Retail Business Intelligence Solutions
Retail Business Intelligence
Retail decisions are made on incomplete data. The Shopify report shows online revenue. The POS export shows in-store. The WMS has stock levels. The loyalty platform has customer segments. Nobody has all four in one place, so margin decisions get made on the channel you happened to export yesterday.
A unified analytics layer pulling from Shopify, POS, WMS, and your loyalty platform. Margin by SKU, inventory performance, store comparison, and customer segmentation, in one dashboard your team can open on Monday morning and trust. We build the data layer first: pipelines, warehouse, unified data model. Then the dashboards on top.
Unified dashboard pulling from Shopify, POS, WMS, and Google Analytics into one view
Inventory analytics with stockout prediction, slow-mover identification, and reorder alerts
Margin and discount analysis by category, channel, and store location
Customer segmentation for loyalty targeting and retention campaigns
Store performance comparison across all locations in one report
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
Trusted by
The brief
Start with what is not working.
Good software decisions begin with the constraint, not a list of features or a preferred technology.
Margin by SKU is spread across three exports, and nobody on the commercial team can see the full picture without a day of manual work?
Stockouts happening in one location while another carries weeks of dead stock because inventory data is not unified in real time?
Plain answer
RaftLabs builds retail business intelligence systems that unify data from Shopify, POS systems, warehouse management software, and loyalty platforms into a single analytics layer. Retail teams use these dashboards to track margin by SKU, identify slow-moving stock, compare store performance, and segment customers for targeted promotions. A first working dashboard typically ships in 6 to 10 weeks, then expands from there.
What to remember
- Retail BI unifies Shopify, POS, WMS, and loyalty data into one analytics layer instead of five disconnected exports
- Margin by SKU, inventory performance, store comparison, and customer segmentation are the four dashboards retail teams need most
- A first working dashboard ships in 6-10 weeks; a full retail data platform runs £15,000-£80,000+, depending on data-source count and complexity
- A data warehouse (BigQuery or Snowflake) underpins most builds, typically costing a few hundred dollars a month
Proof
- shipping production software across retail, commerce, and data
- Since 2015
- RaftLabs delivery record
- average client rating across delivered projects
- 4.9/5
- Clutch, verified reviews
- scope and cost agreed in writing before any development starts
- Fixed price
- Every RaftLabs engagement
Retail business intelligence only works when every number ties back to one source of truth. Most retail teams don't have that. Sales live in one export. Stock lives in another. Customer history sits in a third. The weekly margin review runs on whichever file someone pulled last. The decision gets made on partial data, and the gap shows up after the markdown or the stockout.
That gap is expensive. McKinsey found AI-driven demand forecasting can cut forecasting errors by 20% to 50% and lost sales from product unavailability by up to 65% (McKinsey & Company, 2021). None of that works without a clean data layer underneath. So we build the pipelines, warehouse, and data model first. Then the dashboards. In that order, the numbers your team acts on are correct.
Retail BI capabilities
What we build
- 01
Unified retail data warehouse
A central warehouse in BigQuery or Snowflake that pulls data from every system your retail operation runs: Shopify, POS, WMS, Google Analytics 4, Meta Ads, and loyalty platforms. Data arrives on your chosen schedule. Hourly for operational visibility, daily for management reporting. Once the warehouse is running, adding a new data source means connecting one more pipeline, not rebuilding the analytics layer from scratch.
- 02
Sales and margin analytics
Margin reporting by SKU, category, store, and channel. Net margin after cost of goods, shipping, returns, and discounts applied per line item. Sell-through rate by category and season so the commercial team knows which lines to mark down. Discount analysis that shows how much margin promotional pricing cost versus the volume it drove.
- 03
Inventory performance dashboards
Stockout prediction using sell-through rate and current stock on hand. Slow-mover identification showing which items have been sitting longer than the target holding period. Reorder point automation based on lead time, forecast demand, and safety stock target. For multi-location retailers, a store-by-store transfer opportunity view.
- 04
Store performance comparison
Side-by-side performance reports across all store locations: revenue against target, conversion rate, average basket size, units per transaction, and margin contribution. Like-for-like comparison that strips out new store openings. Store ranking by performance metric for franchised or multi-brand retail groups.
- 05
Customer segmentation for loyalty targeting
Customer segmentation built from purchase history, visit frequency, average spend, and category preference. RFM scoring to identify VIP, at-risk, and lapsed customers. Segment lists that export directly to your email marketing or loyalty platform. Loyalty programme analytics showing tier movement and redemption rate by reward type.
- 06
Custom retail dashboards
Dashboards built for the people who actually make decisions: the operations manager who checks stock levels every morning, the commercial director who reviews margin weekly, and the CEO who needs a one-page view. Built in Metabase, Power BI, Looker Studio, Tableau, or custom React dashboards embedded in your existing admin panel.
We haven't published a standalone BI dashboard case study yet. The closest proof is our retail-data pipeline work. One build put real-time operational analytics on top of a 40-plus-location operator's existing POS hardware. Another cleaned and validated receipt data for a supermarket chain. Same core problem: unify data from live systems, then make the team trust it.
Proof
Related work
- 01
Real-time analytics for a 40+ location retail operator
- transactions processed in a single day during testing
- 20K+
- 02
Retail data pipeline and OCR validation for a supermarket chain
- data validation accuracy, up from ~80%
- 99%
- 03
Grubly processes 10,000+ restaurant orders while saving owners $5,000 in third-party delivery commissions
Grubly lets restaurants own their online ordering channel. 10,000+ orders processed, $5,000 saved in delivery commissions in the first 15 days, and a direct customer relationship that third-party platforms do not allow.- transactions in first 15 days
- $ 10k+
- savings on food delivery commissions
- $ 5k
- zero to launch
- 12 weeks
Work with us
What retail decision can't your team make right now because the data isn't in one place?
Tell us your current systems and what the reporting gaps are costing you. We'll scope the BI build.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.
Common questions
A focused retail analytics build - 3-5 data sources, core dashboards for sales, inventory, and margin - typically runs £15,000-£35,000. A full retail data platform with customer segmentation, predictive stock alerts, and multi-location reporting runs £35,000-£80,000+. Maintenance and hosting costs depend on data volume and update frequency.
Shopify and Shopify Plus, Square, Lightspeed, Vend, WooCommerce, DEAR Inventory, Cin7, Unleashed, Brightpearl, custom ERP and WMS systems, Google Analytics 4, Meta Ads, and loyalty platforms including Smile.io and LoyaltyLion. We can connect to any system with an API or data export.
We build refresh cycles to match your reporting needs - hourly for operational dashboards (inventory levels, today's sales), daily for management reporting (margin, sell-through, week-on-week), and weekly or monthly for strategic reporting (customer LTV, seasonal trend analysis). Real-time streaming is available for high-volume retailers.
For most retail BI projects, yes. We typically use BigQuery or Snowflake as the central warehouse - data from Shopify, your POS, and other sources lands there, gets cleaned, and powers the dashboards. The cost is low for most retail data volumes (a few hundred dollars per month). We set up and manage the warehouse as part of the project.
Yes. We build in Metabase, Looker Studio, Power BI, Tableau, and Grafana. We also build custom React dashboards embedded in your existing admin panel when you want the analytics inside your product rather than in a separate tool. We recommend the right tool based on your team's existing setup.