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Basic Customer Lifetime Value

Customer Analytics Full Version →

How much is a customer worth over the months ahead? Answer it with two small models: one for whether they stay, one for how much they spend. Same engine as the full CLV tool, with one marketing lever and no extra dials.

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Get Data
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Check Columns
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Run
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Read Results

👨‍🏫 Professor Mode: Guided Learning Experience

Brand new to lifetime value? Turn on Professor Mode for a step-by-step walkthrough.

OVERVIEW & WHAT THIS TOOL SOLVES

A customer is not a single transaction—they are an ongoing relationship that may keep generating purchases for months or years. Estimating what that relationship is worth in today's dollars with one crude average blurs loyal small-basket shoppers together with occasional big spenders. Basic Customer Lifetime Value (CLV) splits the problem into two clean models—one predicting whether a customer is active in each future period and one predicting how much they spend when active—then discounts and sums those expected cash flows over your planning horizon.

🎯 Questions It Answers

  • What is the present value of a customer's expected future spend over the next T months or quarters?
  • How strongly does our focal marketing lever (e.g., email touches or loyalty contacts) boost retention vs. basket size?
  • Does a quiet month mean a customer has permanently churned (contractual) or simply paused before returning (non-contractual)?

📂 What Data You Need

  • Customer-Period Panel CSV: Repeated rows per customer across time periods (months, quarters, or weeks).
  • Core Columns: Customer ID, Time Period, Active Flag (0/1), Spend When Active, and 1 focal marketing lever.
  • Horizon & Discount Rate: Number of future periods to project and per-period discount rate.

💡 What You'll Discover

  • Two-Part Model Fit: Separate logistic retention/reactivation model and OLS spend-given-active model.
  • Retention vs. Reactivation Regime: Automatic detection of whether quiet customers stay gone or bounce back.
  • Discounted CLV Trajectory: Period-by-period survival, expected spend, and cumulative discounted CLV per customer.
🔍 Key Nuances: Why Two Models, Quiet Months & Full CLV Upgrade
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1. Will they be active?

A logistic regression predicting the 0/1 is_active flag, separating ongoing retention from reactivation after a quiet period.

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2. If active, how much?

An ordinary regression fitted strictly on the periods where the customer actually purchased, modeling basket size without zero-inflation distortion.

  • Contractual vs. Non-Contractual Regimes: In subscription settings, a canceled customer rarely returns (reactivation near zero). In retail and e-commerce, a shopper who skips March often returns in April—writing them off after one quiet month severely understates CLV. This tool estimates both retention and reactivation probabilities automatically.
  • When to Use the Full CLV Tool: Upgrade to the full version when you need multiple marketing levers with diminishing-returns saturation, segment-specific models, customer covariates, and an ROI targeting simulator.
📐 Under the Hood: Model Equations & Variable Definitions
1. Discounted Customer Lifetime Value (Two-Part Expected Cash Flow Sum)
\[ \mathrm{CLV}_i = \sum_{t=1}^{T} \frac{P(\text{active}_{i,t}) \;\times\; \mathbb{E}[\text{spend}_{i,t} \mid \text{active}_{i,t} = 1]}{(1 + r)^t} \]
💬 Read it in plain English: In each future period t, expected revenue is the probability the customer is active multiplied by their expected spend when active; dividing by (1 + r)t discounts future cash flows into today's dollars and sums them over horizon T.
CLVi
Discounted Customer Lifetime Value OUTPUT ⓘ
Total present value of customer i's expected future purchases over horizon T.
P(activei,t)
Predicted Activity Probability in Period t ESTIMATED ⓘ
Probability between 0 and 1 that customer i transacts in future period t.
E[spendi,t | active]
Conditional Expected Spend When Active ESTIMATED ⓘ
Expected dollar spend in period t given that customer i makes a purchase.
T, r, t
Planning Horizon & Per-Period Discount Rate YOUR DATA ⓘ
Total future periods projected (T), period index (t = 1 … T), and per-period discount rate (r).

STEP 1 — GET SOME DATA

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Use a Case Study

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Upload Your Data

One row per customer per period. Required columns: customer_id, period, is_active (0 or 1), spend. Plus at least one marketing column.

Drag & Drop raw data file (.csv, .tsv, .txt)

Period-level data — one row per customer per period