Extract discovery insights

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.

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.

Findings summary

The pain is real, but it is not universal—and it is concentrated in specific reconciliation workflows. Five of the eight interviewees described reconciliation as a meaningful close burden. Their problems were concrete: dropped bank-feed transactions and manual matching (Call 1); high-volume card statements re-keyed from PDFs (Call 3); intercompany balances (Call 5); processor payouts obscured by fees and refunds (Call 7); and repetitive matching at scale (Call 8). One reported finding £14k in duplicate transactions. These examples suggest the opportunity may be less “speed up month-end close” and more “handle particular messy, high-volume matching jobs reliably.”

Company context seems to matter.¹ The clearest pain came from organisations with volume, multiple entities, many cards, or complex payment flows. Two interviewees said close was fine: one had a well-established ERP and mainly struggled with receipts; another was small, knew every transaction, and had frozen budgets. A reconciliation product is therefore unlikely to be equally valuable to every mid-sized company.

Trust and implementation are central adoption barriers. One finance leader regretted a tool switch after a four-month migration, lost matching rules, and a return to spreadsheets. Another would not trust automation until it worked on messy real data. A product that matches well in a demo may still fail if it cannot preserve existing rules, handle exceptions, and prove accuracy on customers’ own data.

Willingness to pay is unproven. One controller said saving two days could justify the cost, but explicitly expressed scepticism based on past promises. Another prospect would not buy this year. These calls reveal value hypotheses, not validated demand or pricing.

Implications for the product team

Explore a narrow, workflow-led wedge rather than assuming a broad reconciliation platform is needed. Card-spend reconciliation may fit our existing product particularly well; processor payouts and intercompany are other distinct opportunities, but may require different data and capabilities. Test which segment has both acute pain and access to usable data. Prioritise messy-data performance, explainable matches and exceptions, and low-friction migration or coexistence with spreadsheets.

Confidence

• Moderate confidence that reconciliation is a significant pain for some complex, high-volume finance teams: several independent, specific examples support this.
• Low-to-moderate confidence on which workflow or customer segment is the best product wedge: each use case appears in few calls.
• Low confidence on market-wide prevalence, willingness to pay, achievable time savings, or product feasibility: eight qualitative calls cannot establish these, and no prototype or pricing was tested.

Treat this as a reason to run targeted workflow and data-validation research—not yet as evidence to commit to a broad build.