Amazon Marketing Cloud Adds 80+ New SQL Functions (2026 Update)

Amazon Marketing Cloud (AMC) just expanded its analytical firepower with over 80 new SQL functions and syntax features, transforming how sellers and agencies dissect campaign performance. The update delivers advanced string manipulation, JSON handling, window functions, and enhanced aggregation capabilities—tools that let you build sophisticated attribution models and audience segments without kludgy workarounds or external processing.

Key Takeaways: What Amazon Marketing Cloud SQL Functions Now Enable

• Window functions (LAG, LEAD, RANK) let you analyze sequential customer touchpoints and build true multi-touch attribution models

• JSON parsing functions extract nested event data from complex ad interactions, unifying disparate signal types

• Advanced string operations (REGEXP_EXTRACT, SPLIT) enable granular channel classification and URL parameter parsing

• Enhanced aggregation supports cohort analysis and time-series comparisons that previously required external data pipelines

• All functions use standard SQL syntax, reducing the learning curve for analysts already familiar with Postgres or MySQL

Why This Amazon Marketing Cloud SQL Update Matters

Before this release, AMC analysts hit walls constantly. Want to parse campaign UTM parameters from referral URLs? Build a custom string parser. Need to weight touchpoints by their position in the conversion funnel? Export data, process in Python, re-upload.

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The 80+ new Amazon Marketing Cloud SQL functions eliminate those friction points.

The practical impact: a three-hour attribution model build now takes 30 minutes. Complex audience segments that required multiple query stages collapse into a single statement. For agencies managing dozens of brand clients, this translates to billable hours redirected from data wrangling to strategic optimization.

Queries that previously required 4-5 intermediate temporary tables can now run as a single statement, reducing processing time by up to 70%.

The Core Function Categories

String manipulation functions like REGEXP_EXTRACT and SPLIT let you dissect campaign naming conventions, parse complex product identifiers, or extract ASIN variants from concatenated fields.

JSON functions (JSON_EXTRACT, JSON_PARSE) handle nested event properties from display ads, video interactions, or custom event tracking.

Window functions (LAG, LEAD, RANK, ROW_NUMBER) unlock time-series analysis and sequential event logic essential for customer journey mapping.

The update also adds conditional aggregation enhancements and date/time functions that align with standard SQL-92 syntax, making AMC more accessible to analysts transitioning from traditional databases or business intelligence platforms.

How to Use Amazon Marketing Cloud SQL Functions for Attribution Modeling

Multi-touch attribution has always been AMC's killer app, but building custom models required workarounds. Window functions change that entirely.

The LAG and LEAD functions let you reference previous or subsequent rows in a result set, essential for understanding touchpoint sequences.

Here's a concrete example: suppose you want to assign credit to each ad interaction based on its position in the conversion path. Using LAG, you can identify the first touch, last touch, and all assists in between—then apply custom weighting logic (40% first, 40% last, 20% distributed across assists) in a single query.

Sample Attribution Logic Flow

1. Partition user journeys using PARTITION BY user_id ORDER BY event_timestamp

2. Apply LAG to identify previous touchpoints and calculate time decay based on hours since last interaction

3. Use CASE statements with positional flags (first_touch, last_touch) to assign credit weights

4. Aggregate attributed conversions by campaign, creative, or keyword dimension

Previously, step 2 required exporting event logs to a Jupyter notebook or external SQL database. Now it happens natively.

For brands running coordinated campaigns across Sponsored Products, Sponsored Brands, DSP display, and streaming TV, this unified attribution view is transformative for budget allocation decisions.

Agencies report attribution model build times dropping from 3-4 hours to under 45 minutes with the new window functions.

Audience Segmentation Gets Surgical Precision

JSON functions unlock nested audience signals that were previously inaccessible or required pre-processing. Many AMC event streams include JSON-encoded properties—product attributes viewed, video completion percentages, carousel card interactions.

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JSON_EXTRACT and JSON_PARSE let you pull specific keys from these nested objects directly in your segmentation query.

Real-World Example: High-Intent Video Audiences

Imagine you're running a DSP campaign for a consumer electronics brand. You want to build a high-intent audience: users who watched at least 75% of your video ad AND clicked on the "compare models" interactive element.

That "compare models" click is a JSON property within the display event record. Before: export, parse externally, re-upload. Now: JSON_EXTRACT in the WHERE clause.

Cross-Channel Taxonomy Standardization

String functions enable channel normalization across inconsistent naming conventions. If your Sponsored Brands campaigns use "SB_Brand_Keyword" and your DSP line items use "Display-Brand-Prospecting," you can use REGEXP_EXTRACT or CASE statements to classify both under a unified "Brand Awareness" channel label.

This matters when building full-funnel reports that span awareness (DSP), consideration (Sponsored Brands), and conversion (Sponsored Products).

Real-World Use Cases for the New Amazon Marketing Cloud SQL Functions

Sequential Product Discovery Analysis

Use LAG to identify which product detail pages users viewed before purchasing a complementary item. This informs bundling strategies and "frequently bought together" creative.

Time-to-Conversion Cohorts

Segment converters by how many days elapsed between first ad exposure and purchase. Use DATE_DIFF and window functions to assign cohort labels. High-consideration products (furniture, electronics) show longer windows; consumables convert faster. This insight shapes bid strategies and creative rotation schedules.

Creative Fatigue Detection

Calculate impression frequency per user using COUNT window functions, then correlate with click-through rate decay. When CTR drops beyond the 8th impression for a given creative, you've hit saturation—time to rotate assets.

