# Fanatics stopped chasing audiences, optimized for lifetime value, lifted LTV 19%

*The sports merch giant rewired its media buying to predict customer worth, not just clicks.*

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-2026-06-19t21-1
Subject: Fanatics
Tags: ltv optimization, outcome-based media, customer acquisition, ad targeting, retention

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Fanatics, the $38 billion sports merchandise platform, documented a **19% lift in customer lifetime value** after switching its campaign optimization from audience targeting to LTV metrics, according to Digiday. The company stopped buying media against demographic segments and interest clusters. Instead, it trained its bidding algorithms to identify and acquire customers predicted to spend more over time.

The mechanics: Fanatics fed historical purchase data—order frequency, cart size, category mix, retention cohorts—into its media platform. It then optimized bids in real time toward users whose behavior patterns matched high-LTV customer profiles, not toward users who fit a target demo. The brand still ran the same creative on the same channels. The only change was the signal the algorithm chased: predicted total spend instead of immediate conversion or audience match.

This works because audience targeting selects people by attributes—age, location, declared interest—while LTV optimization selects people by predicted economic behavior. A 22-year-old in Ohio who likes the NFL is an audience segment. A user whose browsing cadence, referral source, and first-session engagement match the profile of customers who place four orders a year at an average **$140** cart is an LTV signal. The latter predicts revenue. The former predicts relevance, which does not always correlate with spend.

The **19%** lift is the margin between what Fanatics paid to acquire these customers and what those customers returned in total spend over the measurement window. Outcome-based media buying reduces waste on high-intent, low-value converters—users who click, buy once, and vanish—and reallocates budget toward users who return. For a physical-product brand, this distinction separates profit from churn.

A small physical-product brand can run the same play without enterprise data infrastructure. Start by tagging every order with UTM source, medium, and campaign in your order management system or Shopify. After **90 days**, export your customer list and segment by total revenue per customer. Identify the top **20%** by LTV. Note the common acquisition sources: which ad sets, keywords, or referral partners delivered them. Go back into your ad account—Meta, Google, TikTok—and create a Lookalike or Similar Audience seeded from that high-LTV segment, not from all converters. Shift **30–50%** of your prospecting budget to that audience. Measure new-customer LTV at 90 days against your control. If the Lookalike outperforms, expand. If not, refine the seed segment—tighten the LTV threshold or extend the observation window to **180 days**—and rebuild the audience. Most platforms let you upload a customer list and optimize toward it for under **$500** in test budget. The cost is in the discipline to wait 90 days for the LTV data, not in the tooling.

The broader pattern: as acquisition costs rise and attribution windows shrink, optimizing for the *quality* of the customer—not the speed of the conversion—becomes the only sustainable margin lever. Fanatics proved that the same creative, the same channels, and the same budget produce materially different returns when the bid algorithm chases a different target. The question is whether your brand has the patience to measure it.

## The takeaway

Optimize bids for predicted lifetime value, not audience fit, and shift budget toward customers 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