Extract discovery insights

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.

Product Findings: Month-End Close & Reconciliation Opportunities

We spoke with eight finance leads at mid-sized companies (70–400 staff) to evaluate appetite for a reconciliation product. Here is what we learned, followed by our confidence assessment.

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What We Learned

#### 1. Pain is Acute, but Fragmented by Niche Reconciliation is universally recognized as a bottleneck¹, but the root cause varies heavily by business model: * Retail, Hospitality & Construction (High Volume/Messy Data): Companies dealing with multiple store cards (Call 3), complex payment processor payouts (Call 7), or high-volume line items (Call 8) spend up to 40% of their close cycle manually matching data in shared spreadsheets or re-keying PDF statements.³ * Agencies & Multi-Entity (Intercompany): Multi-entity structures suffer from recurring intercompany balancing discrepancies that defy easy automation⁴ (Call 5). * Logistics (Basic Bank Feeds): Basic bank feed drops force manual tick-and-bash routines in Excel (Call 1).

#### 2. Adoption Barriers and Skepticism Run High Even where pain is severe, market entry is fraught with buyer resistance: * Migration PTSD: Switching costs are massive. One CFO noted a four-month migration failure that destroyed custom matching rules and drove staff back to spreadsheets, refusing to switch again unless migration is fully managed (Call 4). * Deep Trust Deficit: Finance leads have been burned by over-promised automation and pristine demo environments. They demand proof on their own messy data before believing claims (Calls 1 & 8). * Budget Freezes & Apathy: Smaller teams (70–95 staff) with clean ERP setups or frozen budgets report no pressing need for new point solutions (Calls 2 & 6).

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Confidence Assessment: Low-Moderate (Conditional)

• Problem Validation (High Confidence): We are highly confident that reconciliation is tedious, manual, and a major driver of close delays in high-volume, multi-card, or multi-entity businesses.
• Commercial Viability (Low-Moderate Confidence): Despite the pain, we cannot rely on a self-serve, software-only motion.² High switching friction, deep skepticism toward AI/automation demos, and strict migration/implementation requirements mean buyers will churn or bounce without hands-on onboarding.

#### Recommendation Do not build a generic, self-serve reconciliation tool. If we proceed, the product must include a heavy-touch onboarding/migration service to overcome migration PTSD, alongside a "sandbox" feature allowing prospects to test matching rules on their actual messy data before purchasing.

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.