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An MRR forecast you can defend in front of the board
Stop building the MRR waterfall in Excel every quarter. AnalityQa AI reads your subscription system, decomposes net new MRR into new, expansion, contraction, and churned, and projects ARR forward with confidence bands.
→MRR numbers in the founder's deck, the CFO's spreadsheet, and Stripe's dashboard never quite match — usually because each one defines "net new" differently.
→The MRR waterfall (new + expansion − contraction − churned) is a stitched-together pivot table that breaks every time a new plan launches.
→Annual contracts paid up-front need to be normalised to a monthly recognition basis to land in the MRR series — a step that's often skipped or done inconsistently.
→Forecasting next-quarter MRR requires combining trailing 90-day momentum, sales pipeline, known renewals, and seasonality — work that takes a finance analyst a week and is out of date the day it's delivered.
Why the usual approach breaks down
Stripe's reports show MRR but not the components you need
Stripe's revenue dashboard shows aggregate MRR. It does not break out expansion from contraction at the customer level, does not handle annual prepayments uniformly, and does not let you slice by plan, geography, or acquisition channel. Anything beyond the headline number requires exporting and recomputing.
Annual contracts distort monthly views
A customer who signs a $12,000 annual deal on January 1 is delivering $1,000/month of MRR — not a $12,000 spike in January and zero for the rest of the year. Most homegrown MRR tracking gets this wrong, especially at year-end when annual contract volume spikes.
Plan changes need to be classified, not just counted
An upgrade from Starter to Pro is expansion MRR. A downgrade is contraction. A cancellation is churn. Pause-to-resume sequences are not churn. Each of these classifications affects the waterfall and the forecast — and they live in subscription event logs that take real query work to pull cleanly.
A forecast without a confidence range is just a guess
Boards want a point estimate AND a range. Most internal MRR forecasts are a single number with no statistical basis — what the founder thinks plus what the head of sales hopes. Producing a range requires either a time-series model or a Monte Carlo on the moving parts, neither of which most finance teams have set up.
How AnalityQa AI AI solves it
Upload your data — or connect it live — and ask in plain English.
01
Connect Stripe, Chargebee, or your billing database
AnalityQa AI reads from Stripe via CSV export or directly from a Postgres replica of your billing system. The schema is auto-detected; subscription events (created, updated, cancelled, paused) are mapped to MRR movements automatically. You can also blend with a CRM export to add commercial context (acquisition channel, AE, segment).
02
Normalised MRR with annual contracts handled correctly
Annual prepaid contracts are amortised across 12 months by default, so January no longer has a phantom spike. Multi-year deals are handled the same way. You can override the recognition rule per contract type if your finance policy is different.
03
Full waterfall: new, expansion, contraction, churned
Ask "Show me the MRR waterfall for the past 12 months" and AnalityQa AI returns each component as its own series, plus the aggregate net new MRR line. Drill into any month to see the customer-level events that contributed to that month's number.
04
Forward projection with confidence bands
AnalityQa AI projects MRR 30, 60, and 90 days forward using your trailing momentum and historical seasonality. The chart shows a central line with a 90% confidence band. You can layer in known committed pipeline or scheduled renewals to refine the forecast — those become explicit assumptions that the board can challenge.
05
Investigation when a month deviates from forecast
When actual MRR misses the forecast, ask "Why was March MRR 4% below the projection?" AnalityQa AI runs an investigation across the contributing components, returns a written diagnosis (e.g. "contraction came from 6 specific accounts; new MRR was on plan"), and lists the customers involved.
You askedGenerated in 4.2s
"Show me the MRR waterfall for the past 12 months — new, expansion, contraction, churned, and net new."
Revenue
€1.42M+12.4%
Gross margin
58.2%+1.8pp
Burn rate
€84k/mo−6.1%
Stacked bar + line: MRR components by month
Last 12 mo
Forecast line with confidence band
Bar chart: new MRR by channel + line for trend
A dashboard built in AnalityQa AI — from question to chart, no SQL.
