.. _dpa-lift-analysis-for-direct-mail-campaigns-concept-process-and-business-value: =============================================================================== Lift Analysis for Direct Mail Campaigns: Concept, Process, and Business Value =============================================================================== **Stage 8 · 📊 Model Evaluation** · Lesson 56 of 56 · *advanced* :doc:`◀ Previous · AUC–ROC Curve: Evaluating Classification Model Performance <55-auc-roc-curve-evaluating-classification-model-performance>` · :doc:`↑ Section ` .. important:: **✨ AI-generated content.** This page was written with the assistance of an AI language model and is provided as a learning aid. Despite careful review, it may still contain mistakes, omissions, or out-of-date information. Whether you are new to the topic, a team lead, or a senior practitioner, treat it as a starting point rather than an authoritative reference: read it critically and independently verify anything you act on (code, commands, figures, and factual claims) against official documentation and primary sources before relying on it. A budget, not a threshold --------------------------- The course closes with the evaluation tool born in **direct mail** — the corner of marketing where every contact costs real money. A campaign can afford to mail, say, only **20%** of the customer base. The question is not "which cases are positive?" but "**whom should we contact first?**" — a **ranking** problem with a budget. **Lift analysis** measures how much better a model's ranking is than mailing at **random**. Score, sort, slice -------------------- The procedure is simple. **Score** every prospect with the model (their probability of responding), **sort** the list from highest to lowest score, then **slice** it into ten equal groups — **deciles** — so decile 1 holds the top 10% of prospects by model score. Mailing a past campaign's data through this lens shows the **response rate per decile**: if the model ranks well, decile 1 responds far above average, decile 2 next, and the bottom deciles barely respond at all. Gains and lift ---------------- Two curves summarise the table. The **cumulative gains** curve shows, for each targeting depth, the share of **all responders** captured: for example, the top decile alone might contain **28%** of all responders, and the top two deciles together over half. The **baseline** is random targeting — contact X% of customers, reach X% of responders. **Lift** is the ratio of the two: .. math:: \text{lift at depth } X = \frac{\%\ \text{of responders captured in the top } X\%}{X\,\%}. A lift of **1** means the model adds nothing; a lift of **2** at 20% depth means the model finds **twice** the responders random mailing would. The **further** the gains curve rises above the baseline, the more valuable the model — and scikit-plots draws both the cumulative-gain and lift curves directly from scores. The business payoff --------------------- Lift converts model quality into **money**. If the top four deciles capture most responders, the campaign mails **40%** of the base, captures the bulk of the responses, and saves the cost of the other 60% — a concrete, defensible decision drawn straight from the chart. It is a fitting end to the course: the journey that began with *why we analyse data* ends with a model, honestly evaluated on held-out data, telling a business **exactly what to do** — the prescriptive payoff the first lesson promised. .. hint:: **Related lessons:** :doc:`AUC–ROC Curve: Evaluating Classification Model Performance <55-auc-roc-curve-evaluating-classification-model-performance>` · :doc:`Binary Classification Model Evaluation and Threshold Optimization <53-binary-classification-model-evaluation-and-threshold-optimization>` · :doc:`Recency, Frequency, and Monetary Value (RFM) <28-recency-frequency-and-monetary-value-rfm>` · :doc:`Why Do We Analyze Data? <01-why-do-we-analyze-data>` .. seealso:: **Source article** Adapted (context, re-expressed) in our own words from: `https://insightful-data-lab.com/2026/01/16/lift-analysis-for-direct-mail-campaigns-concept-process-and-business-value/ `__ (insightful-data-lab.com). .. tags:: purpose: reference, topic: data analysis, topic: data preparation, level: advanced