail data. Order volume forecasting handles weekly and yearly seasonality with automatic retraining. A recommender system generates cross-sell suggestions per customer, product, and store location.

EVA natural language retail analytics interface answering plain English questions about store performance, showing conversion rate analysis and customer survey insights without requiring SQL queries
Survey analytics

Ask questions about your retail data in plain English

EVA exports survey definitions and customer responses as first-class data lake entities. Operations teams can query survey results, order flows, and customer behavior in plain English and get answers from live warehouse data, without writing SQL.

Built for scale

Feed your data lake directly from EVA

Near real-time streaming. EVA's data lake is not a batch export. It is a fully event-driven streaming pipeline, built on Kafka and Parquet.

  • Immediate publishing on every domain event
  • Parquet files written continuously to cloud storage
  • No batch windows or data gaps
EVA retail data platform with real-time event streaming, publishing order and inventory updates continuously without batch delays

Azure, S3, and Google Cloud Storage. Confluent Cloud sink connectors deliver EVA's Parquet data directly to the cloud environment you already operate in.

  • Azure, Amazon S3, and Google Cloud supported
  • Works with the BI tools you already use
  • No ETL pipelines and no migration projects
EVA data lake integration with Azure Data Lake Storage, Amazon S3, and Google Cloud Storage for structured Parquet file delivery

Bronze, Silver, Gold. EVA's Parquet streams feed a structured Databricks data warehouse with three distinct layers, ready for dashboards the moment data arrives.

  • Bronze holds raw records from EVA's streams
  • Silver applies business logic and partitioning
  • Gold delivers pre-aggregated dashboard tables
EVA retail data warehouse built on Databricks Unity Catalog with Bronze, Silver, and Gold layers for omnichannel retail analytics

Watchtower. EVA monitors order flows continuously. Fulfillment exceptions are detected, classified by store, and surfaced before they become customer problems.

  • Exceptions automatically detected and classified by store
  • Daily reports delivered to your cloud storage
  • No platform login required to stay informed
EVA Watchtower operational monitoring running on Apple Mac Mini hardware for always-on in-store order flow monitoring and fulfillment exception detection

Near real-time streaming. EVA's data lake is not a batch export. It is a fully event-driven streaming pipeline, built on Kafka and Parquet.

  • Immediate publishing on every domain event
  • Parquet files written continuously to cloud storage
  • No batch windows or data gaps
EVA retail data platform with real-time event streaming, publishing order and inventory updates continuously without batch delays

Azure, S3, and Google Cloud Storage. Confluent Cloud sink connectors deliver EVA's Parquet data directly to the cloud environment you already operate in.

  • Azure, Amazon S3, and Google Cloud supported
  • Works with the BI tools you already use
  • No ETL pipelines and no migration projects
EVA data lake integration with Azure Data Lake Storage, Amazon S3, and Google Cloud Storage for structured Parquet file delivery

Bronze, Silver, Gold. EVA's Parquet streams feed a structured Databricks data warehouse with three distinct layers, ready for dashboards the moment data arrives.

  • Bronze holds raw records from EVA's streams
  • Silver applies business logic and partitioning
  • Gold delivers pre-aggregated dashboard tables
EVA retail data warehouse built on Databricks Unity Catalog with Bronze, Silver, and Gold layers for omnichannel retail analytics

Watchtower. EVA monitors order flows continuously. Fulfillment exceptions are detected, classified by store, and surfaced before they become customer problems.

  • Exceptions automatically detected and classified by store
  • Daily reports delivered to your cloud storage
  • No platform login required to stay informed
EVA Watchtower operational monitoring running on Apple Mac Mini hardware for always-on in-store order flow monitoring and fulfillment exception detection

Near real-time streaming. EVA's data lake is not a batch export. It is a fully event-driven streaming pipeline, built on Kafka and Parquet.

  • Immediate publishing on every domain event
  • Parquet files written continuously to cloud storage
  • No batch windows or data gaps
EVA retail data platform with real-time event streaming, publishing order and inventory updates continuously without batch delays

Azure, S3, and Google Cloud Storage. Confluent Cloud sink connectors deliver EVA's Parquet data directly to the cloud environment you already operate in.

