How to Analyze the Drivers of Revenue Growth

Introduction

You're trying to grow revenue but not sure what to move first; the goal here is simple: identify what moves revenue now and next so you can forecast and act. Revenue is driven by five concrete levers-price, volume, mix (product or customer mix), retention (repeat business), and channels (where customers come from)-each behaves differently and requires different tactics. Breaking (decomposing) revenue into these parts tightens forecasts and points straight to action: price lifts improve margin, volume gains need demand or distribution, mix shifts change average spend, retention lowers acquisition needs, and channel shifts alter unit economics. Break revenue into parts you can change. Here's the quick math: a 2% price rise with a 1% volume drop still nets growth if margins expand-what this hides is churn sensitivity; defintely track leading indicators.

Key Takeaways

• Break revenue into five levers-price, volume, mix, retention, channels-to identify what moves revenue now and next.
• Use decomposition frameworks (top‑down vs bottom‑up, Revenue = Price × Volume × Mix, cohort/retention analysis) to tighten forecasts and reconcile views.
• Track concrete metrics: price elasticity, units/active users/orders for volume, mix via ARPA/attach rates; remember price lifts margin, volume drives scale.
• Segment by RFM, LTV and channel economics; prioritize cohorts/channels with the highest payback and lowest churn.
• Turn insights into 90‑day experiments with owners and KPIs (CAC, contribution/unit, capacity limits, macro risk) to test highest‑impact moves fast.

Revenue-decomposition frameworks

You're building a forecast and need to know what actually moves revenue now and next, so you can test the highest-impact levers fast. Here's how to run two rigorous frameworks, cross-check them, and turn numbers into experiments.

Use top-down vs bottom-up approaches for cross-checks

Start by stating the goal: validate whether your growth target is realistic given market size and internal capacity. Top-down asks how big the market is and what share you can plausibly win. Bottom-up adds credibility by building from real unit economics and pipeline.

Practical steps:

• Estimate TAM (total addressable market) for FY2025 by product and geography.
• Set plausible target share for FY2025 and compute implied revenue.
• Build bottom-up from customers × ARPA (average revenue per account) × renewal/expansion rates for FY2025.
• Reconcile gaps: if bottom-up < top-down, check conversion rates, pricing, or market assumptions.

Example check (illustrative Company Name FY2025 figures):

• TAM FY2025: $15,000,000,000
• Target share 1.67% → implied revenue $250,500,000
• Bottom-up: $200,000,000 ARR from active accounts + $50,500,000 expected expansion = $250,500,000

Here's the quick math: if your bottom-up misses the top-down by >10%, audit conversion, pricing, or channel coverage. What this check hides: TAM assumptions can be fuzzy; focus on realistic served market and share gains you can prove with pilots.

Pick two frameworks and reconcile them

Apply the growth equation: Revenue = Price × Volume × Mix

Use the multiplicative growth equation to break total revenue change into what's earned from price, units, and product mix. That lets you run targeted tests: price experiments, volume funnels, or mix incentives.

Specific steps and best practices:

• Define Price as average selling price (ASP) per unit or ARPA for FY2025.
• Define Volume as units sold, active accounts, or orders in FY2025.
• Define Mix as the weighted effect of shifting sales toward higher- or lower-priced SKUs or tiers.
• Calculate percent change contributions using multiplicative decomposition: ΔRevenue ≈ (1+ΔPrice)×(1+ΔVolume)×(1+ΔMix) - 1.
• Do this at product × channel granularity, then roll up.

Worked example (Company Name FY2025 baseline $250,000,000):

• Price change: +3% (ASP increases)
• Volume change: +8% (more units/users)
• Mix change: +2% (shift to premium SKUs)
• Revenue growth ≈ (1.03×1.08×1.02 - 1) = +13.7%
• New revenue ≈ $284,250,000

Account for elasticity: if price elasticity = -1.5, a +3% price raise may cut volume by ~4.5%. Recompute: (1.03×0.955×1.02 -1) ≈ -0.6%, which flips the outcome. Here's the quick math - always model elasticity scenarios. What this estimate hides: cross-price effects, promotional timing, and channel-specific reactions.

Pick two frameworks and reconcile them

Complement with cohort and cohort-retention analysis

Revenue decomposition without cohorts misses persistence. Cohort analysis shows whether current growth is repeatable or one-time. Use cohorts by acquisition month, product, and channel to read retention and expansion patterns for FY2025.

