Identity resolution in direct mail is key to understanding who responds and why, especially when blending offline and online behaviors. Here’s how cookie & pixel tracking, matchback analysis, household-level attribution, and advanced data all work together to create a comprehensive identity resolution framework for your direct mail performance analysis:
1. Cookie & Pixel Tracking: Capturing Digital Behavior
What it is: Cookies and pixels are tools used to track user interactions online.
How it supports identity resolution:
· When direct mail includes a personalized URL, QR code, or vanity URL, recipients often visit a landing page. A tracking pixel (like a Meta or Google pixel) on that page captures anonymous browser behavior.
· Cookies can then associate that activity with broader digital behavior (e.g., product page views, abandoned carts).
· Through partnerships with identity graphs or data onboarding services, that anonymous data can be linked back to individuals or households using deterministic (logged-in user) or probabilistic (device fingerprinting, location, etc.) matching.
Use Case Example: A user scans a QR code on your direct mail piece and browses your website. Even if they don’t convert, their behavior is logged and used for retargeting or later attribution.
2. Matchback Analysis: Proving Lift and Response
What it is: Matchback is the process of comparing responders (purchasers, leads, etc.) to the original mail file to identify those who were influenced by the campaign.
How it supports identity resolution:
· After the campaign ends, a list of new customers or leads is created and matched to the mail file using PII (name, address) or hashed identifiers (email, phone).
· This validates who received the direct mail piece and subsequently converted, even if they didn’t use a tracked URL or coupon code.
Use Case Example: A customer makes an in-store purchase after receiving your mail piece. Their address is matched back to the mailing list, confirming attribution.
3. Household-Level Attribution: Resolving Cross-Person Behavior
What it is: Attribution at the household level links marketing responses across all members of a home.
How it supports identity resolution:
· Sometimes the person who receives the mail isn’t the one who responds (e.g., one spouse receives a catalog, another visits the website).
· Household-level attribution uses address-level resolution to account for shared decision-making, devices, and purchasing.
Use Case Example: Your direct mail piece is addressed to Sarah, but her partner John makes the online purchase. Household-level attribution ensures the mail piece still gets credit.
4. Advanced Data (3rd Party, Behavioral, Psychographic): Enhancing Identity Match
What it is: Enrichment data such as purchase history, browsing behavior, lifestyle, or intent data sourced from third-party providers.
How it supports identity resolution:
· Data onboarding services match offline PII to digital identifiers (hashed emails, mobile ad IDs, cookies) to create a 360° view of the customer.
· Psychographic and behavioral overlays improve targeting precision and post-campaign segmentation.
· This advanced data is also used in look-alike modeling to identify new prospects with high match potential.
Use Case Example: You onboard your direct mail audience and find their device IDs. Post-campaign, you see increased visits from those IDs and connect that activity to the original mail file.
Putting It All Together: The Identity Resolution Flow
1. Direct mail is sent with digital activation cues (QR, PURL).
2. Pixel & cookie tracking logs online activity.
3. Matchback analysis ties offline conversions to the mail file.
4. Household-level attribution assigns credit even if responders differ.
5. Advanced data enrichment links identities across channels and reveals why they converted.
Why This Matters for Marketers
· Improved attribution across channels (especially when online conversion paths are muddy).
· Smarter optimization based on actual responder profiles.
· More accurate ROI calculations from both online and offline conversions.
· Enhanced personalization for future campaigns based on enriched profiles and cross-channel behaviors.
