Retail analytics consulting helps brands and retailers combine sales, inventory, marketing and finance data to see true margin, demand and customer value. Hoist DigiSoft connects Shopify, POS, marketplaces, ad platforms, 3PL and accounting data into one secure warehouse, then builds the dashboards and forecasts your merchandising, marketing and finance teams use daily.
What are the data challenges in retail and ecommerce?
- Revenue in one place, cost in five. Shopify knows the order. The ad platforms know acquisition cost. The 3PL knows fulfillment cost. Accounting knows fees and landed cost. Nobody sees contribution margin per order.
- Every platform claims the sale. Meta, Google and email each report attributed revenue that often adds up to more than you actually sold.
- Inventory out of step. Stock levels differ between the store, the marketplace, the warehouse and the planning spreadsheet.
- Channels reported separately. Direct-to-consumer, wholesale, marketplaces and physical stores each have their own reports and definitions.
- Customer data split by channel. The same person buys online and in store under two records, so lifetime value is understated.
Which systems do we connect?
- Storefronts and marketplaces: Shopify, plus marketplaces such as Amazon where APIs or exports allow.- POS for physical stores.
- Ad and marketing platforms: Google Ads, Meta, email and SMS tools, GA4.
- Fulfillment: 3PL portals, WMS, shipping platforms.
- Finance: QuickBooks, Xero, NetSuite, payment processor payouts and fees.
- Planning spreadsheets: buying plans, budgets and landed cost sheets, replaced by governed tables.
Most brands we’d talk to need a Shopify data warehouse: Shopify and everything around it loaded into BigQuery, Snowflake, Redshift or Microsoft Fabric with tools such as Fivetran, then modeled with dbt. See data warehouse consulting.
Which KPIs and dashboards do we build?
| Area | Example KPIs |
|---|---|
| Profitability | Contribution margin per order, SKU and channel; discount and refund impact; landed cost |
| Marketing | Blended CAC, marketing efficiency ratio (revenue ÷ total ad spend), new vs returning revenue |
| Customers | Customer lifetime value by cohort and first product, repeat rate, time to second order |
| Merchandising | Sell-through, weeks of cover, stockouts, aged and dead stock |
| Operations | Fulfillment cost per order, ship time, return rate by reason |
| Finance | Daily revenue to payout reconciliation, gross margin vs plan |
Each KPI is defined once in the semantic layer, so “revenue” means the same thing (net of discounts, refunds and tax, or not, as you decide) in every dashboard.
Forecasting. With clean sales history by SKU, we build demand forecasting models that factor in seasonality, promotions and lead times. See data mining & predictive analytics.
Operations. Application integrations keep orders, refunds and stock levels in step between the storefront, 3PL and accounting.
How do we handle compliance and security?
- Card data stays out. Payment card numbers never enter the warehouse; we work with processor payout and transaction references only, which helps keep your analytics out of PCI DSS cardholder-data scope.
- Customer personal data minimized. Emails and addresses are hashed or masked in analytics tables unless a role needs them.
- Privacy rules. Designed around US state privacy laws (such as CCPA in California), PIPEDA in Canada and the Privacy Act 1988 in Australia, including honoring deletion requests downstream.
- Encryption and access. Encrypted at rest and in transit; role-based access; API keys for every platform stored in your key vault.
More: data security and encryption.
What does a retail data project look like?
Illustrative example. Not a client project.
A direct-to-consumer brand sells on Shopify and through two wholesale accounts, ships from a 3PL, and runs Meta and Google ads. Finance calculates margin once a month in a spreadsheet.
- Audit: six sources; three different “revenue” definitions in use.
- Architect: BigQuery in the brand’s Google Cloud project; one order-level margin model.
- Build & Secure: Fivetran for Shopify and ad platforms, scheduled loads for 3PL and wholesale files, customer emails hashed.
- Run & Improve: daily margin dashboard; SKU demand forecast added once three seasons of clean history are in place.
Designed outcome: margin per order, SKU and channel visible daily instead of monthly.
Related services
FAQs
What is retail analytics consulting?
Retail analytics consulting is outside expertise to combine and analyze sales, inventory, marketing, customer and finance data for a retailer or brand. It usually means building a data warehouse, defining KPIs like contribution margin and lifetime value, creating dashboards, and adding forecasts for demand and inventory.
Why do we need a Shopify data warehouse?
A Shopify data warehouse stores Shopify data alongside ad spend, fulfillment, wholesale and accounting data that Shopify doesn’t hold. Shopify’s own reports show what happened in the store; a warehouse shows profit per order, customer value across channels and inventory across locations, with history you control.
Can you reconcile ad platform revenue with actual sales?
Yes. Ad platforms each claim credit for the same orders, so their attributed revenue usually adds up to more than you sold. We report actual orders from your store and accounting as the source of truth, then compare blended metrics like total spend against total new-customer revenue.
Do you store customer payment card data?
No. Payment card numbers never enter the warehouse. We use payment processor transaction and payout references for reconciliation, which helps keep your analytics environment out of cardholder-data scope. Customer emails and addresses are hashed or masked in reporting tables unless a specific role needs them.
See your real margin, every day.
Book a free 30-minute data audit. We’ll map your storefront, ad, fulfillment and finance data and show you what it takes to see margin per order.