Actionable steps:

• Define cohorts (acquired month/year) and measure monthly revenue per cohort for 12+ months.
• Compute Gross Revenue Retention (GRR) and Net Revenue Retention (NRR) for FY2025 cohorts.
• Segment cohorts by channel and ARPA to find high-payback cohorts.
• Translate NRR into forward bookings: NRR > 100% means expansion-led growth; NRR < 100% signals churn risk.

Example cohort math (illustrative FY2025 figures):

• Starting cohort ARR (Jan-Dec 2024) = $200,000,000
• GRR = 80% → retained revenue = $160,000,000
• Expansion adds 30% → NRR = 110% → end-of-period revenue = $220,000,000

Here's the quick math: a cohort with 110% NRR replaces churn and fuels growth; a cohort with 80% GRR loses structural revenue. What this hides: cohort-level seasonality and activation delays - test retention levers (onboarding, pricing tiers) and re-run cohorts monthly to catch trends early. defintely prioritize cohorts with shortest payback and highest expansion.

Pick two frameworks and reconcile them

Price, volume, and product mix

You're trying to find what to change this quarter so revenue actually moves - not just wishful forecasting. The direct takeaway: quantify how price, volume, and mix each drive dollars, then test the cheapest lever first.

Price: quantify change versus elasticity

If you raise or cut price, ask how sensitive buyers are. Price elasticity (percent change in quantity divided by percent change in price) gives a first-order revenue and margin estimate. Here's the quick math:

• Measure baseline: average unit price, units sold, and unit variable cost.
• Estimate elasticity from historical A/B tests, geo-price tests, or industry benchmarks - use cohorts to avoid survivorship bias.
• Model outcome: Revenue change ≈ (1 + %ΔP) × (1 + %ΔQ) - 1, where %ΔQ = elasticity × %ΔP.

Practical example: baseline unit price $100, unit cost $60, monthly units 1,000. If elasticity = -1.2 and you raise price by +5%, expected quantity change = -6%. Revenue moves from $100,000 to $98,700, while gross margin dollars rise from $40,000 to $42,300 because margin per unit increases.

Best practices and caveats:

• Run controlled price tests where possible; don't extrapolate short promo elasticities to permanent price change.
• Segment elasticity by cohort (new vs. repeat, enterprise vs. SMB); aggregate elasticities hide key variation.
• Account for competitive reaction and time-lag - initial churn may settle after re-benchmarking.

What this estimate hides: cross-elasticities with mix or channel, and long-term LTV impact from perceived value changes. If onboarding takes 14+ days, churn after a price rise will be higher - so factor that in.

Volume: measure shifts by units, active users, or orders

Volume is the scale lever - more buyers or more purchases per buyer. You should track multiple volume signals and reconcile them to revenue.

• Define the unit: units sold, active buyers, or orders - pick the one that maps to your business model.
• Instrument events: product view → add-to-cart → purchase; track conversion funnels and cohort conversion curves.
• Monitor week-over-week and cohort retention; use cohort charts to separate growth from one-off spikes.

Concrete steps: set daily feeds for units sold and active accounts; create a rolling 90-day cohort table that shows conversion by day 1, day 7, day 30. Example KPI: monthly active users 25,000, buying users 5,000 → conversion 20%. If marketing lifts buying users to 6,000 (a +20% increase), and ARPA holds, revenue rises commensurately.

Best practices and limits:

• Attribute volume changes to proper campaigns using last-touch and multi-touch models; reconcile with revenue to catch attribution leakage.
• Watch fulfillment and lead times - if you can't deliver, volume growth dies fast.
• Use control groups to validate lift; promotional uplift often decays once promos stop.

Quick note: a volume-only play scales quickly but can dilute ARPA if the new buyers are low-value - segment growth to see real impact.

Product mix: track attach rates, premium tiers, and SKU shifts

Mix changes shift ARPA (average revenue per account) and margin without changing buyer counts. You must measure which SKUs and bundles drive the most incremental dollars.

• Compute ARPA = total revenue / active accounts over the same period.
• Track attach rate = number of accounts buying add-on ÷ total accounts; monitor tier migration (free → basic → premium).
• Build contribution-by-SKU: incremental revenue and incremental margin per attach.

