How Data-Sharing Ads Work Without Losing Your Customers

Why this matters

Third-party cookies are fading out. Platforms like Shopify ask merchants to pool their own customer data to keep running effective targeted ads. That’s powerful—but merchants worry: “If I share, will rivals use my data to steal my best customers?” This paper proposes a design that makes sharing worthwhile, even for cautious merchants.

The big idea

• Treat customer data and ad slots like tradable goods.
• Score ad matches so each customer sees the merchant with the best fit (highest quality x likelihood to click).
• Use simple payments and tie-breaking rules to ensure every merchant is at least as well off as before sharing.
• At large scale, the platform can implement everything through three markets that are easy to understand.

The Three Markets, Explained Visually

To make data-sharing fair and profitable, the platform creates three simple markets. Each market serves a different type of merchant, but all connect through the platform’s central “brain,” which ensures balance and prevents anyone from losing out.

Imagine this as a digital marketplace for advertising. Some merchants “sell” data for steady income. Others “exchange” data for better ad targeting. Big players “buy” access to premium audiences. The platform acts as the market organizer, keeping trades fair, setting prices, and ensuring no merchant loses customers unfairly.

What Problem Is This Solving?

Sharing data across many merchants makes ads far more accurate — but it also creates a new risk: competitors might target your customers using the same shared pool of information. This research tackles that challenge by redesigning how platforms handle shared data to make it both profitable and safe for everyone involved.

Here’s the core issue and how the model fixes it:

• Fairness Merchants stay protected. Each seller is guaranteed to earn at least as much as they would have by keeping their data private — no one loses customers or revenue by participating.
• Precision Customers see smarter ads. The platform uses a transparent “scoring rule” that matches every shopper with the merchant most relevant to them — boosting click-through rates and satisfaction.
• Balance Payments keep things fair. If one merchant risks losing customers to another, the system automatically adjusts with small transfers or credits, keeping everyone motivated to share data.

In short: the design builds trust. It allows merchants to benefit from collective intelligence (better targeting and engagement) without sacrificing what matters most — their own loyal customers.

How the Platform Decides Which Ad to Show

When many merchants want to reach the same customer, the platform needs a fair way to decide whose ad appears. It does this using a simple scoring system that measures both relevance and value.

Each merchant gets a score based on two key factors:

1. Click Likelihood (CTR) – How likely this customer is to click that merchant’s ad.
2. Click Value (Profit Margin) – How much that click is worth to the merchant if it happens.

The platform multiplies these two numbers (Value × CTR) to get a single “quality score.” The merchant with the highest score wins the ad spot for that customer. If two merchants tie, the platform applies a small, fair adjustment to keep everyone motivated to share data in the future.

In simple terms: the best overall offer wins the ad. It’s not just about who pays more, but who offers the most relevant and valuable match for the customer. This keeps ads efficient, fair, and rewarding for everyone — merchants, platforms, and consumers alike.

Who benefits?

Real-World Parallels

Although the model was designed for digital advertising, its logic applies to many other real-world systems where different players need to exchange resources fairly — especially when those resources vary in value and quality.

The study highlights that this “bundle-and-balance” method — combining multiple small markets and using transfers to keep everyone satisfied — could improve several complex industries:

• Spectrum Auctions Governments sell or reallocate radio frequencies to telecom companies.
  Problem: companies want the best bands without overpaying, and regulators must avoid monopolies.
  How this model helps: it can organize the bidding into balanced sub-markets, aligning incentives so companies get fair access while the public still benefits from competition.
• Carbon Credit Markets Firms trade pollution rights (credits) to meet emissions targets.
  Problem: large polluters can dominate or manipulate prices, discouraging greener behavior.
  How this model helps: a “fair exchange” mechanism could set transparent rules and payments so all participants — from small innovators to big emitters — have reason to cooperate and reduce carbon output.
• Public Resource Allocation Governments and organizations often need to distribute scarce goods — like funding, energy, or vaccines — across many groups.
  How this model helps: by using data-driven scoring and compensation, allocations could become more efficient, transparent, and politically fair.

In essence, this research isn’t only about ads — it’s about building trustworthy digital markets in any domain where cooperation beats competition. Whenever people or companies must share sensitive data or scarce resources, this model offers a blueprint for keeping the process fair, incentive-compatible, and efficient.

Key takeaways

• Sharing data can be safe and profitable if the platform guarantees your outside option.
• Three markets—sell, exchange, buy—make the system practical at scale.
• A simple scoring rule plus fair tie-breaks decides who shows the ad—no black magic needed.
• Transfers (payments) neutralize any remaining poaching risks.