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: 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.

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.