Blended Versus Channel LTV

Why this matters

Marketing Analysts use Lifetime Value (LTV) to decide where to spend, how fast to scale, and when acquisition is profitable. Two views matter:

• Blended LTV: the average LTV of a cohort across all acquisition sources. Great for company-level planning.
• Channel LTV: the LTV of a cohort filtered by the acquisition channel. Essential for bids, budgets, and channel-specific guardrails.

Real tasks you’ll face:

• Set CAC caps for paid channels based on expected LTV and payback.
• Explain why overall LTV dropped when you increased spend (mix shift vs true quality drop).
• Forecast next quarter revenue using recent cohort LTV curves.

Concept explained simply

Think of LTV as the total net contribution (revenue minus variable costs like discounts, payment fees, cost of goods, and support) your average new customer will generate over a time window (e.g., 90 or 180 days).

• Blended LTV: One number for the whole cohort, mixing all channels.
• Channel LTV: One number per channel cohort (e.g., Paid Social January, Paid Search January).
• Use both: blended for top-level pacing; channel for allocation and bids.

A simple mental model

• Two lenses: blended = helicopter view; channel = zoom lens.
• Blended tells you if the machine is healthy overall. Channel tells you where to push or pull spend.
• Mix shifts can change blended LTV even if channel LTVs do not change.

When to use which

• Use blended LTV for executive updates, total revenue forecasts, and runway planning.
• Use channel LTV for CAC caps, bid strategies, and daily budget allocation.
• If attribution is noisy, rely on blended for top-level; use channel with caution and quality checks.

Worked examples

Example 1: Compute blended and channel LTV and compare to CAC

Cohort: 1,000 customers acquired in January.

• Counts: Paid Social 400, Paid Search 300, Organic 300.
• Cumulative net contribution per original customer (through Month 2):
  • Paid Social: M0 = $3.0, M1 = $1.5, M2 = $1.0 → LTV_2 = $5.5
  • Paid Search: M0 = $4.0, M1 = $2.5, M2 = $1.8 → LTV_2 = $8.3
  • Organic: M0 = $2.0, M1 = $1.4, M2 = $1.1 → LTV_2 = $4.5
• Blended LTV_2 = (400×5.5 + 300×8.3 + 300×4.5) / 1000 = $6.04
• Average CACs: Paid Social $4, Paid Search $6, Organic $1
• LTV:CAC:
  • Paid Social: 5.5 / 4 = 1.38
  • Paid Search: 8.3 / 6 = 1.38
  • Organic: 4.5 / 1 = 4.50
• Payback (first month where cumulative LTV ≥ CAC):
  • Social: by M1 (3.0 + 1.5 = 4.5 ≥ 4)
  • Search: by M1 (4.0 + 2.5 = 6.5 ≥ 6)
  • Organic: by M0 (2.0 ≥ 1)

Insight: Blended LTV = $6.04 does not mean each channel is $6.04. Allocation decisions must use channel values.

Example 2: Mix shift can move blended LTV without channel change

Suppose you double Paid Social spend, raising its share from 40% to 60% while channel LTVs stay the same. The blended LTV will drop because the mix tilts toward a lower-LTV channel (5.5 vs 8.3). Nothing got “worse” inside channels—only the mix changed. Communicate this to stakeholders to avoid overreacting to blended changes.

Example 3: Geography confounds LTV

Imagine Paid Search spans US and LATAM. US LTV_90 = $70, LATAM LTV_90 = $30. If spend shifts from US to LATAM, channel LTV for Paid Search may fall due to geography mix. Fix: segment cohorts by channel + region, then recompute LTVs. You’ll see the real driver is mix, not quality.

Build this quickly in a spreadsheet

1. List new users by cohort (e.g., Jan) and channel with counts.
2. Aggregate net contribution by month since acquisition (M0, M1, M2…) for that cohort and channel.
3. Compute channel LTV_t = cumulative contribution / original users for each channel.
4. Compute blended LTV_t as a weighted average across channels by user counts.
5. Add CAC per channel and calculate LTV:CAC and payback month.
6. Chart cumulative LTV curves by channel to visualize payback and asymptotes.

Quality checks and common mistakes

• Mixing apples with oranges: don’t blend different products or geographies unless you intend to.
• Using revenue instead of contribution: subtract variable costs or apply gross margin.
• Shifting time windows: compare LTV at the same t (e.g., LTV_90 vs LTV_90), not 60 vs 90 days.
• Attribution flip-flops: changing attribution rules mid-way breaks comparability.
• Ignoring refunds/fraud: adjust to keep LTV honest.

Exercises

Solve these before the quick test. Tip: write out the weighted averages explicitly.

Exercise 1 — Compute blended and channel LTV, ratios, and payback

June cohort: 600 customers

• Counts: Paid Social = 300, Paid Search = 200, Organic = 100
• Cumulative net contribution per original customer (M0–M2):
  • Paid Social: M0=$2.5, M1=$1.2, M2=$0.8 → LTV_2
  • Paid Search: M0=$3.5, M1=$2.1, M2=$1.4 → LTV_2
  • Organic: M0=$1.8, M1=$1.1, M2=$0.9 → LTV_2
• CACs: Social $3.0, Search $5.5, Organic $0.5

Tasks:

• Compute LTV_2 for each channel.
• Compute blended LTV_2 for the cohort.
• Compute LTV:CAC per channel and the earliest payback month.
• Which channels meet a 3:1 target by day 90?

Exercise 2 — Set CAC caps from channel LTV

Target: LTV:CAC ≥ 3 by day 90.

• 90-day LTVs (net contribution): Paid Social $24, Paid Search $60, Organic $20
• Current CACs: Social $7.5, Search $21, Organic $5

Tasks:

• Compute max CAC per channel to hit the 3:1 target.
• For each channel, is the current CAC within the guardrail?
• Which channel can scale first and why?

• Checklist before submitting:
  • Used contribution LTV (not revenue LTV) for the ratio.
  • Compared CACs to the correct caps (LTV/3).
  • Explained your scaling choice in one sentence.

Practical projects

• Build a cohort LTV dashboard for the last 6 months showing blended vs channel LTV_30/60/90 and payback.
• Create a budget allocator that takes channel LTV and CAC to output max CAC and suggested daily budgets.
• Run a sensitivity test: vary margin and retention by ±10% and observe impact on LTV and payback.

Who this is for

• Marketing Analysts, Growth Analysts, and Data-savvy Marketers making spend decisions.

Prerequisites

• Comfort with basic spreadsheets and cohort aggregation.
• Understanding of revenue vs contribution margin.
• Basic attribution concepts (first-touch vs last-touch).

Learning path

1. Compute cohort-level LTV (blended) consistently by time window.
2. Split into channel cohorts and compute channel LTVs.
3. Add CAC and measure LTV:CAC and payback.
4. Segment further by geo/product to remove mix issues.
5. Automate and set alert thresholds for spend decisions.

Next steps

• Apply the exercises to your last real cohort and compare to the worked examples.
• Document your rules (time window, attribution, margin method) so results stay consistent.
• Take the quick test below to check your understanding. Note: the test is available to everyone; only logged-in users will have progress saved.

Mini challenge

In one paragraph, explain to a stakeholder why blended LTV fell this month even though channel LTVs are flat—and what you’ll do with budgets tomorrow morning.