# Fanatics stopped buying audiences, started buying LTV. Media ROI jumped 19%.

*The sports merch giant optimized for customer value, not clicks, and proved outcome-based buying works for physical goods.*

By **Jenny Huang Goodman MPA MSc MHSA, Principal** — The Stash Edge, Hako Shikin LLC.
Published 2026-06-19.

Canonical: https://www.pops4.com/stash/articles/fanatics-2026-06-19t18-1
Subject: Fanatics
Tags: ltv optimization, media buying, retention, customer data, outcome-based marketing

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Fanatics, the $31 billion sports merchandise platform, rewired its media buying away from demographic targeting and toward customer lifetime value optimization — and lifted LTV by **19%**, according to Digiday. Instead of buying audiences that fit a profile, the brand now buys media based on which placements and creative deliver customers who spend more over time.

The shift is mechanical. Fanatics fed historical purchase data back into its media platforms — Google, Meta, programmatic — and told the algorithms to optimize for predicted LTV, not clicks or first-purchase conversion. The system learns which ad placements and creative variants attract buyers who return, spend more per order, and stay engaged longer. Media dollars move toward those placements. The brand stopped paying for traffic that converts once and disappears.

This works because the signal is tighter. Audience targeting guesses: "This person looks like a sports fan, so show them jerseys." LTV optimization measures: "This placement delivered 400 customers last month; 60 of them bought again within 90 days and spent an average of $210 total. That placement gets more budget." The feedback loop is direct. The platform sees which customers came from which creative and channel, matches them to purchase history, and adjusts bid strategy in real time. Fanatics reported the **19%** LTV lift across campaigns that switched to this model, per Digiday.

The underlying mechanism is that not all customers are worth the same, and most media platforms now let you teach them the difference. A buyer who orders a single $40 hat is worth less than a buyer who orders a hat, returns for a jersey, and signs up for team-specific drops. Standard conversion campaigns treat them identically. LTV-optimized campaigns learn to distinguish them at acquisition and bid more aggressively for the second type. For a physical-product brand, this means media spend flows toward the channels and messages that attract repeat buyers, not one-time bargain hunters.

A small or solo physical-product brand runs the same play with a tighter loop and smaller data set. Start by tagging every customer acquisition source in your order system — UTM parameters, coupon codes, referral links. After 90 days, segment customers by source and calculate average order value, repeat purchase rate, and total revenue per cohort. Identify which sources delivered the highest-value customers. Then manually shift your media budget toward those sources. If Instagram Story ads drove 50 customers who bought once and TikTok drove 30 customers who bought twice, allocate more budget to TikTok, even if Instagram delivered more gross conversions. You are optimizing for durability, not volume.

Once you have six months of data, most platforms — Meta, Google, TikTok — let you upload a customer list with lifetime value tags and create a lookalike or value-based bidding campaign. Export your top **20%** of customers by LTV, upload the list, and tell the platform to find more people like them. Set your campaign objective to "value" rather than "conversions." The platform will deprioritize cheap clicks and prioritize placements that historically delivered high-LTV users. This costs nothing to set up. The only input is clean data: customer email, source, purchase history, total spend. If your system does not track this, add it today. The shift from guessing at audiences to targeting value is table stakes now, and Fanatics just proved it works at scale for physical goods.

The broader pattern is that outcome-based buying is moving downstream. What Fanatics did with a full data science team, a $10,000-a-month Shopify store can now approximate with native platform tools and a spreadsheet. The question is whether you are measuring the outcome that matters.

## The takeaway

Optimize for customer lifetime value, not conversion volume, and your media budget flows toward buyers who return.

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## Publisher

**Hako Shikin LLC** — Virginia Beach, Virginia. Founded 1997. ASI 217876 · DUNS 18-204-6339.
Principal and author: **Jenny Huang Goodman MPA MSc MHSA**.

- Author: https://www.huanggoodman.com/about
- LLM context: https://www.pops4.com/stash/llms.txt
- MCP endpoint, for AI agents: https://mcp.pops4.com/mcp
- Client dashboard: https://dashboard.pops4.com/
- Catalogue: 70,000+ products, 200+ brands