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Review your selected methods side by side; rows that differ are highlighted.
| BG/NBD Model× | Customer Lifetime Value× | |
|---|---|---|
| Field | Marketing | Marketing |
| Family≠ | Regression model | Process / pipeline |
| Year of origin≠ | 2005 | 1996 |
| Originator≠ | Peter S. Fader, Bruce G. S. Hardie & Ka Lok Lee | Robert Blattberg and John Deighton |
| Type≠ | Probabilistic buy-till-you-die model of repeat transactions | Financial modeling methodology |
| Seminal source≠ | Fader, P. S., Hardie, B. G. S., & Lee, K. L. (2005). "Counting Your Customers" the Easy Way: An Alternative to the Pareto/NBD Model. Marketing Science, 24(2), 275-284. DOI ↗ | Blattberg, R. C., Getz, G., & Thomas, J. S. (2001). Customer Equity: Building and Managing Relationships as Assets. Harvard Business School Press. ISBN: 978-0875847191 |
| Aliases≠ | Beta-Geometric/NBD Model, BG/NBD, Buy-Till-You-Die Model, Fader-Hardie-Lee Model | CLV, LTV, Customer Value |
| Related≠ | 4 | 5 |
| Summary≠ | The BG/NBD (Beta-Geometric/Negative Binomial Distribution) model is a probabilistic buy-till-you-die model that predicts how many times a customer will transact in the future and whether that customer is still active, using only their past purchase recency and frequency. Introduced by Peter Fader, Bruce Hardie and Ka Lok Lee in their 2005 Marketing Science paper "Counting Your Customers the Easy Way," it was designed as a far simpler alternative to the Pareto/NBD model of Schmittlein, Morrison and Colombo while delivering comparable forecasts. The model couples a Poisson purchasing process, whose rate varies across customers by a gamma distribution, with a geometric dropout process governed by a beta-distributed dropout probability. The key behavioral story is that customers buy at a steady individual rate while alive and become permanently inactive with some probability immediately after any purchase. Because the latent attrition is unobserved, the model infers each customer's probability of still being alive from how recently and how often they bought. Its estimation requires only the (x, t_x, T) summary per customer and can even be fit in a spreadsheet, which made customer-base analysis practical for ordinary analysts. | Customer Lifetime Value (CLV) is a financial metric that quantifies the total profit a company expects to generate from its relationship with a customer over the entire duration of that relationship. Developed through work by Blattberg, Getz, and Thomas in the 1990s-2000s, CLV integrates acquisition costs, purchase behavior, retention rates, and margin information to estimate the net present value of each customer. |
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