WPP is deploying InfoSum's data clean-room technology across its network in response to Apple's Safari tracking restrictions, according to Digiday. The move signals a structural shift in how large agencies acquire, share, and activate customer data for physical product campaigns—particularly in categories where third-party cookies once powered audience targeting and attribution.
Apple's Intelligent Tracking Prevention (ITP) now blocks cross-site and cross-device tracking by default in Safari, which commands roughly 37% of mobile browser share in the United States. That restriction eliminates the persistent identifiers agencies historically used to track customer journeys across publisher sites, social platforms, and retailer storefronts. For physical product marketers running omnichannel campaigns—where a customer might see an Instagram ad, visit a brand site, then convert at Target.com—the loss of that thread breaks attribution and suppresses retargeting.
InfoSum's platform allows brands and agencies to match first-party customer lists—email addresses, purchase histories, loyalty program data—without sharing raw personally identifiable information. Each party retains control of its own data; the clean room executes queries and returns aggregated insights or activation segments. WPP is using the tool to connect client CRM data with retailer purchase files and publisher audience segments, rebuilding the targeting and measurement infrastructure that Safari's block dismantled. The agency reported the approach yields match rates comparable to cookie-based methods while satisfying privacy audits from legal and compliance teams.
The underlying mechanism is portable. Physical product brands lose granular tracking when browsers block third-party cookies, but they retain owned first-party assets: email subscribers, SMS lists, loyalty members, direct purchasers. Clean-room platforms allow those brands to compare their customer files against a retailer's transaction log or a publisher's registration database, identify overlaps, and activate lookalike audiences or suppression segments—all without exposing individual customer records. The shift trades scale for precision; match rates drop when identifiers are hashed and privacy thresholds applied, but the resulting segments carry documented purchase intent and pass privacy review.
A small physical product brand can run the same play without enterprise software. Start with your owned customer list: Shopify purchasers, Klaviyo email subscribers, or WooCommerce transaction exports. Approach a retail partner—Amazon, Target, a specialty chain—and propose a data collaboration. Offer to share your hashed email file so the retailer can identify which of your customers also shop their stores, then ask for an aggregated report: purchase frequency, basket composition, category crossover. Many retailers now operate their own clean-room services (Target Roundel, Walmart Connect, Amazon Marketing Cloud) and will execute these queries for brands spending $10,000–$25,000 per quarter in media.
Use the overlap insight to guide product assortment, pricing, and promotional timing. If 40% of your DTC skincare buyers also purchase prestige beauty at Sephora, you know your brand can command premium shelf pricing and should focus co-op dollars on in-store education rather than acquisition. If your outdoor gear customers index high for camping and low for fishing at REI, you refine your product messaging and SKU mix accordingly. The retailer never shares individual customer names; you never expose your full email list. Both parties gain decision intelligence within privacy rails.
For paid media activation, negotiate a segment-building deal. Provide your hashed customer file to the retailer's ad platform and request a lookalike audience or a suppression segment. The retailer matches your list internally, builds the audience, and allows you to target it through their owned media—on-site display, sponsored product ads, or offsite social extensions. You pay the retailer's media rates, typically $15–$40 CPM, and reclaim some of the targeting precision Safari's block removed. The segment never leaves the retailer's system; you gain addressability without third-party cookies.
Agencies like WPP are industrializing this approach with platform deals and centralized governance, but the core play scales down. Any brand with 5,000+ email subscribers and a retail partnership can propose a data match, request an overlap analysis, and negotiate audience access. The constraint is relationship capital, not technology budget. Start with your largest retail account, frame the collaboration as category intelligence rather than media arbitrage, and offer reciprocal insight: share your DTC customer survey data or product usage findings in exchange for purchase overlap.
The broader pattern extends beyond retail. Publishers, fulfillment providers, and subscription platforms all hold first-party transaction data and face the same tracking constraints. As Safari's restrictions expand and Chrome phases out third-party cookies in 2025, physical product marketers rebuild targeting infrastructure around owned data and permissioned partnerships. The brands that move early secure collaboration agreements while retailers still view data-sharing as differentiation; late movers face standardized self-serve tools with narrower flexibility.