Book profile

Predictive Marketing: Easy Ways Every Marketer Can Use Customer Analytics and Big Data

Omer Artun, Dominique Levin

A practical guide showing how every marketer—not just data scientists at giant companies—can use big data, customer analytics, and machine learning to deliver personalized experiences that maximize customer lifetime value.

Predictive Marketing demystifies big data and machine learning for everyday marketers, arguing that the same predictive analytics that powered Amazon, Netflix, and Harrah's are now cheap, accessible, and easy to deploy. Authors Ömer Artun and Dominique Levin lay out a complete primer on how predictive analytics works under the hood, how to build complete customer profiles, and how to manage customers as a value portfolio. They then deliver nine concrete 'plays'—from optimizing marketing spend and predicting customer personas to launching predictive programs that convert, grow, and retain customers—each grounded in real company stories and measurable returns. The book's central promise is to restore the personal, one-to-one relationships of the old corner store at the scale of millions of customers, creating a win-win for customers (relevant experiences), businesses (profitable relationships), and marketers (career-making visibility).

The model

A causal model in which data and predictive analytics capabilities (design levers) enable personalized, relevant customer experiences (psychological/behavioral states) that increase customer lifetime value and enterprise value (outcomes), moderated by customer value segment and life cycle stage.

Frameworks you can use

Focus on and organize around the customer, not products or channels.
Allocate marketing dollars to the right people based on lifetime value.
Give to get: deliver value before and throughout the relationship.
Relevance, not reach, is the key marketing metric.
Differentiate treatment by customer value and life cycle stage.
Start with the end in mind when collecting and analyzing data.

Key terms

Customer Data Integration & Quality: The organizational and technical capability to assemble all customer data into a single, accurate, deduplicated, near-real-time 360-degree profile per customer.
Predictive Intelligence Capability: The capability to apply clustering, propensity, and recommendation models to customer data to generate forward-looking customer insights.
Campaign Automation & Execution: The capability to trigger and orchestrate personalized, predictive-insight-driven campaigns across multiple channels in near real time.
Marketing Relevance & Personalization: The degree to which communications and offers match an individual customer's needs, preferences, context, and life cycle stage.
Customer Engagement: The level and consistency of a customer's active interaction with the brand across channels and over time.
Customer Trust & Loyalty: The customer's confidence in and emotional attachment to the brand, built through valuable and respectful experiences.
Likelihood to Buy/Engage: The predicted probability that a customer or prospect will purchase or engage in a future period.
Customer Value Segment: The tier (high/medium/low value) to which a customer belongs based on lifetime value or profitability.