MKT 326 · Assignment 3
This analysis models Customer Lifetime Value (CLV) for Netflix's U.S. subscriber base of 52.77 million customers, segmented by whether they watch Netflix original series. The goal is to quantify the financial return of investing in original content by measuring how viewership affects long-run retention and revenue.
CLV allows Netflix — and any subscription business — to justify marketing and product investments by expressing the net present value of a customer relationship. The model uses a 10% annual discount rate and a 9-year projection horizon.
| Year | Watcher Attrition | Non-Watcher Attrition | Watcher Retention | Non-Watcher Retention |
|---|---|---|---|---|
| 1 | 13% | 20% | 87% | 80% |
| 2 | 17% | 26% | 72.2% | 59.2% |
| 3 | 19% | 30% | 58.5% | 41.4% |
| 4 | 18% | 24% | 48.0% | 31.5% |
| 5 | 19% | 29% | 38.8% | 22.4% |
| 6 | 17% | 25% | 32.2% | 16.8% |
| 7 | 16% | 25% | 27.1% | 12.6% |
| 8 | 15% | 21% | 23.0% | 9.95% |
| 9 | 14% | 19% | 19.8% | 8.06% |
netflix_clv.R
# Netflix CLV Analysis — MKT 326 Assignment 3 # Gianni Hrousis | Lehigh University # ── Model Inputs ────────────────────────────────────────── revenue_per_year <- 11 * 12 # $132 annually cogs_rate <- 0.60 marketing_cost <- 5 discount_rate <- 0.10 total_customers <- 52770000 non_watcher_share <- 0.20 adoption_rate <- 0.40 # ── Attrition Rates ─────────────────────────────────────── attrition_watcher <- c(.13,.17,.19,.18,.19,.17,.16,.15,.14) attrition_non_watcher <- c(.20,.26,.30,.24,.29,.25,.25,.21,.19) # ── Contribution Margin per Customer per Year ───────────── cm <- revenue_per_year * (1 - cogs_rate) - marketing_cost # cm = $132 * 0.40 - $5 = $47.80 # ── CLV Function ────────────────────────────────────────── calc_clv <- function(attrition_vec, cm, discount_rate) { n_years <- length(attrition_vec) retention <- numeric(n_years) clv_contrib <- numeric(n_years) # Year 1: start at 100% of customers retention[1] <- 1 - attrition_vec[1] # Subsequent years: compound retention for (i in 2:n_years) { retention[i] <- retention[i-1] * (1 - attrition_vec[i]) } # Discount each year's contribution to present value # Mid-year convention: customers leave throughout the year for (i in 1:n_years) { clv_contrib[i] <- retention[i] * cm / (1 + discount_rate)^(i - 0.5) } sum(clv_contrib) } # ── Calculate CLV ───────────────────────────────────────── clv_watcher <- calc_clv(attrition_watcher, cm, discount_rate) clv_non_watcher <- calc_clv(attrition_non_watcher, cm, discount_rate) cat("CLV — Watcher: $", round(clv_watcher, 2), "\n") cat("CLV — Non-Watcher:$", round(clv_non_watcher, 2), "\n") # ── Maximum Original Series Spend ──────────────────────── n_non_watchers <- total_customers * non_watcher_share n_converts <- n_non_watchers * adoption_rate clv_gain <- clv_watcher - clv_non_watcher max_spend <- n_converts * clv_gain cat("Max Series Budget: $", format(max_spend, big.mark=",", nsmall=0), "\n")
Higher retention across all 9 years drives significantly greater lifetime value
Higher early attrition (20% in Year 1 vs 13%) sharply reduces NPV
Watcher CLV exceeds non-watcher CLV by $86.12 per customer — a 76.6% premium. This gap is primarily driven by attrition differences in the first 3 years, where the compounding effect of early churn dramatically reduces the non-watcher cohort.
Netflix has 52.77M U.S. customers, 20% of whom (10.554M) do not watch original series. If a new original series converts 40% of non-watchers into watchers, that is 4.22M new converts, each gaining $86.12 in CLV.
Netflix can spend up to approximately $363 million developing a new original series and still break even on a CLV basis — assuming a 40% adoption rate among non-watchers. Any series that costs less and achieves that conversion rate generates positive NPV. This framework directly justifies Netflix's multi-billion dollar content investment strategy and explains why subscriber retention metrics matter more than one-time acquisition costs.
The core insight of CLV is that a customer is worth more than their next transaction. By discounting the expected future stream of profits to present value, CLV expresses the total economic value of a customer relationship in a single number that can be compared directly against acquisition and retention costs.
In Netflix's case, this means the question is not "how much did this original series cost?" but rather "did the series shift enough customers from the non-watcher segment to the watcher segment, and does the resulting CLV increase justify the cost?" The answer, at a 40% adoption rate, is yes — by a significant margin.
This logic generalizes to any subscription business: invest in anything that raises retention, and the CLV math will validate the spend as long as the cost stays below the aggregate CLV gain across the converted customer base.
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