Extract discovery insights

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.

Findings: month-end reconciliation

These calls justify testing a focused reconciliation proposition, but do not yet justify building a broad product. Six participants described reconciliation pain or dissatisfaction with their tools. However, their problems differ substantially, and evidence of willingness to adopt or pay is weak.

What we learned

Reconciliation can materially delay close, but it is not a universal problem. The logistics controller reported at least three reconciliation days within an eight-day close. The agency accountant spends 1.5 days on intercompany balances. The construction controller estimated reconciliation at 40% of close, while the hospitality controller said processor payouts are the final blocker.

Conversely, the SaaS and charity participants explicitly said close works well. SaaS’s remaining frustration was receipt chasing, which should not be counted as reconciliation demand. These counterexamples argue against targeting all finance teams simply because they run a monthly close.

“Reconciliation” covers several distinct jobs.

These may require different data integrations, matching logic and workflows. Combining them into one demand signal would overstate support for any particular product.

Spreadsheets persist for different reasons. Logistics uses them to compensate for feed gaps; retail uses them to coordinate work; manufacturing returned to them after a failed tool migration. Spreadsheet use alone does not establish a desire to replace spreadsheets.

Trust and migration could determine adoption. The construction controller wants proof using messy, real data. The logistics controller is sceptical of promised savings. The manufacturing CFO made migration assistance a condition of switching after losing matching rules during a four-month migration. These are specific adoption requirements, although we cannot assume every buyer shares them.

How confident can we be?

Moderate confidence that meaningful problems exist in some of these organisations. Participants supplied concrete workflows, time estimates and one financial example. These remain self-reports: we have not observed the work or verified the figures. The £14k in duplicates does not establish losses incurred or savings our product could deliver.

Low confidence about prevalence, the best segment or a common solution. Eight exploratory calls provide useful hypotheses, not market estimates. Recruitment criteria and customer relationships are unspecified. Each industry appears only once, so differences cannot reliably be attributed to sector, company size or ERP maturity.

Very low confidence about commercial demand. One participant gave a conditional value statement after being asked about price, without naming a budget or committing to buy. Another explicitly ruled out new spending this year. No purchase, pilot commitment or switching decision is evidenced.

Implication for the product decision

Prioritise validation of card-spend reconciliation, given its proximity to our existing spend-management product, while recognising that only one call directly supports this use case.

Recruit more teams with that workflow, inspect actual statements and reconciliation records, and test a narrow prototype on messy data. Measure time saved, incorrect matches and exception-handling effort. Seek concrete pilot commitments and establish budget ownership and migration requirements before committing to build.

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.