Real examples
Paste your data. Ask. Ship.
You
Show me the MRR waterfall for the past 12 months — new, expansion, contraction, churned, and net new.
AI
Net new MRR averaged $42K/month over the past 12 months. New MRR contributed $31K average; expansion $18K; contraction −$4K; churn −$3K. November had the strongest expansion (+$28K) driven by 4 enterprise upgrades. February had the weakest, at $19K net new.
Stacked bar + line: MRR components by month
You
Forecast MRR for the next 90 days based on the last 12 months of momentum.
AI
Projected MRR is $1.34M (May), $1.39M (June), $1.43M (July). The 90% confidence band is ±$32K — wider in July because of the longer projection horizon. The forecast accounts for the seasonal dip you've seen in June over the past two years.
Forecast line with confidence band
You
Break out MRR growth by acquisition channel for the past 6 months.
AI
Paid acquisition contributed 38% of new MRR, organic 31%, partner referrals 19%, outbound 12%. Paid is plateauing (flat the past 3 months) while organic is accelerating. Expansion MRR comes disproportionately from outbound-acquired accounts.
Bar chart: new MRR by channel + line for trend
You
Why was March MRR 4% below the forecast?
AI
March net new MRR was $40K vs forecast $42K. The gap came from contraction: 3 enterprise accounts downgraded plans in March totalling $11K MRR loss. New MRR was on track. None of the 3 downgrades were flagged in CS health scores at the time.
Investigation report with account-level breakdown
You
What happens to ARR if our churn rate moves from 1.8% to 2.5% over the next 12 months?
AI
Holding new and expansion constant, ARR at month 12 falls from a projected $19.2M to $17.6M — a $1.6M gap. The compounding effect kicks in around month 8 as the cohort base shrinks faster.
Two-line scenario chart: baseline vs stressed churn
What teams get out of it
✓MRR waterfall available on demand instead of recomputed quarterly.
✓Forecast comes with a defensible confidence range, not a hopeful single number.
✓Annual contracts no longer distort monthly views.
✓Forecast misses get explained the same day, by named accounts and components.
Frequently asked questions
Does AnalityQa AI handle annual contracts and one-time fees correctly?+
Yes. Annual prepaid contracts are amortised across the contract length by default, so they contribute $X/month to MRR rather than a single spike. One-time fees are excluded from MRR by default. Both rules are configurable — tell the chat your finance policy and it will be applied.
What data sources can it read?+
Stripe CSV exports, direct Postgres connections to your billing database, Chargebee exports, and HubSpot deal/subscription exports are the most common. AnalityQa AI auto-detects the schema and maps it to MRR concepts. CSV uploads work side-by-side with live database connections.
How are pause/resume sequences classified?+
Paused subscriptions are excluded from active MRR but not counted as churn. If a paused subscription resumes within 60 days, it returns to MRR without being double-counted as new. The 60-day threshold is configurable.
Can it forecast scenarios — what if churn rises, what if a new plan ships?+
Yes. Layer a hypothesis on top of the baseline forecast: "What if churn rises to 2.5%?" or "What if we close $40K of pipeline in May?" AnalityQa AI runs the scenario and shows the two lines on the same chart so you can quantify the gap.
How does it handle multi-currency revenue?+
Each subscription is normalised to a single reporting currency (your default). The exchange rate used is configurable — point-in-time, period-average, or fixed. The same normalisation is applied to historical and forward-looking views so the trend is consistent.
Will the MRR number match what Stripe shows?+
It will be close but not always identical, because Stripe's headline MRR uses its own definitions (which include pending charges and certain trials). AnalityQa AI shows you the SQL behind the number so you can reconcile. The most common gap is annual amortisation policy.
Can the board see this forecast directly without a license?+
Yes. Pin the forecast to a dashboard and share the public link — the board, your investors, your bookkeeper open the URL and see the live numbers. No login, no AnalityQa AI account required for viewers.
