Gift-giving often feels like a guessing game. Even with the best intentions, people frequently end up giving presents that don’t match the recipient’s current interests, leading to wasted effort and disappointment. Meanwhile, targeted ads—which reflect a person’s actual preferences—are locked away in the algorithms of advertising platforms, inaccessible to the friends and family who could use them to choose better gifts.

A Data-Driven Solution for Gifts

One way to address this problem is by creating a tool that lets users voluntarily share their recent ad interests with select contacts, turning personalized ads into gift inspiration. Here’s how it could work:

• Opt-in sharing: Users grant permission to collect ad data (or manually input ads they’ve seen) from platforms like Google, Facebook, or Instagram.
• Controlled visibility: Instead of making this data public, users could share it with specific groups, like family members planning holiday gifts.
• Curated suggestions: The tool could highlight trends (e.g., “You’ve seen ads for outdoor gear five times this month”) and recommend related products.

For advertisers, this could mean more meaningful engagements—after all, a gift based on genuine interest is more likely to be used and appreciated.

Exploring Stakeholder Benefits and Challenges

For gift-givers, this tool would reduce uncertainty. Instead of guessing, they’d have tangible clues about what the recipient is currently interested in. For recipients, it would mean getting gifts aligned with their preferences without explicitly requesting them—preserving the element of surprise.

However, the idea relies on trust in data privacy. Platforms like Google or Meta might resist integration, but manual exports (similar to “Download Your Data” features) could work as a starting point. An MVP could begin with a simple browser extension that lets users screenshot and share ads before evolving into automated, API-based suggestions.

How It Compares to Existing Tools

Unlike traditional wishlists, which require users to manually add desired items, this idea taps into passive signals—what people are already engaging with. It’s also different from survey-based tools (like Elfster), which rely on direct input, or Pinterest, where users curate aspirational boards. Instead, it uses actual behavioral data (e.g., ads clicked or products browsed) to identify real purchase intent.

By bridging the gap between digital advertising and real-world gifting, this approach could make gift-giving more thoughtful—and less of a gamble.