Retail & E-Commerce · Proposed Use Case
A proposed engagement to eliminate overselling, reduce stockouts by 40%+, and deliver real-time margin visibility across online, in-store, and wholesale channels.
The challenge
This multi-channel retailer operates across a Shopify storefront, two physical retail locations, and a growing wholesale arm - yet each channel maintains its own inventory count. Stock levels are reconciled manually at the end of each day, meaning that by mid-afternoon the online store regularly advertises products that have already sold out in-store. Overselling has become a persistent customer-experience problem, generating refund requests, negative reviews, and eroding brand trust.
Margin analysis is equally fragmented. The finance team pulls Shopify sales data, cross-references it with Cin7 warehouse costs, and then manually maps everything into Xero. True per-product, per-channel profitability is only visible weeks after the fact, making it nearly impossible to react to underperforming SKUs or shifting supplier costs in a timely manner. Promotional pricing decisions are made on gut feel rather than real margin data.
The disconnect extends to Stripe payment reconciliation. Refunds, partial payments, and multi-currency transactions create discrepancies between what Shopify reports as revenue and what actually lands in the bank. The operations team spends hours each week chasing down variances, time that would be far better spent on growth initiatives.
The solution
DataSpec would create a unified inventory and margin intelligence layer that connects Shopify, Cin7, Xero, and Stripe into a single, continuously synchronised data model. Every sale, restock, transfer, and return across all three channels would feed into one authoritative inventory count - updated in near real-time rather than via end-of-day batch reconciliation.
On the margin side, DataSpec would map each SKU's landed cost from Cin7 against its selling price in each channel, automatically accounting for Stripe processing fees, promotional discounts, and shipping surcharges. The result would be a live per-product, per-channel margin dashboard that surfaces underperformers and highlights opportunities without requiring a single spreadsheet.
Payment reconciliation would also be automated. DataSpec would match every Stripe transaction - including refunds, disputes, and currency conversions - back to the originating Shopify order and the corresponding Xero ledger entry. Discrepancies would be flagged automatically rather than discovered manually days or weeks later.
Expected results
With a unified inventory model in place, the retailer would be expected to eliminate overselling entirely and reduce stockouts by more than 40%. Real-time stock visibility across channels would allow the operations team to trigger replenishment orders proactively, rather than reacting to out-of-stock alerts after customers have already been disappointed.
Instant margin visibility would empower the merchandising team to make data-driven decisions about promotions, pricing, and product mix. Underperforming SKUs would be identified in days rather than weeks, and the finance team would reclaim the hours currently spent on manual reconciliation - redirecting that effort toward strategic planning and supplier negotiation.
The automated Stripe-to-Xero reconciliation alone is expected to save several hours per week of manual effort, while dramatically reducing the risk of undetected revenue leakage from unmatched refunds or disputed transactions.
See yourself in this story?
Whether you're ready to connect or just exploring, we'd love to understand your data challenges.
More case studies
← All case studies