Illustrative example: baseline ARPA = $100. Add-on price = $30. If attach rate rises from 15% to 22%, add-on ARPA moves from $4.50 to $6.60, lifting ARPA by $2.10 to $102.10. That's a simple change that can beat a price hike with lower churn risk.

Steps to act:

• Prioritize attach increases where contribution margin is high.
• Test bundling vs. cross-sell: measure incremental purchases and incremental churn separately.
• Report mix shifts weekly and map them to promo calendars and sales incentives.

What to watch: SKU cannibalization and channel-specific mix - a reseller push may increase units but favor low-margin SKUs. Also track how premium upgrades change LTV and support costs; sometimes ARPA goes up but net LTV falls due to higher servicing.

Price moves margin, volume moves scale

Customer and channel segmentation

You want to find the pockets of revenue that give the fastest, biggest returns and stop wasting spend on low-payback buckets. The fastest path: split customers by revenue, growth rate, and churn, map those segments to channels, then fund the cohorts with the best LTV-to-CAC and shortest payback.

One-liner: Not all customers are equal-focus where payback is fastest

Segment by revenue, growth rate, and churn (RFM: recency, frequency, monetary)

Start by pulling your FY2025 customer ledger and transactional events for the last 12-24 months. Build a table, one row per customer, with these columns: last purchase date (recency), purchases per period (frequency), total revenue and gross margin (monetary), month-over-month revenue growth rate, and churn flag/date.

Practical steps

• Bucket recency: active in last 30/90/365 days
• Bucket frequency: 1, 2-5, 6+ purchases per year
• Bucket monetary: top 20%, middle 50%, bottom 30% (Pareto check)
• Compute cohort growth: 3- and 12-month CAGR per customer segment
• Calculate churn: customers lost in a period ÷ customers at period start

Best practices

• Use cohorts (by acquisition month) to isolate vintage effects
• Show heatmap: revenue concentration vs. churn - high revenue/high churn is urgent
• Flag customers with rising churn risk (activity falling >30% in 90 days)
• Normalize for seasonality by comparing same-period FY2025 vs FY2024

Actionable outputs

• List top 10% customers by revenue and their churn rate - protect them
• Identify mid-revenue fast-growers - invest in upsell
• Label low-revenue/high-frequency as retention plays

Here's the quick math: sort customers by revenue, then overlay churn; the intersection of high revenue and low churn gives stable base, high revenue/high churn gives immediate retention ROI. What this hides: cohort seasonality and one-time bulk purchases - segment those separately, defintely.

Attribute revenue by channel (direct, distributor, marketplace)

Attribute every dollar in FY2025 back to a channel: direct sales, inside sales, distributors/partners, marketplaces, and paid digital. Use transaction-level UTM, partner IDs, or ERP mapping. If you can't attribute precisely, create a reconciliation: bank receipts → order IDs → channel tag.

Practical steps

• Implement multi-touch attribution window aligned with sales cycle (e.g., 30-90 days)
• Tag partner/reseller orders at point of sale with partner ID
• Allocate recurring revenue by original acquisition channel for LTV tracking
• Net out channel fees/commissions to get channel-level gross margin

Best practices

• Prefer multi-touch for long B2B cycles; last-click for short transactional e‑commerce
• Measure channel CAC = total channel spend ÷ new customers acquired in that channel
• Report channel economics monthly for FY2025 and rolling 12 months
• Compare net revenue after returns, chargebacks, and marketplace fees

Actionable outputs

• Rank channels by FY2025 net revenue, margin, and payback time
• Move budget from channels with long payback and low margin into channels with positive unit economics
• Set ownership: Sales Ops owns partner attribution fixes; Growth owns digital tagging

One clear lens: channel = acquisition cost, take rate, and net margin. If a marketplace brings volume but costs too much per new retained customer, it's a distribution experiment not a growth channel.

Prioritize high-LTV cohorts for investment

Compute cohort LTV (lifetime value) with a contribution-margin approach: LTV = sum over months of (ARPA × gross margin% × survival probability). For FY2025, run LTV curves for each acquisition cohort and channel to compare on a like-for-like basis.

