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Blog›Finance / FP&A

Revenue Recognition and ARR/MRR Analysis with AI

Connect your billing system or upload a subscription export. Ask AnalityQa AI for an ARR waterfall, an MRR movement bridge, or a deferred revenue aging schedule — and get a presentation-ready output in seconds.

Try AnalityQa AI AI free →See live examples
Finance team analyzing charts

The problem

  • →ARR waterfall reports require pulling data from the billing system, the CRM, and the GL, then manually reconciling discrepancies before any chart can be built.
  • →MRR movement analysis — new, expansion, contraction, churn — is rebuilt from scratch each month because the source data has no consistent categorization of change type.
  • →Deferred revenue schedules are maintained in Excel and drift from the GL balance whenever contract amendments or early terminations are processed mid-period.
  • →Revenue from new contracts, upsells, and renewals is booked in the ERP without clean segmentation, making it impossible to answer 'how much of this month's revenue came from expansion?' without a manual lookup.

Why the usual approach breaks down

Billing data and GL data rarely agree without manual reconciliation

Stripe, Chargebee, and similar billing systems recognize revenue on their own schedule. The GL entry from your accountant may differ due to adjustments, credits, or timing. Reconciling the two before producing a waterfall takes significant manual effort.

MRR movement categorization requires business logic, not just data

Classifying a customer change as expansion versus a new contract versus a reactivation depends on rules specific to your billing model. Excel models hard-code these rules in formulas that break when the billing system changes a field name.

Deferred revenue schedules are fragile in Excel

A deferred revenue amortization schedule built in Excel requires a row per contract, a column per period, and manual updates for every amendment. At 500+ contracts, the workbook becomes unmaintainable.

ASC 606 / IFRS 15 compliance requires detailed documentation

Auditors want evidence of performance obligations, transaction price allocation, and recognition timing. Producing this evidence from a manual Excel model requires reconstructing the logic from scratch during each audit cycle.

How AnalityQa AI AI solves it

Upload your data — or connect it live — and ask in plain English.

01

Upload your billing export or connect directly

AnalityQa AI accepts exports from Stripe, Chargebee, Recurly, and similar billing platforms, or any subscription CSV with customer, contract value, start date, and end date. It also connects directly to your GL or ERP for reconciliation.

02

Ask for waterfall and movement analysis in plain language

Type 'Show me the ARR waterfall for Q1' or 'Break down MRR movement for March by new, expansion, contraction, and churn.' AnalityQa AI applies the categorization logic automatically and builds the chart.

03

Auto-join billing with actuals for reconciliation

AnalityQa AI detects when you have both a billing export and a GL export and flags any contracts where recognized revenue in the billing system does not match the GL posting — surfacing reconciling items before the period closes.

04

Generate deferred revenue schedules on demand

Upload your contract data and ask 'Build a deferred revenue aging schedule through December.' AnalityQa AI calculates the amortization for each contract, aggregates by period, and flags contracts with unusual recognition patterns.

05

Export audit-ready detail

Every revenue recognition output includes the underlying contract-level detail as a second tab — with source, recognition logic applied, and period-by-period amortization — ready for an auditor or a board package.

You askedGenerated in 4.2s

"Show me the ARR waterfall from January through March, broken down by new, expansion, contraction, and churn."

Revenue

€1.42M+12.4%

Gross margin

58.2%+1.8pp

Burn rate

€84k/mo−6.1%

Waterfall chart showing opening ARR, new bookings, expansion, contraction, churn, and closing ARR for each month in the quarter, with dollar values labeled on each bar.

Last 12 mo

Bridge chart of MRR movement from February to March closing balance, with a supporting table listing the top five customers in each movement category.

Bar chart of monthly deferred revenue release over the next 12 months, with a table showing the top 20 contracts by remaining deferred balance.

A dashboard built in AnalityQa AI — from question to chart, no SQL.

Real examples

Paste your data. Ask. Ship.

You

Show me the ARR waterfall from January through March, broken down by new, expansion, contraction, and churn.

AI

AnalityQa AI reads your subscription data, classifies each customer change by movement type using your billing rules, and builds a waterfall from opening ARR to closing ARR for each month.

Waterfall chart showing opening ARR, new bookings, expansion, contraction, churn, and closing ARR for each month in the quarter, with dollar values labeled on each bar.
You

Break down MRR movement for March — what drove the net change?

AI

AnalityQa AI identifies every customer with a contract change in March, categorizes each change, and ranks the top contributors to each movement bucket.

Bridge chart of MRR movement from February to March closing balance, with a supporting table listing the top five customers in each movement category.
You

Build a deferred revenue aging schedule showing what will be recognized over the next 12 months.

AI

AnalityQa AI reads your active contract data, calculates monthly recognition amounts per contract based on start/end dates and contract value, and aggregates by period.

Bar chart of monthly deferred revenue release over the next 12 months, with a table showing the top 20 contracts by remaining deferred balance.
You

Which customers had revenue recognized this quarter that does not appear in the GL?

AI

AnalityQa AI joins your billing export and GL extract on customer and period, then surfaces rows where billing-system recognized revenue has no matching GL entry within a 5% tolerance.

Table of reconciling items with customer name, billing-system amount, GL amount, variance, and a suggested reconciliation note for each.

What teams get out of it

✓Monthly ARR waterfall preparation drops from two days to under 30 minutes.
✓Deferred revenue schedules stay current automatically rather than being rebuilt each quarter.
✓Revenue reconciliation between billing and GL happens within the period, not during close.
✓Audit evidence for ASC 606 / IFRS 15 is generated on demand from the same data used for management reporting.

Frequently asked questions

Which billing systems does AnalityQa AI connect to?+

AnalityQa AI has direct connectors for Stripe, Chargebee, and Recurly. For other billing platforms, you can export a subscription CSV with customer, contract value, start date, and end date — AnalityQa AI handles the rest. GL data can be pulled from NetSuite, QuickBooks, or uploaded as a CSV.

How does AnalityQa AI categorize MRR movements — new, expansion, churn?+

AnalityQa AI applies a standard SaaS movement logic by default: new is a customer with no prior ARR, expansion is an existing customer with a higher ACV, contraction is an existing customer with a lower ACV, and churn is a customer whose contract ended. You can customize these rules in plain language during the data-prep step.

Can AnalityQa AI help with ASC 606 or IFRS 15 documentation?+

AnalityQa AI produces contract-level recognition detail showing performance obligations, transaction prices, and period-by-period amortization. This output is formatted to match what auditors typically request. It does not replace legal or accounting judgment on classification.

Is there an audit trail for SOX compliance?+

Yes. Every query, data source, and output is logged with a timestamp and the user who ran it. Exports include data-lineage footnotes. The log is exportable as a CSV for your SOX evidence package.

How is our revenue data secured?+

All data is encrypted in transit and at rest. Each customer workspace is fully isolated — your data is never shared with other customers or used to train models.

Can I compare recognized revenue against the sales forecast?+

Yes. Upload or connect your sales forecast alongside your billing export and ask 'How does recognized revenue this quarter compare to the forecast by segment?' AnalityQa AI joins the two on the matching dimensions and builds a variance table.

How is AnalityQa AI priced?+

Pricing is based on workspace size and connector count, not query volume. View-only users are not charged per seat. Contact the team for a quote based on your billing platform and team size.

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