# Fanatics ditched demographic targeting, optimized ad spend for lifetime value, lifted LTV 19%

*Sports merchandise giant tied media buys directly to predicted customer value, not who you are but what you buy long-term.*

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-2026-06-19t15-1
Subject: Fanatics
Tags: ltv, media buying, retention, fanatics, outcome-based

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Fanatics, the sports merchandise and digital platform, stopped buying media on audience segments and started buying it on predicted customer lifetime value, according to Digiday. The company documented a **19%** lift in LTV after the shift. Instead of targeting age, income, or interest cohorts, Fanatics now feeds first-party purchase data and retention curves into its ad platform and bids higher on users the model predicts will spend more over twelve to twenty-four months.

The mechanics: Fanatics built a cohort model that scores each converting user by projected LTV using past transaction frequency, average order value, category breadth, and email engagement. Those scores flow back into Meta, Google, and programmatic platforms as conversion values, and the algorithms learn to bid up on traffic that trends toward high-LTV outcomes. The brand no longer optimizes for cost per acquisition alone. It optimizes for cost per unit of predicted lifetime revenue, adjusting bids in real time as the model updates.

Why it worked: Traditional audience targeting treats all conversions as equal. A first-time buyer who spends forty dollars and never returns counts the same as a first-time buyer who spends forty dollars, comes back eight times, and joins the loyalty program. When you optimize for those outcomes separately, you train the media platform to find more of the high-repeat buyer and fewer of the one-and-done transaction. Fanatics was already sitting on years of purchase history across jerseys, hats, memorabilia, and trading cards. The data showed clear bifurcation: some customers buy once around a championship, others become quarterly repeat buyers. Feeding that pattern back upstream turned the ad auction into a tool for customer selection, not just customer acquisition.

The retail mechanism is portable. If you sell a physical product with repeat potential, candles, supplements, apparel, outdoor gear, you have the same data structure. Pull the last twelve months of orders. Segment customers by number of purchases. Calculate average LTV for each segment: one-time, two-time, three-plus. Use that figure as the conversion value in your ad platform. On Meta, you assign a dynamic event value to the purchase event. On Google, you send an enhanced conversion value into the tag. The algorithm reads the higher value and reallocates budget toward placements and creatives that attract those buyers. You do not need Fanatics-scale data. A Shopify store with two hundred orders and twenty repeat customers has enough signal to start. Set your one-time buyer value at average order value, your repeat buyer at twice that, run it for thirty days, and watch cost per repeat customer drop.

Start with a simple three-tier model: one purchase, two purchases, three-plus. Export order histories, calculate LTV for each tier, map those values into your conversion tracking. If your repeat buyers spend **$180** lifetime and your one-time buyers spend **$60**, tell the platform a repeat buyer is worth three times as much. The bid logic adjusts. You pay more to acquire the right customer and less to acquire the wrong one. No new creative, no new landing page. The entire play runs in the event payload. Most small brands can implement this in under four hours using Shopify webhooks or a Zapier pass-through to Google Tag Manager.

The broader pattern: outcome-based media buying is replacing demographic targeting across categories where repeat matters more than volume. Fanatics proved the play at scale in a vertical where customer loyalty was historically assumed to be team-driven and unmanageable. The **19%** LTV lift, per Digiday, came from better customer selection, not better creative or better product. If you sell consumables, collectibles, or anything with a reorder curve, the same mechanism applies. The platform wants to spend your budget efficiently. When you teach it what efficiency actually means for your business, it delivers.

## The takeaway

Stop optimizing for conversions; optimize for the value of the converter, and the platform finds better customers.

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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
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