Steps to compute and prioritize

• Define ARPA (average revenue per account) per cohort for FY2025 months
• Estimate gross margin per cohort (price minus variable costs)
• Estimate retention curve from FY2025 cohort data; apply to model future cash flows
• Discount at operating WACC or use simple payback - many teams use payback ≤ 12 months as a de-risk threshold
• Calculate LTV/CAC and rank cohorts; target cohorts with LTV/CAC ≥ 3x

Best practices and tests

• Prioritize cohorts with short payback and high incremental margin for FY2025 spend
• Run A/B pricing or packaging tests on mid-LTV cohorts to shift them up
• Test retention plays (onboarding emails, success teams) on cohorts with low ARPA but high frequency
• Use small, time-boxed experiments: 90 days per test with clear KPI (ARPA, churn, payback)

Concrete action example: pick the FY2025 cohort acquired via Channel A with LTV/CAC = 4x and 8-month payback; increase marketing spend there by 20% while running a retention pilot on a lower-LTV cohort. Owner: Growth; Metric: improve cohort ARPA by 5% in 90 days.

One-liner: Not all customers are equal-focus where payback is fastest

Unit economics and operational drivers

You're scaling revenue but need to know which dollars actually stick and what ops will break first - here's the short takeaway: measure contribution per unit, tighten CAC payback, and remove capacity choke points so each incremental sale increases cash profit.

Calculate contribution per unit: price minus variable cost

Start with the simple formula: Contribution per unit = Price - Variable cost. That tells you how much each sale contributes to covering fixed costs and profit.

Steps to calculate and validate:

• Collect price and per-unit variable costs (materials, direct labor, transaction fees, shipping).
• Include incremental marketing tied to the unit (promo discounts, coupons).
• Compute contribution margin = contribution per unit ÷ price (expressed as a percentage).
• Run a sensitivity table: change price ±5% and cost ±10% to see contribution swings.

Practical examples: if price is $100 and variable cost is $40, contribution = $60 and contribution margin = 60%. If variable cost rises to $50, contribution falls to $50, a 17% drop in contribution.

Best practices and checks:

• Tag costs as fixed vs variable monthly to avoid misallocating overhead.
• Recompute monthly if raw-materials or shipping are volatile.
• Report contribution by SKU, by channel, and by customer cohort.

Here's the quick math: contribution tells you which products to push when capacity is tight. What this estimate hides: allocated overhead still matters for profitability decisions; don't ignore it.

One-liner: Contribution per unit shows real cash earned per sale.

Track CAC and payback time

Customer acquisition cost (CAC) is total marketing and sales spend divided by new customers acquired in the period. Payback time is months required for cumulative contribution from a customer to cover CAC.

Steps and KPIs to track weekly/monthly:

• Calculate CAC = (marketing + sales expenses) ÷ new customers.
• Estimate monthly contribution per customer (price × gross margin share or ARPA × contribution margin).
• Compute payback months = CAC ÷ monthly contribution per customer.
• Track LTV:CAC ratio (lifetime value ÷ CAC); target depends on model (SaaS often >3, consumer marketplaces 2-4).

Concrete example: CAC = $300, monthly contribution per new customer = $50, payback time = 6 months. If churn rises and contribution drops to $30, payback extends to 10 months-that's a cadence risk.

Best practices and guardrails:

• Segment CAC by channel - paid search, organic, sales-led; prioritize channels with shortest payback.
• Use cohort windows (0-3, 3-6, 6-12 months) to see how CAC efficiency evolves.
• Stress test CAC under higher CPMs or lower conversion rates to set buffer limits.

Operational action: if payback >12 months and capital is tight, shift spend to lower-CAC channels or raise price/upsell to shorten payback.

One-liner: CAC payback pinpoints whether growth consumes or creates cash.

Monitor capacity, fulfillment, and lead times that limit sales

Even great unit economics and strong CAC won't generate revenue if you can't deliver. Identify where capacity or logistics cap growth: manufacturing throughput, warehouse space, pick-pack times, delivery windows, and supplier lead times.

Weekly operational checks and metrics:

• Measure utilization rate = actual output ÷ maximum sustainable output.
• Track order cycle time end-to-end and on-time fulfillment rate.
• Monitor supplier lead time variance and safety stock days.
• Report backlog and build-rate: backlog ÷ weekly production capacity.

Practical steps to act fast:

• Raise capacity temporarily via overtime, subcontracting, or 3PL for a predictable revenue ramp.
• Reduce lead-time with dual sourcing, smaller safety-stock triggers, or vendor-managed inventory pilots.
• Run a 90-day surge plan: map critical path, assign owners, and pre-buy components if contribution justifies working capital.
• Measure results: fulfillment rate to improve from 85% to 95% within 60 days, or pause new customer acquisition until capacity improves.

