Retail data strategy consulting
Retail and distribution data has gotten faster, broader, and more political. POS, e-commerce, marketplace, loyalty, and supply chain systems each produce a version of the truth, and the executives looking at margin do not have time to reconcile them by hand. DI Squared builds retail data strategy that integrates these systems into a single model and the analytics layer that sits on top of it. We have done this work for specialty retailers, distributors, and consumer brands since 2008.
Snowflake, Databricks, dbt, Fivetran fluent
HQ Atlanta, serving US, Canada, Europe
- Assortment, pricing, demand forecasting, customer segmentation, channel margin, inventory turns.
- Typical stack: Snowflake or Databricks; dbt; Fivetran for SaaS ingest; Qlik, Power BI, or Tableau.
- We connect merchandising, supply chain, and finance views to the same source of truth.
- Discover, Map, Navigate, Adjust framework applied across every engagement.
What retail and distribution data strategy actually requires
Retail and distribution data strategy is the work of reconciling several adjacent operating models into one analytics platform. The merchandising team thinks in classes, departments, and assortments. The supply chain team thinks in DCs, SKUs, and on-hand. The finance team thinks in margin, markdown, and inventory carrying cost. The customer team thinks in segments, lifetime value, and channel. All of them are right. All of them are looking at the same business. Retail data strategy consulting, done well, builds a model that each function can use without losing its own perspective. We are vendor-neutral, but fluent in the platforms that matter: Snowflake, Databricks, Qlik, Power BI, Tableau, dbt, Fivetran, and the rest of the modern stack.
- Retail and distribution delivery experience since 2008
- Specialty retail, distribution, consumer brands, marketplaces
- Common ingest patterns: POS, e-commerce, marketplace, WMS, ERP, loyalty
- Three-time Qlik Solution Partner of the Year
Retail and distribution analytics use cases we deliver
Retail and distribution engagements typically cluster around a few patterns.
Demand forecasting and replenishment.
SKU-level forecast accuracy, bias, allocation analytics, and lost sales estimation.
Assortment and pricing.
Class and item productivity, markdown analytics, price elasticity, and promotional lift.
Customer segmentation and loyalty.
Cohort analytics, lifetime value modeling, channel preference, and program ROI.
Channel margin and omnichannel performance.
Profitability by channel, fulfillment cost, return rate analytics, and ship-from-store economics.
Inventory turns and working capital.
GMROI, inventory aging, slow-mover analytics, and DC-level productivity.
Supplier and distribution analytics (for distributors).
Line fill rate, on-time delivery, supplier rebates, and customer cost-to-serve.
Architecture for a multi-channel reality
Modern retail and distribution platforms have to ingest from a long list of systems: POS, e-commerce, one or more marketplaces, WMS, ERP, loyalty, returns, and increasingly customer service and review platforms. We design retail data platforms with that reality in mind. Fivetran or comparable tools handle the SaaS-side ingest. Custom or vendor-specific connectors handle POS and ERP. dbt models the data into a coherent warehouse layer. Snowflake or Databricks is the platform we tend toward. Qlik, Power BI, or Tableau serves the merchandising, supply chain, finance, and executive audiences. Governance is built in from the start through Collibra or an equivalent catalog so that “what is a customer” and “what is a SKU” mean the same thing in every dashboard.
Discover, Map, Navigate, Adjust in retail and distribution
Discover.
We assess the merchandising, supply chain, customer, and finance system footprint. We look at master data (item, location, customer, supplier), the existing reporting environment, and how the executive team consumes information today.
Map.
We document a target state: data domains, ingestion pattern, warehouse model, semantic layer, BI footprint, governance approach, and the sequenced roadmap. The document is one a merchandising leader, a CIO, and a CFO can all read.
Navigate.
We deliver in phases that put usable analytics in front of the merchandising, supply chain, and finance audiences as quickly as the underlying integrations allow. Loyalty and customer analytics typically come in a later wave once the core merchandising layer is trusted.
Adjust.
Retail moves fast. Channels shift, vendors change, marketplaces enter and leave the assortment, and the analytics has to keep pace. We help capabilities keep adding tangible value over time.
Retail technology stacks we work with
We are vendor-neutral, but fluent in the retail platforms that power modern commerce. Our clients commonly rely on NCR, Oracle, SAP, Microsoft Dynamics 365, NetSuite, Shopify, Magento, BigCommerce, and other POS, ERP, and eCommerce systems. We integrate these data sources to create a trusted foundation for analytics, reporting, and AI.
A typical engagement may use Snowflake or Databricks as the data platform, dbt for transformation, and Fivetran or client-specific connectors for data ingestion. For governance, we work with platforms such as Collibra, while Qlik, Power BI, and Tableau provide the analytics layer.
We are pragmatic about existing technology investments. If your organization has standardized on Shopify and Power BI, or SAP and Qlik, we build on that foundation rather than recommend starting over. Our focus is maximizing value from the systems you already use while creating a scalable data platform for growth.
Retail data strategy: common questions
Q: Do you work with distributors as well as retailers?
A: Yes. The data models overlap heavily (item, customer, location, supplier, transaction) and the analytics patterns are adjacent. Distributors typically have more emphasis on line fill, on-time delivery, and customer cost-to-serve, and retailers typically have more emphasis on assortment, markdown, and customer lifetime value. We adjust the engagement to the operating model.
Q: We sell across our own site, marketplaces, and physical stores. Can a single platform support all of it?
A: Yes. A modern data platform can integrate all three channels into one warehouse layer, with consistent definitions of customer, SKU, and transaction. The work is real, but the platform decision is rarely the bottleneck. The bottleneck is usually master data and channel-specific business rules, and we plan accordingly.
Q: How do you handle loyalty and customer data privacy?
A: We design customer analytics with row-level access controls, PII tagging in the catalog, and clear segregation between identified and de-identified marts. Where the client operates in jurisdictions with stricter regimes (CCPA, GDPR), we work with your privacy team to build the platform inside that perimeter.
Q: How long does a retail data strategy engagement take?
A: A focused strategy engagement runs six to ten weeks. An implementation that delivers a first-wave retail analytics platform across merchandising, supply chain, and finance typically runs six to twelve months. Customer and loyalty analytics often follows in a second wave.
Build retail analytics every function can trust
Talk with a DI Squared retail strategist about your channel mix, your assortment complexity, and the analytics roadmap your merchandising and finance teams both need.