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.

Reconciliation discovery: findings from 8 finance-lead calls (August 2026)³

Bottom line: Most of the finance leads we spoke to have real reconciliation pain. But it shows up as several different problems, not one, and the biggest barrier to adoption is distrust and switching cost. We have almost no evidence on willingness to pay. This justifies more targeted discovery. It doesn't yet justify a decision to build.

What we learned

1. The pain is common but not universal. Six of eight say reconciliation is a significant part of close (Calls 1, 3, 4, 5, 7, 8).¹ The two who said close is fine (2, 6) run the smallest companies, at 95 and 70 staff, and one of them has a modern ERP. That hints at a size threshold around 100+ staff, but it rests on two data points.

2. Where people gave numbers, the cost is large. - About 3 of 8 close days (1) - 1.5 days a month (5) - About 40% of close (8) - £14k of duplicates found in one quarter (3)

All of these are self-reported.

3. "Reconciliation" is at least five separate problems. Each call with pain named a different source: - Bank feed dropping transactions (1) - Card statements arriving as PDFs and re-keyed into a shared spreadsheet (3) - Intercompany balances that don't agree across entities (5) - Payment-processor payouts that net off fees and refunds, so nothing matches the bank line (7) - Sheer volume of repetitive matching (8)

No two calls named the same source. One "reconciliation product" would have to handle different matching logic for different segments. Card spend (3) sits closest to what we already do and could be a natural starting point, but it came up in only one call.

4. Switching cost and skepticism are the main barrier.² - Call 4 switched reconciliation tools last year. Migration took four months, they lost their matching rules, and half the team drifted back to spreadsheets. He won't switch again unless someone else does the migration. - Call 8 won't believe a tool works until it handles their messy data, because demos always use clean data. - Call 1 has "heard that before."

That's three of eight, including people with heavy pain. Done-for-you migration and proof on the customer's own data look like requirements, not differentiators.

1. One adjacent signal. Call 2's only complaint was chasing people for receipts. That's our existing spend-management territory, not reconciliation.

How confident can we be?

Moderate confidence: - Reconciliation takes a meaningful share of close time for mid-sized finance teams. - The underlying causes vary widely between companies. - Buyers are wary of switching tools and of vendor demos.

Low or no confidence: - Willingness to pay. Only one person answered the question, and only conditionally. One has a budget freeze. - Which problem to build for. Each pain source appears exactly once. - Size threshold. It rests on two calls. - Prevalence. We don't know how these eight were recruited, whether they're customers, or how representative they are.

Method limitations: - Eight calls, one interviewer, one month. - We're working from excerpts chosen as "relevant," so the full transcripts may shift the emphasis. - The pay question was open-ended and hypothetical.

What would raise confidence before a build decision

• Recruit 8–10 more finance leads at companies with 150–400 staff. Screen them by pain source so we learn which problem repeats.
• Test the card-spend starting point with existing customers. Ask how they reconcile card spend today. We may already hold the data needed to solve it.
• Test willingness to pay concretely. Use price anchors or offer a paid pilot rather than asking an open question.
• Run a matching test on one prospect's real, messy data. Call 8 set that bar, and it's the test that would convince the skeptics.

Month-end close discovery: findings summary

Bottom line

Reconciliation is a real, recurring pain for mid-sized finance teams, but these eight calls don't yet show which product to build or whether anyone will pay for it. I'd treat this as grounds for a further, more targeted round of research, not a build decision.

What we learned

1. Five of eight leads have a real reconciliation problem (calls 1, 3, 5, 7, 8). Two said close is fine (calls 2 and 6), and one has a tool and regrets it (call 4).

2. They describe different problems, not one. - Bank feeds dropping transactions, matched by hand in Excel (call 1) - Card statements arriving as PDFs and re-keyed, with £14k of duplicates in a quarter (call 3) - Intercompany balances that never agree, costing about 1.5 days (call 5) - Payment processor payouts that net off fees and refunds, and are always the last thing holding up close (call 7) - Sheer matching volume, about 40% of one controller's close (call 8)

No problem appears in more than one call, so we can't yet say which is most common or most valuable.

3. The time cost is meaningful but self-reported. Estimates ranged from 1.5 days to 3 of 8 days. These are recollections, not measurements.

4. Those who are fine tend to be smaller or better equipped. The two "close is fine" leads are the smallest companies in the sample (70 and 95 staff), and one already has a modern ERP. This is suggestive at best with eight calls. The pain sits mostly in the 180–400 staff range.

5. Adoption barriers are as prominent as the pain. - Call 4's failed migration (four months, lost matching rules, team back on spreadsheets) is a warning about switching costs. He would only switch again if someone else did the migration. - Call 8 won't believe automation works until he sees it handle messy data: "Every demo uses clean data." - Call 1 is sceptical: "I've heard that before."

6. Willingness to pay is essentially untested. Only call 1 gave a conditional answer ("if it gave me two days back, it pays for itself"), and she hedged it. Call 6 has a budget freeze. No one expressed urgency or asked about pricing.

7. An adjacent finding: call 2's only complaint was chasing receipts, which sits closer to our existing spend-management product than reconciliation does.

How confident can we be?

Other limitations: - Eight calls is a small sample, and we don't know how participants were recruited, so existing customers or those already interested may be overrepresented. - The excerpts are edited and one interviewer ran all the calls. - Hypothetical questions ("would you pay?") overstate real intent.

Suggested next steps

1. Pick a wedge. Card reconciliation (call 3) is closest to our current product. Follow up with calls 3, 5 and 7 to size how often each problem occurs and what it costs.
2. Test with real data. Ask call 8 and similar leads for anonymised, messy samples to see whether matching is feasible.
3. Design for migration. Call 4 suggests onboarding and preserving matching rules may decide adoption. Treat this as a product requirement, not an afterthought.
4. Test price and urgency. Use concrete pricing or a paid pilot, not hypotheticals.
5. Widen the sample. Include firms of 150–500 staff and a mix of ERP and non-ERP setups, and record how each participant was recruited.