ProblemsWe Have Too Many Products › Fintech

We Have Too Many Products
in Fintech

Proliferation costs are real, mostly invisible, and land on the products that were paying for everything. This page works through it for fintech companies specifically — including an unedited excerpt from a real analysis of a fintech.

The short answer

Proliferation costs are real, mostly invisible, and land on the products that were paying for everything. The version of this question that applies to fintech companies is not the generic one. Lending fixed the P&L and converts revenue worth a 7x multiple into revenue worth a 2x multiple — so an answer that ignores blended take rate will be confidently wrong. The analysis has to start from charge-off rate and contribution margin rather than from revenue.

Product lines accumulate because each addition is individually justifiable and nothing is ever removed. The cost is not in any one of them; it is in the complexity they collectively impose — inventory, changeovers, support knowledge, sales attention, forecasting error.

That cost is borne disproportionately by the profitable core, because that is where the capacity being fragmented lives. Which is why rationalisation often increases total profit even when the removed lines were nominally contributing.

The analysis worth doing ranks lines by contribution against the constraint they consume, then asks which of the tail exists for a reason — a strategic customer, a channel requirement — and which exists because nobody has looked.

How to tell this is actually your problem

These three together are the signature. One on its own usually points somewhere else.

✓ A minority of lines produces the large majority of revenue
✓ Nothing has been discontinued in several years
✓ Operations complexity is rising faster than volume

The move that usually makes it worse. Cutting the tail by revenue rank alone, which removes lines that were cheap to carry and keeps ones that quietly consume the constraint.

Who this is for — and who it is not

It is for you if you run or finance a fintech and a minority of lines produces the large majority of revenue. It is the situation where the numbers are available but nobody has put them in an order that produces a decision.

It is not for you if Percision is the wrong tool if you already know the answer and only need execution capacity, or if the business is pre-revenue — then the constraint is evidence about the market, not analysis of your own figures. Percision is not a lawyer, tax advisor, auditor, licensed appraiser, clinical or regulatory filer, or an AI implementation shop. It does not do HR casework, creative-only brand work, or impersonate a named consulting firm. It is a strategy analysis engine — not a template library. Also wrong if you need facilitation, politics, or someone to sit with a lender or buyer. Those are human jobs.

Percision is not a lawyer, tax advisor, auditor, licensed appraiser, clinical or regulatory filer, or an AI implementation shop. It does not do HR casework, creative-only brand work, or impersonate a named consulting firm. It is a strategy analysis engine — not a template library.

What this looks like when the analysis is actually run

Below is an excerpt from a real run of this analysis on a fintech. It is a sample profile rather than a customer, and it is unedited engine output — this is the format you get, on your own numbers.

The subject is Verrano Pay, a sample company profile used for testing rather than a customer — $84M net revenue, 28,000 merchants, $9.4B of payment volume.

Excerpt from a real Percision run · Cost Reduction & Efficiency · sample company profile

The move. Scale lending book from $110M to $260M advances using existing distribution and data assets while maintaining charge-off rate below 9.0% covenant.

The leak it closes. Reduces 26% partner rev-share leakage by increasing merchant stickiness through lending relationship

The assumption it rests on. Platform partners maintain 180-day termination clauses without exercising exit — the engine put the probability at 0.7.

What the run committed to
Investment required$0 incremental equity
Expected return4.5x
Revenue, year 1$24.1M lending revenue (30% growth)
Revenue, year 2$31.3M lending revenue (30% growth)
Revenue, year 3$40.7M lending revenue (30% growth)
Exit criteriaTerminate if charge-off rate exceeds 8.7% for two consecutive quarters OR if any platform partner terminates contract

This is one move out of a full analysis. Read a complete report — every page, no email required.

What the engine does with this question

This question routes to Matrix Strategy, one of 29 engagements the platform runs. For fintech companies it works through blended take rate, charge-off rate, contribution margin and CAC by channel, then produces the sequence rather than a list of options — which move first, what it funds, and the observation that would say the sequence is wrong.

You watch the analysis get built before paying anything. Read a complete report here if you would rather see the depth first.

Questions people ask about this

How do I decide what to discontinue?

Contribution per unit of the binding constraint, then a check on strategic dependencies. Revenue rank alone gets this wrong in both directions.

Will customers leave if I discontinue products?

Some will, and the analysis should price that before the decision rather than after. Usually the revenue at risk is smaller than the complexity cost being removed, but it should be a finding rather than an assumption.

How much complexity cost is normal?

It is rarely tracked, which is why it grows. A workable proxy is the trend in operating cost per unit of volume; when that rises while volume rises, complexity is the usual explanation.

Is this different in fintech than in other industries?

Materially, yes. Lending fixed the P&L and converts revenue worth a 7x multiple into revenue worth a 2x multiple — which changes both the diagnosis and the order of the fixes. The metrics that decide it here are blended take rate, charge-off rate, contribution margin, and an answer built on industry-general benchmarks will usually point at the wrong one first.

What data do I need before this analysis is worth running for a fintech?

Less than most people expect. Your last twelve months of revenue and cost split the way you already split it, plus whatever you hold on blended take rate and charge-off rate. The analysis is explicit about what it is assuming where your data stops, which is more useful than waiting for numbers you may never have.

When is Percision the wrong tool?

Percision is the wrong tool if you already know the answer and only need execution capacity, or if the business is pre-revenue — then the constraint is evidence about the market, not analysis of your own figures. Percision is not a lawyer, tax advisor, auditor, licensed appraiser, clinical or regulatory filer, or an AI implementation shop. It does not do HR casework, creative-only brand work, or impersonate a named consulting firm. It is a strategy analysis engine — not a template library. Also wrong if you need facilitation, politics, or someone to sit with a lender or buyer. Those are human jobs.

Does Percision replace a lawyer, tax advisor, auditor, or AI implementation team?

Percision is not a lawyer, tax advisor, auditor, licensed appraiser, clinical or regulatory filer, or an AI implementation shop. It does not do HR casework, creative-only brand work, or impersonate a named consulting firm. It is a strategy analysis engine — not a template library.

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