  • Azure, Amazon S3, and Google Cloud supported
  • Works with the BI tools you already use
  • No ETL pipelines and no migration projects
EVA data lake integration with Azure Data Lake Storage, Amazon S3, and Google Cloud Storage for structured Parquet file delivery

Bronze, Silver, Gold. EVA's Parquet streams feed a structured Databricks data warehouse with three distinct layers, ready for dashboards the moment data arrives.

  • Bronze holds raw records from EVA's streams
  • Silver applies business logic and partitioning
  • Gold delivers pre-aggregated dashboard tables
EVA retail data warehouse built on Databricks Unity Catalog with Bronze, Silver, and Gold layers for omnichannel retail analytics

Watchtower. EVA monitors order flows continuously. Fulfillment exceptions are detected, classified by store, and surfaced before they become customer problems.

  • Exceptions automatically detected and classified by store
  • Daily reports delivered to your cloud storage
  • No platform login required to stay informed
EVA Watchtower operational monitoring running on Apple Mac Mini hardware for always-on in-store order flow monitoring and fulfillment exception detection

FAQ

Questions?
We’re here to help.

What data does EVA export to a data lake?

EVA exports over 45 distinct entity types including orders, invoices, payments, stock mutations, shipments, loyalty programs, customer activity, surveys, financial periods, and product content. Every entity maps to a versioned data stream, updated continuously as events occur in the platform.

Which cloud storage providers does EVA support?

EVA delivers structured Parquet files directly to Azure Data Lake Storage Gen2, Amazon S3, and Google Cloud Storage. No custom ETL pipelines are required. Your existing BI and analytics tools can connect to the data immediately.

Does EVA support real-time retail analytics or is it batch-based?

EVA's data layer is fully event-driven. Every order, stock movement, payment, and customer interaction triggers an immediate publish to a dedicated data stream. There are no batch windows or scheduled exports. Your data lake reflects your retail operation continuously, not at the end of the day.

Can EVA's data layer connect to Power BI, Tableau, or Looker?


EVA delivers structured Parquet files to your cloud storage of choice. From there, any BI tool that connects to Azure, S3, or Google Cloud works with the data directly. This includes Power BI, Tableau, Looker, Google Data Studio, and others. No named connectors are required on the EVA side.

How does EVA handle omnichannel reporting across stores and ecommerce?

EVA connects store, ecommerce, fulfillment, and customer data in a single platform. All transactions, regardless of channel, flow into the same data layer. Omnichannel conversion, cross-channel customer lifetime value, and fulfillment performance are all reportable from one source without stitching data between systems.

Can EVA data be used for machine learning and demand forecasting?

Yes. EVA's data lake exports are structured and versioned, making them suitable for downstream machine learning workloads. EVA's own forecasting layer uses order history per tenant to generate demand forecasts that handle weekly and yearly seasonality with automatic retraining. Retailers can also use the exported data in their own ML infrastructure.

How does EVA support loyalty analytics and customer lifetime value reporting?


EVA exports loyalty programs, tiers, badges, and per-customer point mutation history as first-class data entities. This gives analytics teams everything needed to compute customer lifetime value, tier progression rates, churn risk, and loyalty ROI directly from the data lake, without manual extraction or enrichment steps.

Is EVA's retail analytics suitable for enterprise retailers with complex store networks?

Yes. EVA's data infrastructure is built for multi-location, multi-market retailers. Data is partitioned by tenant, store, and time period, making it performant for enterprise-scale query volumes. Store performance dashboards are configurable per role, and the data warehouse layer delivers pre-aggregated tables ready for reporting at any organizational level.

What is the difference between EVA's data lake and a traditional retail reporting tool?

Traditional retail reporting tools pull data on a schedule and typically cover one system at a time. EVA's data layer streams every retail event continuously across orders, payments, inventory, loyalty, and customer behavior into a unified, structured dataset. The result is a foundation for real-time dashboards, machine learning, and cross-entity analytics rather than a fixed set of pre-built reports.

Can store managers access performance data without a data analyst?

Yes. EVA provides real-time KPI dashboards per store location, configurable per role. Store managers see revenue, conversion rate, average order value, and associate productivity updated continuously. For deeper analysis, EVA's natural-language querying layer allows operations teams to ask questions over live warehouse data in plain English, without writing SQL.

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