Example constraint: production capacity 10,000 units/month, demand forecast 15,000 units - backlog points to lost sales or rising expedited costs. If contribution per unit is $20, each unmet unit equals forgone contribution of $20.

One-liner: Operational bottlenecks cap revenue growth - fix the slowest step first.

Owner: Operations: run a 90-day capacity playbook and report weekly to Growth; Finance: model cash for pre-buy by Friday.

Macro and competitive factors

Model TAM and share gains and losses

You need to know the size of the opportunity (TAM) and how much of it you can realistically win this year so you stop chasing impossible targets and prioritize the right plays.

Steps to build a defensible TAM and share model:

• Top-down: start with an industry revenue figure for 2025 you trust (trade association, government stats, or analyst reports).
• Bottom-up: sum addressable customers × 2025 average spend per customer to create a benchmark TAM.
• Define SAM (serviceable addressable market) and SOM (serviceable obtainable market) by geography, channel, and product fit.
• Model share-change scenarios: base, upside, downside - map each to revenue and margin impacts.
• Stress-test: reduce expected spend per customer by 10-20% and re-calc share impact.

Here's the quick math using a simple 2025 example you can copy: assume 2025 market = $12.0 billion (TAM); your current share 0.8% = $96.0 million; target share 1.2% = $144.0 million. Gaining 0.4 percentage points = $48.0 million extra revenue.

What this estimate hides: pricing changes, capacity limits, and channel conflicts all shrink the achievable SOM - so build a sensitivity table that varies price, conversion, and churn.

Next step: Growth/Strategy - produce a three-scenario TAM/SOM/SOM waterfall for 2025 in a spreadsheet by Friday. This defintely helps prioritize sales motion.

External shocks change achievable growth

Assess cycle sensitivity: GDP, consumer spend, commodity prices

You must quantify how macro moves your top line so you can stress-test forecasts and set trigger-based actions when the economy turns.

How to measure cycle sensitivity (practical steps):

• Collect monthly/quarterly revenue and macro series (GDP growth, retail sales, CPI, commodity indices) for 24-60 months.
• Run simple regressions to estimate elasticities (revenue change per 1% change in each macro factor).
• Translate elasticities into scenario impacts: mild recession, stagflation, commodity shock.
• Assess timing: leading indicators (consumer sentiment, new orders) give 1-3 month lead on demand shifts.
• Embed triggers: e.g., if GDP growth falls > 0.5 percentage points, pause new channel launches.

Example elasticities (use as templates, measure your own): if revenue elasticity to GDP = 1.2, a 1% GDP drop implies roughly a 1.2% revenue decline. If commodity (input) price elasticity = 0.6, a 20% jump in input costs can erode gross margin materially unless you raise price or cut cost.

What to watch in 2025: central bank rate moves, consumer credit delinquencies, and energy price spikes - each shortens runway for growth experiments and raises customer churn risk.

Next step: FP&A - calculate your revenue sensitivity table (GDP ±1%, commodities ±20%, consumer spend ±5%) and add to the forecast by Wednesday.

External shocks change achievable growth

Map competitor moves: pricing, product launches, distribution deals

You need a disciplined process to translate competitor actions into likely impacts on price, volume, and share so you respond fast and rationally.

Practical playbook to map and act on competitor moves:

• Create a competitor tracker: price, promo cadence, new SKUs, channel expansions, and contract wins - update weekly.
• Estimate impact rules: e.g., national price cut of 10% by a top rival may force a 0.5-2.0 point share shift depending on differentiation.
• Simulate outcomes: model lost volume vs. regained volume if you counterprice, add features, or expand distribution.
• Prioritize responses by payback: price match only if payback < 6 months; otherwise use targeted retention offers.
• Use non-price levers: inventory guarantees, faster fulfillment, exclusive bundles to defend share without margin erosion.

Example action logic: competitor A drops price 8% in a channel that delivers 30% of your revenue. If your elasticity to competitor price is 1.0, expect ~8% volume loss in that channel; respond with a targeted bundle that raises ARPA by 6% to protect margins.

