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Here's Why Proprietary Data Is Your Programmatic Differentiator (And How to Build It for Long-Term Success)

In an ad tech landscape where third-party cookies are fading, competition is fierce, and users demand more control over their data, one asset stands out as a game-changer: proprietary data.

For programmatic advertisers and publishers, it’s no longer just "nice to have"; it’s the foundation of sustainable growth.  

In this blog, we'll break down what proprietary data is, why it matters, how to build and use it effectively, and how it solves real-world programmatic challenges.

What Exactly Is Proprietary Data in Programmatic?

Proprietary data is the first-party data that your business owns, controls, and collects directly from your audience or operations. It's not bought, shared, or repurposed from third parties; it's yours, and yours alone.  

In programmatic, this includes:  

  • For advertisers: Data from your website/app (e.g., which product pages users linger on, how they interact with ads, cart abandonment patterns), CRM data (past purchases, email engagement), or campaign performance (which creatives drive 2x more clicks for your brand).
  • For publishers: Data on how users engage with your inventory (e.g., "users from Southeast Asia spend 3x longer on video ads than banner ads"), which advertisers' ads perform best on your site, or peak traffic times for your audience.

Unlike generic third-party data, proprietary data is tied specifically to your business. It's not diluted by overuse across the industry, which makes it infinitely more powerful.  

Why Proprietary Data Isn’t Optional Anymore

Third-party data once powered programmatic targeting, but those days are fading away:

  • Browsers like Safari and Chrome have phased out third-party cookies.
  • Regulations like GDPR and CCPA penalize misuse of non-consensual data.
  • Users are increasingly wary of sharing information with unknown parties.

In this environment, proprietary data isn't just a "differentiator"; it's the only way to build trust, precision, and resilience. Here's why:  

1. It solves the "relevance crisis" in targeting

Generic targeting (e.g., "moms aged 30–35") leads to wasted ad spend. Proprietary data lets you target intent, not just demographics.

For example, a gaming advertiser notices (via their proprietary app data) that users who watch "how-to" videos for Level 10 are 5x more likely to buy in-game currency. They use this data to target similar users with ads for Level 10 tips, doubling their conversion rate.

Without this unique insight, they'd be stuck targeting "gamers aged 18–25" like every other advertiser.

2. It builds trust with users (and regulators)

Users dislike irrelevant ads, and they hate feeling tracked without consent even more. Proprietary data is collected directly from users who've interacted with your brand, so it's more likely to be consensual and transparent.

For example, a publisher that asks users, "Can we track your ad preferences to show you more relevant content?" (and uses that data to serve better ads) sees much higher ad engagement than one relying on third-party trackers.  

Regulators agree: Data collected with clear consent is far less risky than murky third-party data, reducing legal headaches.  

3. It future-proofs your strategy against privacy changes

When Apple launched its App Tracking Transparency (ATT) framework with iOS 14.5, upending digital marketing measurement, advertisers reliant on third-party data saw attribution accuracy plummet by up to 70%, and this was amplified by 96% of US iPhone users opting out of cross-app tracking within the first month.

In contrast, those leveraging proprietary data pivoted swiftly, relying on in-app behavior (e.g., "users who open the app 3x in a week") to target high-value audiences, effectively weathering the privacy-driven upheaval.

Proprietary data isn't dependent on cookies, device IDs, or industry-wide tracking tools. It's tied to your audience's interactions with your brand, making it immune to external shifts.  

4. It drives better monetization for publishers

Publishers with proprietary data can prove their inventory's value to advertisers. For example, a news site with data showing "readers of our 'sustainable living' section click 2x more on eco-friendly product ads" can charge a 20% premium for that inventory.  

Without this data, they're stuck competing on price alone, commoditizing their most valuable asset.  

How to Build and Activate Proprietary Data (Step-by-Step)

Owning data is useless if it's sitting unused in a spreadsheet. Here's how to turn it into a working asset:  

Step 1: Collect the right data (not just more data)

Start by defining your goals, then collect data that directly fuels them.  

  • For advertisers: If your goal is to reduce cart abandonment, track "time spent on checkout page," "which payment method users hesitate on," and "whether they viewed a retargeting ad before returning."
  • For publishers: If your goal is to boost CPMs, track "ad placement engagement" (e.g., "in-article ads get 50% more clicks than header ads"), "audience retention during ads," and "which advertiser categories align with your top-performing content."

Pro tip: Always tie data collection to user consent. Use clear pop-ups (e.g., "We use your activity to show you better ads, learn more or adjust settings") to stay compliant and build trust.  

Step 2: Break down data silos

Data is only powerful if it’s connected. Most businesses collect data across tools (websites, apps, CRMs, ad platforms), but these data live in separate silos, making it hard to see the full picture.  

Fix this by:

  • Using a CDP (Customer Data Platform) or DMP (Data Management Platform) to unify data into a single user profile. For example, merge a user's website browsing history with their in-app purchases to create a 360° view.
  • Collaborating across teams: Marketing, sales, and product teams often collect overlapping data. Share it to avoid gaps.

Step 3: Activate it in real time (where it matters most)

Programmatic moves fast; your data should too. Feed your proprietary data into your DSP (for advertisers) or SSP (for publishers) to power real-time decisions.

Examples:  

  • An advertiser uses their proprietary "high-intent" user segment (e.g., "users who added items to cart in the last 24 hours") to bid more aggressively on ad slots targeting those users.
  • A publisher uses real-time data on user engagement (e.g., "this user is spending 5+ minutes on the page") to serve higher-CPM ads, knowing the user is more likely to pay attention.

Step 4: Iterate with "data feedback loops"

Proprietary data isn't static; it gets better with use. Create feedback loops to refine your approach:  

  1. Test a new segment (e.g., "users who clicked on a discount ad").
  2. Measure performance (e.g., "this segment has a 15% higher conversion rate").
  3. Double down on what works (e.g., expand targeting to similar users).
  4. Discard low-value data (e.g., "time spent on the homepage" that doesn’t correlate with conversions).
  5. Example: A publisher notices that data on "user location + device type" predicts ad engagement better than "age + gender." They shift their data collection to focus on those high-impact signals.  

Common Challenges (and How to Solve Them)

Building proprietary data isn't without hurdles, though. Here's how to tackle the biggest ones:  

Challenge 1: "We don't have enough data."

You don't need millions of users; you need quality data from your core audience. Start small: A niche publisher with 10,000 daily users can still uncover powerful insights (e.g., "80% of our users engage with ads after reading reviews").  

Fix: Prioritize data from your most valuable users (e.g., repeat customers, high-time-spent visitors) and expand from there.  

Challenge 2: "Our data is messy or outdated."

Dirty data (duplicates, errors, old entries) leads to bad decisions.  

Fix: Automate cleaning with tools like Precisely or Qlik. Set rules (e.g., "delete user data after 12 months of inactivity") to keep it fresh.  

Challenge 3: "We don't know how to activate it."

If your team lacks technical skills, start with simple integrations. Most DSPs/SSPs let you upload custom audiences with just a CSV file.  

Fix: Partner with your ad tech provider to walk through activation steps. Many platforms offer templates for common use cases (e.g., "retargeting high-intent users").  

Final Thoughts

Your proprietary data lets you target with precision, build trust with users, and adapt to industry changes, all while standing out from competitors stuck in “me-too” strategies. Your platform generates unique data every day. Nurture that data, activate it, and watch it become your most valuable tool for growth.  


If you’re looking to craft more effective, AI-driven ad experiences, start with GatherStar today. And if you want dedicated expert support to maximize your results, reach out to the GatherStar team to explore how we can help elevate your programmatic campaigns.