What You Can (and Cannot) Learn from Your Post-Buy Data

How Peer39’s Analytics Suite helps turn measurement into momentum.

Post-buy reports are often treated like a final report card. The campaign ends, the PDF lands in your inbox, and you scan completion rates, reach, frequency, and cost. Helpful, but not the full story.

Post-buy data is descriptive by nature. It shows what happened and where, but not always why it happened or how to improve next time. That’s where Peer39’s Analytics Tool Suite bridges the gap, mapping every performance signal back to the contextual categories used at pre-bid.

When pre- and post-buy views share the same taxonomy, advertisers can finally connect planning, activation, and optimization to turn reports into insights that drive performance.

What Post-Buy Data Reveals

At its best, post-buy reporting provides a high-fidelity map of delivery and outcomes.
With Peer39 Analytics, buyers gain a complete, transparent view of campaign activity across channels and formats:

• Channel and content composition: View delivery across CTV, OLV, display, and mobile in-app, down to show, genre, or page type.
• Performance metrics: Completion rates, quartile completions, CTR, cost per completed view, Attention Index, and other KPIs mapped to contextual dimensions.
• Quality indicators: Brand suitability events, invalid traffic detection, MFA (Made for Advertising) sites, and screensaver or casting utility impressions that signal wasted CTV spend.
• Audience and environment insights: Daypart, device, and geographic breakdowns paired with contextual signals such as content type or negative sentiment help show where performance truly lives.

These data points make it easy to answer the first round of questions:
Where did we run? What performed above or below average? Which segments carried the most weight? Where did risk or waste appear?

What Post-Buy Data Cannot Reveal on Its Own

Even the most detailed result set has blind spots.

• It doesn’t explain intent or tone. Two shows may share a topic but carry opposite meanings. Without suitability or sentiment context, performance alone can be misleading.
• It rarely uncovers causal drivers. A high completion rate on one channel might be fueled by just a few standout programs, something Peer39 exposes through program-level analysis.
• It hides targeting drift. Without mapping pre-bid contextual categories to post-buy reports, advertisers can’t easily see where delivery spilled into unverified or blind inventory.
• It’s often delayed. If you only evaluate campaigns after the fact, optimization opportunities disappear with the spent budget.

Close the Loop with Peer39

The fix: connect your pre-bid taxonomy to your post-buy analytics.

Peer39 makes this seamless because every Analytics Dashboard is built from the same contextual framework used in pre-bid activation. That means:

• More than 150 measured data dimensions mapped directly to Peer39 pre-bid categories
• CTV Quality Score combining performance and suitability into one view for faster optimization
• Keyword-level analytics for display and OLV, the industry’s first at that level of granularity
• Attention Index integrated into all reports, including fraud detection and daily updates
• MFA analytics to reveal spend wasted in low-quality or made-for-ads environments

Together, these tools provide a continuous learning loop so advertisers can track what environments drive performance and feed those insights back into planning and creative.

A Practical Workflow Using Peer39 Analytics

1. Plan with clear hypotheses. Use Peer39 pre-bid data to predict which content types, channels, or genres should perform best.
2. Tag your plan with mirrored categories. Because Peer39 Analytics uses the same taxonomy for reporting, your pre-bid strategy and post-buy results stay in sync.
3. Use ongoing visibility. Pull dashboards by content type or CTV Quality Score to spot drift, waste, or early winners before the campaign ends.
4. Diagnose with layered insights. Combine metrics like Attention Index, suitability, and content type to understand why outcomes differ.
5. Document and optimize. Record what you paused, scaled, or refined, and use those learnings to strengthen the next brief.

Pitfalls to Avoid

• Overreacting to small samples. Add minimum delivery thresholds before drawing conclusions.
• Chasing vanity metrics. Always pair outcome metrics such as completion rate with Peer39’s suitability and quality insights.
• Assuming app-level results equal content-level performance. Peer39’s program- and page-level intelligence shows that context drives outcomes.

A Short Checklist for Strong Post-Buy Practice

 ✅ Were all pre-bid categories mapped to Peer39 analytics before launch?
✅ Did we validate delivery against targeting intent mid-flight?
✅ Did we identify and exclude MFA or unsafe inventory using Peer39’s Safety and Suitability data?
✅ Did we document which contextual segments drove outsized results?
✅ Did we integrate those insights into the next plan?

The Bottom Line

Post-buy data is powerful, but context completes the picture.

By mapping Peer39’s contextual intelligence—spanning CTV, OLV, and display analytics—into post-buy reporting, advertisers turn static reports into dynamic optimization.

You don’t just see what happened.
You understand why and what to do next.

That’s how Peer39 turns measurement into momentum.