Simulate a pricing change before you make it
Test a price increase, a new tier, or a subscription move against simulated customers, competitors, and press — and get an estimated-probability report in minutes.
MiroFish simulates how a pricing change will be received before you ship it. You describe the move — say, raising a plan from $29 to $39 — and the engine builds a knowledge graph of your segments, competitors, and channels, then runs hundreds of AI agents that react to the change and to each other across simulated social platforms. The result is a structured report: the most-likely reaction with an estimated probability, churn risk broken down by segment, the likely social narrative, competitor counter-moves, and recommended mitigations. It is decision support, not a guarantee — the probabilities are calibrated estimates, and it does not replace a real pricing test with live customers.
How MiroFish simulates it
A pricing simulation on MiroFish runs the same four-stage pipeline as any prediction, tuned to price sensitivity:
- 1
Describe the exact move and the tension
The specific change (amount, who it applies to, notice period) plus the trade-off that makes it hard — e.g. “we need the revenue but our biggest accounts are the most price-sensitive.” Attach a pricing-rationale doc (.txt/.md) to ground it.
- 2
Segment modeling
Your customer segments, competitors, and channels become interconnected agents. Price-sensitive and loyalty-driven cohorts are modeled separately, because they react in opposite directions.
- 3
Multi-round reaction
Agents complain, defend, compare alternatives, and churn (or don’t) over several rounds. Second-order effects surface — a review thread, a competitor’s “switch and save” offer, a press pickup.
- 4
Estimated-probability report
Churn risk by segment, the most-likely reaction narrative with an estimated likelihood, competitor moves to expect, a net-revenue direction, and mitigations ranked by impact — followed by a chat to interrogate any of it.
A worked example
A 4,000-customer B2B SaaS raises its Pro plan from $29 to $39/mo for existing customers, with 60 days’ notice, to fund support hiring.
What you give it
- Change
- +34% on Pro, existing customers
- Notice
- 60 days
- Segments named
- Solo users, agencies, enterprise
- Stated fear
- Agency churn
What the report estimates
- Most-likely outcome
- Contained backlash, net-positive revenue
- Highest churn risk
- Solo/price-sensitive segment
- Agency segment
- Lower churn than feared (switching cost)
- Top mitigation
- Grandfather annual plans; add a value note
Illustrative output. Real numbers depend on your data; treat percentages as directional estimates, not measurements.
What it can’t do (honest limits)
- —It estimates reactions, not exact churn or revenue figures — read percentages as “roughly,” never to the decimal.
- —It cannot see your private renewal data or contracts; the segment model is only as good as what you describe.
- —It will not predict an unrelated shock (a competitor’s own price cut announced the same week, a funding event).
- —It is not a substitute for a real price test on a live cohort — use it to decide which test is worth running.
Questions people ask
Will raising prices cause customers to churn?
A pricing simulation estimates churn risk by segment rather than giving a single number: price-sensitive cohorts usually show the highest risk, while loyalty- or switching-cost-bound segments often churn less than feared. MiroFish returns the most-likely reaction with an estimated probability plus the alternative scenarios, so you can plan for more than one outcome. It is a calibrated estimate, not a guarantee.
Can I test a price change before rolling it out?
Yes — you can simulate the change online in 5–10 minutes without touching real customers. You describe the exact move and the segments you care about, and MiroFish runs a multi-agent simulation of how those segments, competitors, and press react. It complements a live A/B price test; it does not replace one, because simulated agents are reasoning about your scenario, not real buyers making real payments.
What happens to my customers if I raise prices 20%?
The simulation returns a segment-by-segment reaction: which cohorts are most likely to leave, which absorb it, the likely social-media narrative, and any competitor counter-moves — each with an estimated likelihood. The report also flags the single change (e.g. grandfathering, longer notice) that most improves the outcome. Exact churn depends on your real data, so the figures are directional estimates.
How do I choose between two price points?
Run the two prices as separate simulations and compare the reports side by side — comparative predictions are sturdier than any single absolute number. At a few dollars per run, two simulations are cheaper than one wrong price. The comparison shows which price point triggers more churn in which segment and which nets more projected revenue, as estimates you can then validate.
Will competitors undercut me if I raise prices?
Competitor agents in the simulation can respond to your move, so the report surfaces the likelihood of a “switch and save” counter-offer and which of your segments it would target. This is an estimate of competitive behavior based on how such players typically act, not real-time intelligence on a specific competitor’s plans.
What is the churn risk of switching from one-time to subscription?
A subscription move is a reputation event, so MiroFish models it as a reaction rather than a spreadsheet: it estimates which user segments revolt, the review-bombing risk in the first weeks, and the narrative that spreads. You get a most-likely outcome with an estimated probability and mitigations (e.g. legacy-license grandfathering) ranked by impact.
Which customer segments are most price-sensitive?
When you name your segments in the prompt, the simulation reports which ones react most strongly to the price change and in what direction — typically flagging solo/low-usage cohorts as most sensitive and switching-cost-locked accounts as least. It is inferring sensitivity from the described behavior, so naming even two or three real specifics sharpens the result.
How do I predict backlash to a pricing-page change?
Describe the exact page change and where your audience gathers (subreddit, X, a forum), and the simulation estimates backlash intensity, which objections dominate, and whether it escalates or fades. The report includes an estimated probability for the most-likely reaction and a recommended response — useful precisely because pricing-page backlash is hard to predict from spreadsheets alone.
Can AI simulate customer reaction to a price change?
Multi-agent simulation is designed for exactly this: instead of extrapolating historical numbers, it models the actors — customers by segment, competitors, press — and how they react to a novel event. That is where statistical forecasting has no data. MiroFish returns calibrated probability estimates and the reaction narrative, framed as decision support rather than certainty.
Is it safe to raise prices on existing customers versus only new ones?
You can run both framings as separate simulations and compare. Raising prices on existing customers typically shows higher reputational risk than new-customer-only pricing; the report estimates the gap by segment and the mitigations that narrow it. Treat the comparison as directional — it tells you which approach is riskier and why, not the exact churn each would cause.
Go deeper on the method: read the full guide on the blog →
Run it on your own scenario
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