Customer Story
How a Mid-Market CPG Brand Recovers Trade-Deduction Revenue
September 10, 2025
80% fewer manual hours
≈$1.2M invalid deductions flagged annually


Industry: CPG
Use cases: Trade-promotion reconciliation, Deductions management, Accounts Receivable
Customer intro
A mid-market CPG brand (~$200M annual revenue) sells through national retailers and specialty grocers. A significant share of cash is tied to reconciling trade-promotion deductions and chargebacks from large distributors.
The problem
Every month, Accounts Receivable receives dozens of 50-100 page chargeback packets, each containing thousands of line items. AR clerks must manually match every transaction to promotion terms and invoices. This process is slow, deadline-sensitive, and prone to error.
Trade promotions are the second-largest line item on a CPG company’s P&L after Cost of Goods Sold, with expenses ranging from 15% to 30% of gross sales. Up to 20% of these deductions are invalid due to errors or duplicates. However, the time-consuming manual reconciliation prevents the team from researching and disputing these invalid claims within the required timeframe, leading to a direct loss of revenue. For a company of this size, unchecked deductions can amount to millions of dollars annually.
Baseline effort: two FTEs spending ~30 hours/week each on packet review and reconciliation.
The solution (built around their stack)
- Capture (Outlook → SharePoint). Chargeback PDFs arriving in a shared Outlook inbox are auto-ingested.
- Extract (cloudsquid). Line items (invoice, PO, SKU, qty, amount, reason code, promo ID, dates) are turned into rows at 99.9% field-level accuracy on the customer’s document set. Immediate results with no training required.
- Match (ERP + Excel view). Rows join to promotion contracts and invoices in a reconciliation Excel table for AR. ERP remains source of truth; Excel remains familiar subledger for human in the loop checks.
Validate & Flag. Rules detect missing promo IDs, out-of-window dates, rate mismatches, duplicates, and over-threshold amounts. Exceptions flow to an Exceptions Table and alerts to AR team to review flow through Outlook. Owners see only what needs action. - Dispute Pack. Likely invalid deductions have linked evidence packet (Excel + linked source pages).
- Report (Power BI). A dashboard tracks disputed vs. paid, win rate, aging, top reason codes, and cash recovered by customer/broker. CFO-level visibility and control.
Results
- Manual effort down ~80% — from ~30 hours/week across two FTEs to ~6 hours/week, focused on exceptions and disputes.
- ≈up to $1.2M/year in invalid or overcharged deductions flagged for recovery based on current monthly totals.
- 99.9% field-level accuracy enables reliable automation and fewer rework loops with less errors than the human team had before.
Working with Cloudsquid
Rollout was incremental to build initial trust, with rapid expansion in scope after initial 3 month test period: start with one distributor and a small rule set; expand across customers. Outlook, SharePoint, Excel, and Power BI remain the day-to-day tools. Cloudsquid automates the heavy lifting between them.
“We’re on the front foot now. The team spends time on disputes, not data wrangling and it’s showing up in significant cash back to the bottom line.”
- CFO, Mid-Market CPG Brand
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Book a demoAbout the Author

Mike McCarthy
CEO
Mike McCarthy, co-founder and CEO of cloudsquid, is building AI-driven infrastructure to automate and simplify complex document workflows. With deep experience in go-to-market strategy and scaling SaaS companies, Mike brings a proven track record of turning early-stage products into revenue engines. Before founding Cloudsquid, he led North American sales at Ultimate, where he built the GTM team, forged strategic partnerships with Zendesk, and helped drive the company through its Series A and eventual acquisition by Zendesk.
About the Reviewer

Filip Rejmus
Co-founder & CPO
Filip Rejmus, co-founder and Chief Product Officer at cloudsquid, is building infrastructure to help companies manage, scale, and optimize AI workflows. With a background spanning software engineering, data automation, and product strategy, he bridges the gap between AI research and building useful, friendly Products. Before founding Cloudsquid, Filip worked in engineering and data roles at Taktile, SoundHound, and Uber, and contributed to open-source projects through Google Summer of Code. He studied Computer Science at TU Berlin with additional coursework in Quantitative Finance at TU Delft and Computer Graphics at UC Santa Barbara.