Weekly LTV Review Template

Why Weekly LTV Review Matters

LTV deterioration is often invisible until it's severe. A 5% drop in repeat rate or 3% margin compression per week compounds into 20-30% annual erosion before monthly reporting catches it. Weekly review creates early warning systems.

DTC Shopify brands operate on thin margins (8-15% net). LTV changes cascade: a $50 customer with 2.5x repeat rate and 40% contribution margin generates $50 profit. If repeat rate drops to 2.0x, that same customer generates $30 profit - a 40% loss. Weekly tracking catches this in week two, not month three.

Cohort-based review isolates acquisition quality from retention trends. A brand acquiring 500 customers weekly can spot if week-of-Jan-15 cohorts underperform week-of-Jan-8 cohorts, triggering channel or creative audits before budget scales.

Core LTV Metrics to Track Weekly

Establish a single source of truth for these four metrics, updated every Monday morning for the prior week's cohort:

Payback period (days): Time from first purchase to cumulative profit recovery. Threshold: 60-90 days for DTC. Red flag: >120 days or trending up week-over-week.
Repeat purchase rate (30-day, 60-day, 90-day): % of customers who buy again within window. Threshold: 25-35% at 30-day for healthy brands. Red flag: <20% or declining 2+ percentage points.
Average order value (AOV) on repeat purchases: Revenue per repeat customer. Threshold: Within 10% of first-purchase AOV. Red flag: Declining >5% or repeat AOV <80% of first AOV.
Contribution margin per cohort: Revenue minus COGS and fulfillment, divided by cohort size. Threshold: 35-50% for DTC. Red flag: <30% or declining week-over-week.

Weekly Review Checklist

Run this checklist every Monday for the prior week's cohort. Allocate 30 minutes. Assign one operator ownership.

Pull cohort data: Isolate customers acquired in the prior calendar week. Exclude gift purchases, bulk orders, or test transactions.
Calculate payback: Sum cumulative profit from first purchase through most recent transaction. Divide by customer count. Compare to prior 4 weeks.
Measure repeat rate: Count repeat purchasers (2+ orders) at 30, 60, 90-day windows. Calculate as % of cohort. Flag if any window declines >2 points.
Check AOV trend: Compare repeat AOV to first-purchase AOV. If repeat AOV is <85% of first AOV, investigate product mix or discount dependency.
Verify margin: Recalculate contribution margin. If declining, isolate driver: COGS increase, fulfillment cost rise, or discount elevation.
Cross-check channel: If LTV declined, segment by acquisition channel (paid search, email, organic, affiliate). Identify which channel degraded.
Document decision: Record metric, threshold status (green / yellow / red), and action. Yellow = monitor next week. Red = escalate same day.

Decision Thresholds and Escalation Rules

Define clear escalation paths. Vague thresholds create noise; specific ones enable automation and accountability.

Green (no action): Payback <90 days, repeat rate >25%, margin >35%, AOV repeat >85% of first. Continue current strategy.
Yellow (monitor): Payback 90-110 days, repeat rate 20-25%, margin 30-35%, AOV repeat 80-85% of first. Review daily for 5 days. If trend continues, escalate.
Red (escalate same day): Payback >110 days, repeat rate <20%, margin <30%, AOV repeat <80% of first. Halt new spend in affected channel. Convene product, marketing, and ops leads within 4 hours.
Trend rule: If any metric declines 3+ consecutive weeks, escalate to yellow even if absolute value is green. Trajectory matters more than snapshot.

Common Failure Modes and Diagnostics

LTV decline has predictable root causes. Use this diagnostic tree to isolate the driver and prescribe response.

Payback extending (>100 days) + repeat rate stable: Likely COGS or fulfillment cost increase. Audit supplier invoices and shipping rates. Negotiate or reformulate product.
Repeat rate declining (>3 points) + payback stable: Likely product quality, retention email decay, or cohort acquisition quality drop. Audit product reviews, email engagement, and channel mix.
AOV on repeat declining + repeat rate stable: Likely discount dependency or product mix shift. Audit discount codes used by repeat customers. Check if new SKUs have lower margin.
All metrics declining simultaneously: Likely acquisition channel degradation (e.g., paid search audience erosion) or platform algorithm change. Audit channel performance and creative fatigue.
Margin compression only: Likely promotional intensity increase or COGS spike. Audit discount frequency and supplier costs. Recalibrate discount strategy.

Structuring the Weekly Review Meeting

Discipline around process prevents metric drift and ensures consistent decision-making.

Timing: Every Monday, 9 AM. 30 minutes. Same attendees: marketing lead, product lead, ops lead, finance owner.
Agenda: (5 min) Prior week escalations - status update. (10 min) New cohort metrics - review checklist output. (10 min) Diagnostics - if red, run decision tree. (5 min) Actions and owners - assign next steps.
Artifacts: Maintain a rolling 12-week LTV dashboard. Plot payback, repeat rate, margin, AOV. Highlight red weeks. Archive meeting notes with decisions and outcomes.
Escalation path: Yellow findings go to Slack thread. Red findings trigger same-day standup with exec sponsor. Document all red escalations in a quarterly review.

Avoiding Common Pitfalls

LTV review fails when operators conflate correlation with causation, ignore cohort isolation, or allow metric definitions to drift.

Pitfall: Comparing week-of-Jan-8 cohort (measured at 60 days) to week-of-Jan-15 cohort (measured at 30 days). Solution: Always measure cohorts at the same age. Use 30-day repeat rate for weekly comparison.
Pitfall: Including one-time gift purchases or bulk orders in LTV calculation. Solution: Tag transactions at point of sale. Exclude gift flag and orders >3x AOV from cohort.
Pitfall: Changing metric definitions mid-quarter (e.g., switching from 30-day to 60-day repeat rate). Solution: Lock definitions in Q1. Publish them. Revisit only in planning cycle.
Pitfall: Reacting to single-week noise. Solution: Use 3-week moving average for trend detection. Only escalate if trend persists 2+ weeks or absolute value breaches red threshold.
Pitfall: Measuring LTV without isolating channel. Solution: Segment all metrics by acquisition channel. A brand-wide LTV decline often masks channel-specific problems.

Questions

FAQ

How do we handle seasonal cohorts in LTV review?

Isolate seasonal cohorts separately. Week-of-Nov-15 (pre-holiday) will have different repeat rates than week-of-Feb-1 (post-holiday). Compare Nov-15 to prior-year Nov-15, not to Feb-1. Maintain a seasonal adjustment factor (e.g., holiday cohorts repeat 15% lower at 30-day). Apply it to threshold comparison.

What if payback extends but repeat rate stays strong?

Payback extension with stable repeat rate signals margin compression, not retention failure. Audit COGS (supplier price increase?), fulfillment costs (shipping rate change?), or discount intensity (are repeat customers using more codes?). Payback is a margin problem; repeat rate is a retention problem. Diagnose separately.

Should we review LTV by product or only by cohort?

Start with cohort-level review (weekly). Add product-level LTV only if a single SKU represents >30% of revenue or if product mix shifted recently. Product-level LTV requires longer measurement windows (90+ days) due to smaller sample sizes. Use it for quarterly strategy, not weekly operations.

How do we account for refunds and returns in LTV?

Subtract refunded revenue from cumulative profit calculation. Measure return rate separately (% of orders returned within 30 days). If return rate increases, it's a product quality signal, not a payback signal. Track both: LTV (post-refund) and return rate (pre-refund). A cohort with high returns and low LTV needs product investigation, not marketing cuts.

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