Extract discovery insights

Findings: reconciliation discovery

Eight 40-minute calls with finance leads, August

Bottom line

These calls support further validation of a targeted reconciliation product, not yet a broad build decision. Several participants described substantial manual work, but “reconciliation” covers different problems. Adoption will depend on handling messy data and reducing switching effort—not just offering automated matching.

What we learned

1. Reconciliation is a material problem for some, not all. Five participants described current reconciliation pain: - Logistics: three or more days of an eight-day close, with missing bank-feed transactions checked against statements in Excel. - Retail: manual re-keying of card-statement PDFs and a shared spreadsheet used by four people; £14k of duplicates found last quarter. - Agency: intercompany balances take a day and a half to resolve. - Hospitality: processor payouts net of fees and refunds require manual reconciliation and hold up close. - Construction: reconciliation accounts for roughly 40% of close, driven by repetitive matching volume.

Two participants—SaaS and charity—said close was fine. The SaaS participant’s remaining frustration was receipt chasing, not reconciliation. The manufacturing participant primarily described a failed tool migration rather than quantifying current close pain.

2. There is no single, validated product scope yet. The examples span incomplete bank feeds, card-statement ingestion, intercompany balances, processor payouts and high-volume matching. These may require different integrations and workflows. Card reconciliation is plausibly adjacent to our spend-management product, but its evidence here comes from one call; we should not assume it represents the wider opportunity.

3. Trust and implementation are central adoption requirements. The construction controller wanted proof using their own messy data. The logistics controller expressed scepticism about promised time savings. The manufacturing CFO reported a four-month migration, lost matching rules and a return to spreadsheets; he would not switch again without migration support.

Together, these suggest that realistic-data trials, continuity of existing rules and migration assistance may matter as much as matching capability.

4. Willingness to pay remains unproven. One participant said saving two days would make a solution worthwhile, but gave no price or commitment. Another explicitly ruled out new spending this year. The duplicate-loss example indicates potential financial value, but does not establish recoverable savings or a purchasing budget.

How confident can we be?

• Moderate confidence that manual reconciliation creates meaningful pain for a subset of companies like these, and that trust and switching friction deserve early attention.
• Low confidence in market prevalence, the best initial segment, willingness to pay, achievable automation rates or expected close-time savings.

This is a small qualitative sample; recruitment and representativeness are unspecified. Evidence is self-reported, not verified through workflow observation or transaction data. The calls establish neither demand for one common solution nor demand specifically from our existing customers.

Recommended next step

Choose a narrow use case to test—not a general reconciliation platform. Observe workflows and trial matching on real, messy data with several similar customers. Measure manual effort removed, exceptions remaining and implementation burden, then test a priced pilot. Advance to a build decision only with repeatable evidence of value and credible adoption commitments.

Executive Summary

We interviewed eight finance leads across various industries (70–400 FTEs) to assess whether to build a reconciliation product.

The takeaway: A generic, standalone reconciliation tool is a high-risk bet with massive adoption hurdles. However, there is acute, high-value pain around messy, high-volume card and payment processor reconciliations—areas adjacent to our core spend-management platform.

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What We Learned

#### 1. Pain is driven by transaction complexity, not company size * High-pain profiles (FTEs 180–400): Companies with complex transaction streams (retail cards, payment gateway net-offs, intercompany transfers, dropped bank feeds) spend 30–40% of their month-end close (1.5 to 3+ days) manually ticking spreadsheets. * Low-pain profiles (FTEs <120): Smaller teams or digitally native businesses with modern ERPs report manageable 4-to-5-day closes. Their bottlenecks lie in chasing receipts, not reconciling balances.

#### 2. Buyers are deeply skeptical and burned by past tools * Migration trauma: One CFO spent four months migrating to a dedicated reconciliation tool, lost matching rules, and saw the team revert to Excel. He will not switch again without full migration support. * “Clean data” cynicism: Buyers repeatedly noted that vendors promise automated matching but fail on real-world messy data (e.g., processor fees netted against payouts, missing bank feed lines).

#### 3. Clear synergy with core spend management Retail and hospitality leads cited corporate cards and payment payouts as their primary close bottlenecks (e.g., one retail chain discovered £14k in duplicate card transactions via shared spreadsheets). Reconciling card spend naturally fits our existing product footprint.

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Confidence Assessment

Sample Limitations: Eight calls provide qualitative directional signals, but the sample spans very diverse business models (logistics, SaaS, retail, charity, construction). We lack statistical significance on pricing thresholds and specific ERP integrations.

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Product Recommendation

1. Do not build a generic, horizontal reconciliation platform. Competing against established ERPs and broad reconciliation players carries extreme migration friction.
2. Double down on native spend reconciliation: Scope a feature set within our existing platform focused on automated card matching and statement ingestion (e.g., parsing PDF card statements, catching duplicate charges).
3. De-risk with a messy-data PoC: Before committing to a major build, run a technical pilot with 2–3 design partners (such as the retail chain or logistics firm) to prove our matching algorithm handles their dirtiest statement data without manual rule configuration.