ASOS Uses Machine Learning To Understand Customer Value (For the technically inclined, a full description is in the paper below)

The paper discusses a machine-learning framework that uses embedding in looking across all of product's customer views, and then assigns a value to each customer based on the characteristics of customers who he or she appears close to in product's view sequences. This gives a data output that can help differentiate high value from low value customers. This method results in an uplift in AUC (Area Under Curve), meaning that the system with the learned features was better than the system without learned features at distinguishing between the two classes (high-value and low-value).

Customer lifetime network value: customer valuation in the context of network effects

Nowadays customers are increasingly connected and extensively interact with each other using technology-enabled media like online social networks. Hence, customers are frequently exposed to social influence when making purchase decisions. However, established approaches for customer valuation mostly neglect network effects based on social influence. This leads to a misallocation of resources. Following a design-oriented approach, the paper develops a model for customer valuation referred to as the customer lifetime network value (CLNV), incorporating an integrated network perspective.

Estimating Customer Lifetime Value Using Machine Learning Techniques

With the rapid development of the civil aviation industry, high-quality customer resources have become a significant way to measure the competitiveness of the participating airlines. It is well known that the competition for high-value customers has become the core of airline profits. The research of airline customer lifetime value can help airlines identify high-value, medium-value and low-value travelers. What is more, an airline can make resource allocation more rational, with the least resource investment for maximum profit return.

An Introduction to Predictive Customer Lifetime Value Modeling

This article provides a basic understanding of predictive customer lifetime value modeling. It defines the concept and explains why such models are also called customer equity models. Probabilistic models will help us define these parameters at the customer level and make inferences about future purchases and value. Machine learning and Markov models are also worthy approaches to CLV modeling, but they need to be tweaked and sometimes customized to fit the particulars of a business situation. The Pareto/NBD model focuses on modeling lifetime and purchase count.

Subscription Scorecard Metric Deep Dive: Customer Lifetime Value and Customer Acquisition Cost

At the heart of understanding your relationship with customers are two key metrics that should be evaluated together: customer lifetime value (CLTV) and customer acquisition cost (CAC). This article explores how CLTV and CAC provide even greater insight than when used individually. This article outlines the purpose of these two metrics, highlights various ways each can be calculated, and provides examples of real world applications.

Understanding Customer Engagement - How to Map and Make Sense of the Metrics that Matter

Knowing that customer engagement varies according to companies and objectives, this article explains in detail common framework of successful customer engagement metrics. eMarketer experts explain it all; from stating business-level objectives to strategically building a plan, to taking actions that speak the most to your specific customers in different phrases and targeted aspects.

Measure The Return On Sales and Marketing Investments with the CLV:CAC Ratio

Seasoned marketers know that retaining customers is about much more than click-through rates. Particularly for subscription-based companies, an important measurement to remember is the ratio between a customer's lifetime value and their acquisition cost (CLV:CAC). This article shows what numbers to use to understand the spending required to maximize this important ratio.

SaaS Metrics 2.0 – Detailed Definitions

Over time, key metrics for calculating marketing returns have shifted to become more customer-centric. One key ratio for any marketing manager to be aware of is to know is customer lifetime value to customer acquisition cost (CLV:CAC). Often when calculating CLV:CAC one can get lost in the details of ARPA, bookings, and churn rate. At this point it can become difficult to know exactly how best to use these calculations and adjust for particular situations.

A guide to customer lifetime value, customer acquisition cost, and unit economics

How do you truly measure the health of your business? While the day-to-day financial health of a company are essential to running the enterprise, the CMO's focus has to be on the customer. One important calculation for measuring customer satisfaction and resulting profitability is LTV:CAC, the ratio of customer lifetime value to customer acquisition cost. This guide will show you exactly how to calculate this ratio. Through a case study of Starbucks, you will be able to determine the best methods for this calculation and the right one to utilize for your company.

Merging Social Media And Direct Mail

Besides a number of ways of connecting social media and direct mail, such as QR Codes and URLs, the article offers practical ways to refine a direct mail campaign by studying social media analytics. The three dynamic examples in the preceding paragraph of unified social media and direct mail campaigns can heighten the influence and reach of a company’s marketing strategy.

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