by Cassandra Balentine
Many AI-based tools are well suited for variable data publishing (VDP), such as natural language processing (NLP) for address data cleansing. We discuss more on AI and VDP in our September issue.
Ayelet Szabo-Melamed, VP marketing, XMPie, says with address management, AI can assist with standardization, correction and duplicate detection, particularly when dealing with incomplete or inconsistent customer records. However, these tools usually work best when combined with established data hygiene processes rather than replacing them entirely. “Data quality is still fundamentally a governance challenge as much as a technology challenge.”
Address quality is a problem that looks simple until you are staring at 50,000 records with inconsistent formatting, abbreviations, missing postal codes, and duplicates hiding under slightly different name spellings, comments Naimish Patel, VP of sales, OnPrintShop.
He notes that manual cleaning at that scale is not just slow, it introduces new errors.
Rather than rigid field-matching rules, NLP-based tools understand the semantic meaning of address components. “They can recognize that ‘1st Avenue,’ ‘1st Ave,’ and ‘1 Ave.’ refer to the same street. They apply probabilistic matching to detect near-duplicate records where a name is misspelled or a middle initial is dropped. They also flag anomalies based on learned patterns, such as a zip code that does not correspond to the city listed,” shares Patel.
For mail marketers, this matters enormously. “USPS estimates that undeliverable-as-addressed mail costs the industry hundreds of millions annually. Running lists through AI-assisted standardization and National Change of Address validation before a campaign goes to press is fast becoming a baseline expectation, not a premium service,” adds Patel.
NLP adds particular value in business-to-business lists, where company names vary across data sources. “Merging a purchased list with your in-house CRM without deduplication can result in the same prospect receiving four identical mailers. That is not personalization. That is the opposite of it,” says Patel.
Piet DePauw, head of marketing, Enfocus, stresses the importance of using caution when enlisting AI for address cleansing. “Duplicate records matter as they create waste and inefficiencies when working on direct mail or VDP materials. And AI tools can help interpret that messy information by identifying the likely structure of an address, standardizing fields, spotting near-duplicates, and flagging records that need review. However, address cleansing is not something to leave entirely to a black-box model, because we can never see the full process behind its output. The correct approach is to use AI as part of a broader data-quality workflow. AI can help identify patterns and exceptions, but the cleaned data should still be checked against authoritative postal data, defined business rules, and clear matching thresholds.”
Aug2026, DPS Magazine
