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Monitor Customer Lifetime Value Models
Customer lifetime value

Detect model drift and maximize customer retention

Customer Life Time Value (LTV) models in production pose a unique set of performance monitoring challenges. Utilize ML observability to improve overall LTV models outcomes and promote customer loyalty, grow profit margins, and boost overall sales.

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Improve customer lifetime value with ML observability solutions

While it’s challenging to account for performance metrics in LTV models, ML observability enables teams to visualize drift between various model environments and versions to easily identify patterns and anomalous distribution behavior. See how Arize can help you automatically monitor for key drift metrics, surface poor performing feature slices, and account for data quality issues.

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Automatically monitor for drift to account for costly model degradation
Manage data quality metrics to ensure high quality training, validation, and production data
Understand your most important features to easily retrain models for variable timelines
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What is Customer Lifetime Value (LTV)?

Customer Lifetime Value (LTV) is the total monetary value of transactions or purchases made by a customer with your business over their entire lifetime. It is a measurement of how valuable a customer is to your company, not just on a purchase-by-purchase basis but across the entire relationship.

How to calculate LTV?

LTV = ((Average Sales ✕ Purchase frequency) / Churn)) ✕ Profit Margin

  1. Average Sales=(Total Sales) / ( Total no. of orders)
  2. Purchase frequency = (Total no. of orders) / ( Total no. of unique customers)
  3. Churn = 1- Retention Rate
  4. Profit Margin = Based on business context

Challenges with LTV

  • Variable Timelines: LTV models must iterate and estimate long-term value based on a best guess timeline depending on product and business type. For instance, an LTV model may predict a vastly different timeline for an established e-commerce store (5 years), a new e-commerce store (3 years), and a dropshipping store (1 year).
  • Delayed Ground Truth: A consumer’s lifespan is an inherent unspecified amount of time. As a result, some predictions can not map to their ground truth for a long time, thus “lifetime” is very difficult to predict accurately.
  • Missing Metrics: When monitoring for key performance metrics, ground truth is necessary to calculate key metrics, such as accuracy. Since LTV models struggle with delayed ground truth, teams are left in the dark about their model performance in production using traditional performance evaluation metrics.
  • Performance Metric Proxy: Since LTV models are often unable to calculate key performance metrics, drift is used as a proxy to evaluate performance degradation in production. In this case, it’s imperative to set an appropriate baseline to monitor for deviations in production. Typically, baselines are set using training or validation data to measure changes in the distribution of feature values, model predictions, and/or ground truth across different environments.

Why is LTV Important?

According to a Criteo survey, 81% of marketers claim that monitoring CLV boosts sales. One of the primary reasons for measuring LTV is optimizing customer retention. As mentioned in the book, Marketing Metrics, the probability of selling to a new prospective customer is 5%–20%, while the probability of selling to an existing customer is 60%–70%. Therefore, by predicting LTV for customers, you can help your business develop strategies to acquire new customers and retain existing ones while maintaining significant profit margins. It also enables your organization to define marketing goals, plan spending to lower acquisition costs and keep retention high. This also highlights the impact of promoting customer loyalty as regular customers tend to spend more on your products, helping you grow and promote your business.







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