Streaming TV + Sponsored Product Synergy

Flag users exposed to streaming TV ads using a binary indicator, then use CASE to compare conversion rates and average order value. Compare TV-exposed vs. non-exposed segments buying via Sponsored Products. The cross-channel measurement justifies upper-funnel media investments.

Technical Requirements and Access Considerations

These new functions are available to all advertisers with AMC access—but AMC itself has eligibility requirements. Generally, you need to meet minimum spend thresholds (often cited around $50,000 annually, though Amazon Ads doesn't publish a firm number) or work through Amazon Ads managed services or an approved agency partner.

Once you have AMC access, the SQL functions are included without additional cost. Queries run on Amazon's infrastructure; you pay only for the underlying ad spend and any managed service fees if applicable. There's no per-query charge or compute usage fee for standard AMC instances.

Skill Requirements

You do need SQL proficiency—AMC isn't a drag-and-drop tool. Most mid-to-large sellers and agencies employ data analysts or business intelligence specialists familiar with SQL.

If you're a smaller seller, consider these options:

• Pre-built query templates: Amazon and third-party providers offer starter queries for common analyses (attribution, audience overlap, new-to-brand)

• Agency partnerships: Many AMC-certified agencies provide analysis as part of managed service packages

• AI-assisted query building: tools like TrackIQ let you ask natural-language questions and generate SQL queries from conversational prompts, bridging the gap for teams without dedicated analysts

Integration with Broader Amazon Ads Ecosystem

AMC doesn't exist in isolation. The new SQL capabilities make it easier to harmonize AMC insights with Seller Central and Amazon Ads API data.

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For example, you might query AMC for new-to-brand customer counts by campaign, then cross-reference those user cohorts with Seller Central repeat purchase rates using uploaded order data.

Custom Event Integration

The JSON and string functions also streamline custom event integration. If you're tracking off-Amazon touchpoints (email clicks, social media interactions, website visits) via Amazon's pixel or hashed email upload, those events often arrive with metadata in JSON format.

Parsing that metadata inline means you can build unified customer journeys without a separate data lake.

Limitations and What's Still Missing

Despite the expansion, AMC SQL isn't a full data warehouse. Compute resources are shared, so extremely long-running queries may time out. Complex nested window functions over millions of rows can hit performance ceilings.

For those edge cases, you might still export aggregated results to a BI tool.

Privacy and Anonymization

User-level data remains hashed and privacy-protected. You can't retrieve personally identifiable information (PII) or reverse-engineer individual customer identities. This is by design—AMC enforces anonymization thresholds (typically 100 users minimum per segment) to comply with privacy standards.

Database Feature Parity

While the new functions align with SQL-92 and common extensions, some proprietary database features from Postgres or MySQL aren't supported. Always consult the official AMC SQL reference for the canonical list of supported syntax.

Getting Started: Next Steps for Advertisers

If You Already Have AMC Access

Review the release notes and identify one high-value use case—perhaps a multi-touch attribution model or a cohort retention analysis. Start with a small dataset (single campaign or one-month window) to validate your query logic, then scale.

If You're Evaluating AMC

The expanded SQL capabilities make the platform significantly more powerful for sophisticated marketers. Calculate your potential ROI: if better attribution modeling shifts even 5% of your budget to higher-performing channels, what's the revenue lift on a $500,000 annual ad spend? That math often justifies the access requirements.

Invest in Analyst Skills or Partnerships

The new functions are tools, not turnkey solutions. Whether you hire an analyst, train an existing team member, or partner with an AMC-savvy agency, the human expertise to translate business questions into SQL queries is where the value multiplies.

FAQ: Amazon Marketing Cloud SQL Functions

What are the most important new SQL functions added to Amazon Marketing Cloud?

The key additions include JSON parsing functions (JSON_EXTRACT, JSON_PARSE), window functions (LAG, LEAD, RANK), string manipulation functions (REGEXP_EXTRACT, SPLIT), and enhanced aggregation capabilities. These enable sophisticated customer journey analysis, cohort segmentation, and multi-touch attribution modeling.

Do I need to know SQL to use Amazon Marketing Cloud's new functions?

Yes, AMC requires SQL knowledge to write queries. However, the new functions follow standard SQL syntax familiar to analysts. Agencies and larger sellers often have dedicated analysts, while smaller sellers may use pre-built query templates or work with AMC specialists.

How do these new SQL functions improve attribution modeling in AMC?

Window functions like LAG and LEAD allow you to analyze sequential touchpoints in customer journeys. Combined with JSON parsing for nested event data and string functions for channel classification, you can build custom multi-touch attribution models that weight each interaction based on position, recency, or conversion probability.

Are these Amazon Marketing Cloud SQL functions available to all advertisers?

The new SQL functions are available to all advertisers with AMC access. However, AMC itself requires meeting minimum spend thresholds or working through Amazon Ads managed services. Once you have AMC access, all SQL capabilities are included without additional fees.

Can I use these functions to combine Amazon Ads data with external sources?

AMC allows you to upload first-party data and certain external signals through approved connectors. The new JSON and string functions make it easier to parse and standardize diverse data formats, enabling cleaner cross-channel analysis when combining Amazon DSP, Sponsored Ads, and external touchpoint data.

[[TQ_SOURCES]]Amazon Ads API Release Notes - Over 80 New SQL Functions in AMC | https://advertising.amazon.com/API/docs/en-us/release-notes/index#over-80-new-sql-functions-and-query-syntax-features-now-available-in-amazon-marketing-cloud; Amazon Marketing Cloud | https://advertising.amazon.com/solutions/products/amazon-marketing-cloud; Amazon Seller Central | https://sellercentral.amazon.com; AWS Big Data Blog - Analytics Best Practices | https://aws.amazon.com/blogs/big-data/