Operational limit: if fulfillment capacity is maxed, countering a competitor can increase churn elsewhere - always check capacity first.

Next step: Commercial Ops - build the competitor tracker and three tactical responses (price, product, distribution) for each major move; deliver by next Tuesday.

External shocks change achievable growth

How to Analyze the Drivers of Revenue Growth - Action plan

Prioritize drivers with highest impact and fastest tests

Takeaway: focus first on changes that can move material dollars fast and be measured within 30-90 days.

Step 1: pull your FY2025 baseline (total revenue, ARPA/ARPU, monthly churn, active accounts/users, CAC, variable cost per unit). I'll call that dataset Company Name FY2025 baseline.

Step 2: for each candidate driver estimate a 90-day revenue delta using simple marginal math:

• Price: ΔRevenue ≈ Baseline revenue × % price change × share affected
• Volume: ΔRevenue ≈ Δunits × average price
• Mix: ΔRevenue ≈ baseline revenue × change in ARPA from shifting SKUs/tier mix

Example (hypothetical using a Company Name FY2025 baseline): if baseline revenue = $100,000,000 and 60% of revenue is from accounts you can reprice, a +5% price test on that segment = $100M × 0.60 × 0.05 = $3,000,000 incremental. What this estimate hides: elasticity (churn) and timing of contract renewals.

Step 3: score each idea by three lenses - impact (expected % revenue change), speed (days to test), and confidence (data quality). Prioritize ideas with > 1% revenue upside and 60 days to implement. Run a short pilot if confidence < 60%.

One-liner: Prioritize tests that move > 1% revenue and run in 60 days or less.

Recommend 90-day experiments tied to KPI changes

Takeaway: convert each prioritized driver into a single, measurable 90-day experiment with a clear metric, sample, and stop/go rules.

Experiment template (fill with Company Name FY2025 baselines):

• Hypothesis: what change and why
• Primary metric: e.g., ARPA (average revenue per account), monthly recurring revenue (MRR), or net revenue retention (NRR)
• Target: numeric change vs FY2025 baseline (e.g., ARPA + 5%)
• Population & sample size: define affected cohorts (new vs existing, region, channel)
• Duration: 90 days; interim check at day 30 and 60
• Stop/go criteria: uplift +X% with p<0.05 or churn uptick > Y points
• Owner & stakeholders: Growth, Product, Finance, Ops

Specific experiments you can run in 90 days:

• Price A/B: small, staged increases (e.g., +3% vs +7%) on a non-contract cohort; measure ARPA, churn, and conversion lift.
• Bundle upsell: add a value bundle to 20% of accounts with baseline low attachment; target attach rate + 8pp.
• Onboarding quick-win: shortening time-to-first-value by 30% for new accounts to reduce 90-day churn by 0.5 percentage points.
• Channel push: move a 12-week paid campaign in marketplace vs direct sales and track CAC and payback time.

Measure: convert experiment outputs into revenue impact using the marginal math above and record daily MRR and weekly cohort retention to catch negative signals fast.

One-liner: Tie every 90-day experiment to one KPI and a numeric target vs FY2025 baseline.

Set owner and metric

Takeaway: assign a single owner per experiment, a weekly reporting cadence, and a Finance-verified revenue translation to track true impact on cash.

Role bank (typical):

• Owner: Growth - runs experiment, reports weekly
• Support: Product - delivers feature changes
• Support: Sales/CS - executes pricing or packaging changes
• Finance: validates revenue math and updates the cash model
• Ops/IT: ensures tracking and instrumentation

Concrete 90-day KPI examples (use Company Name FY2025 baselines to set numeric targets):

• Growth: increase ARPA +5% vs FY2025 baseline by end of Q2 (e.g., if baseline ARPA = $200, target = $210)
• Customer Success: reduce monthly churn by 0.5 percentage points within 90 days
• Marketing: lower CAC by 10% on tested channel within 90 days

Tracking and governance: update a single dashboard weekly with these fields - baseline FY2025 value, current value, delta, 90-day target, owner, and pass/fail at day 90. Finance should translate KPI deltas into cash using a 13-week rolling model to see real impact.

One-liner: Turn decomposition into a 90-day action plan with one owner, one metric, and weekly checks.

Next step: Growth - launch the ARPA +5% test against a 10% account sample by Monday; Finance - provide Company Name FY2025 baseline and a 13-week cash view by Friday.