# Fanatics lifted campaign performance 19% by optimizing for customer lifetime value instead of audience demos

*The sportswear giant ditched demographic targeting for outcome-based bidding, proving LTV signals beat reach metrics.*

By **Jenny Huang Goodman MPA MSc MHSA, Principal** — The Stash Edge, Hako Shikin LLC.
Published 2026-06-21.

Canonical: https://www.pops4.com/stash/articles/fanatics-2026-06-21t06-2
Subject: Fanatics
Tags: ltv optimization, outcome-based bidding, customer cohorts, meta advertising, repeat purchase

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Fanatics abandoned traditional audience targeting and rebuilt its media campaigns around customer lifetime value, producing a **19% lift** in LTV outcomes, according to Digiday. The sports merchandise retailer stopped optimizing for demographic reach and started feeding historical purchase data directly into its ad platforms, bidding only on users who resembled high-value repeat buyers.

The mechanics are straightforward. Fanatics segmented its customer file by total spend and purchase frequency, then built lookalike audiences from the top quartile. Instead of targeting "men 18-34 interested in sports," the campaigns optimized for predicted LTV using Meta and Google's value-based bidding tools. The platform algorithms learned which users converted into repeat customers, not just first-time buyers, and adjusted bids in real time. Fanatics reported the **19%** improvement in LTV per acquired customer while maintaining the same media spend, per Digiday.

This worked because audience targeting optimizes for the wrong outcome. Demographic and interest-based campaigns maximize clicks or first purchases, flooding your customer file with one-and-done buyers who tank your payback period. LTV optimization flips the objective: the algorithm hunts for users whose behavior patterns match your best customers, even if they fall outside your assumed demographic. A 45-year-old who buys three jerseys a year is worth more than ten 22-year-olds who each buy one hat, but traditional targeting treats them identically. By feeding actual revenue data back into the bidding system, Fanatics taught the platforms to find more of the former and fewer of the latter.

The play scales down without trouble. Export your customer file from Shopify or your ESP and tag every email with total order value. Sort by revenue and pull the top **25%** into a segment. Upload that list to Meta as a custom audience, then build a 1-3% lookalike. In your campaign setup, switch the optimization event from "purchase" to "purchase value" and set your target ROAS to **2.5x** or whatever your top quartile averages. Let the campaign run for **14 days** without touching it while the algorithm learns. Your cost per acquisition will likely climb **15-30%** in the first week, but your second-order rate should jump inside 60 days. Track cohort LTV at 30, 60, and 90 days, not just first-order ROAS. If you are running Google, use their Customer Match and Value rules in Performance Max with the same top-quartile segment. Budget: no incremental cost beyond your current ad spend, just a reallocation of bidding logic.

For brands with thin customer files, start smaller. Run two identical campaigns for **seven days**, one optimized for conversions and one for purchase value, each at **$500**. Compare the 30-day repeat rate between the two cohorts. Even a **5-8%** difference in repeat purchase justifies the switch, because the compounding effect over six months covers the higher entry cost. If your average order is under **$40**, consider optimizing for "add to cart" among your top-quartile lookalike first, then retarget cart-adds with a purchase-value campaign. The goal is to move the algorithm's learning away from impulse clickers toward users who exhibit buying patterns that correlate with repeat behavior.

The broader pattern is that media platforms now have enough data to predict downstream behavior, not just immediate conversions. Optimizing for the wrong event still gets you customers, but the wrong customers at the wrong price. Fanatics proved that feeding better outcome data into the same ad budgets changes who shows up, and who shows up determines whether your cohorts pay back or bleed out.

## The takeaway

Switch campaign optimization from conversion volume to purchase value using your top-quartile customer list as the training set.

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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