The BlueConic Growth Engine now includes three embedded AI agents that help you select, configure, and launch Growth Plays faster.

Each agent operates where you need it most. The Advisor Agent surfaces at the LaunchPad and analyzes your existing BlueConic setup to recommend the Growth Plays most relevant to your current state. The Coaching Agent works inside the AI Canvas as an always-on configuration guide, explaining objects, identifying incomplete steps, and recommending what to do next based on what you've actually built. The Segment Agent goes further, automatically configuring a recommended set of audience segments from a single natural language prompt, with the flexibility to refine via conversation or traditional controls.

Benefits:

• Configure and launch Growth Plays faster, without waiting on expert guidance for foundational setup.
• Get contextual coaching tailored to your actual canvas state.
• Create well-structured audience segments through natural language, no technical expertise required
• Discover relevant Growth Plays based on what you've already built in BlueConic

To learn more, read our documentation.

Securely share customer data between BlueConic and Databricks with native Delta Sharing support.

The Look-alike Notebook brings native, first-party look-alike modeling directly into BlueConic — no data science team, no third-party platform required.

Finding more customers like your best customers is one of the highest-impact moves a marketer can make — but it has traditionally required data science expertise or reliance on ad platform audiences built on cookie data. The Look-alike Notebook changes that by bringing this capability natively into BlueConic, trained entirely on a brand's own first-party data.

A marketer selects a target segment (the audience to find more of), a source segment (the broader population), and the profile properties to include. The notebook trains the model, publishes it to BlueConic, and writes a look-alike score from 0 to 100 to each profile. That score behaves like any other profile property — it can be used in segments, dialogues, connections, and lifecycles immediately. Built-in charts help marketers understand why certain profiles match and choose the right audience size for their campaigns.

Because the model is powered by BlueConic's continuous models support, look-alike scores stay current automatically — recalculated whenever a customer visits the website or an underlying profile property changes. Unlike ad platform look-alikes, there are no third-party data dependencies and no cookie reliance. The model is built on data the brand actually owns.

Benefits

• Expands high-value audiences without third-party data: start with your best customers and find more profiles like them across your full population.
• Enables publishers to monetize first-party data for ad sales — precisely targeted look-alike audiences command higher CPMs than broad demographic segments.
• Improves campaign targeting for ad spend, email, and on-site personalization toward profiles most likely to convert.
• Scores update continuously as profile data changes, so look-alike audiences always reflect the latest customer signals.

This notebook can be installed via this url.

Gate rewards and protect your CRM by confirming phone numbers are real before consumers proceed.

Fake and disposable phone numbers are a persistent problem in high-incentive experiences. With Mobile OTP Verification, consumers must confirm their phone number with a six-digit code sent via SMS before they can continue. Only verified users get through.

The verification step sits directly inside the experience, so there is no redirect or friction outside the flow. The modal and SMS message are fully customizable to match your brand, and it works alongside the existing Email OTP feature if you use both.

Benefits:

• Captures verified, reachable phone numbers that hold up in CRM and SMS campaigns
• Blocks fake entries from accessing rewards, coupon codes, and sweepstakes
• Protects sender reputation by keeping unverified numbers out of downstream lists
• Keeps analytics clean by excluding sessions that do not complete verification

To learn more, read our documentation.

Turn a website URL into a working AI product feed in minutes — no integrations or CSV uploads required.

Vwam now includes Product Feed Import, a new way to generate a product feed using only a website URL. Instead of requiring a Shopify connection, PIM integration, or manually prepared catalog file, teams can simply enter a storefront URL and let Vwam automatically discover and structure product data.

The import process scans ecommerce pages, extracts product information using AI, and generates a usable product collection for your shopping agent — in under two minutes. For smaller catalogs, Vwam imports the full product set. For larger sites, it intelligently selects a representative sample based on featured products, best sellers, new arrivals, and category coverage.

This update helps teams activate AI shopping experiences faster by eliminating manual product feed setup. Combined with the existing content and style ingestion capabilities, you can move from website URL to a more complete AI experience in just minutes.

Benefits:

• Launch AI shopping experiences using only a website URL
• Eliminate manual product feed setup and CSV preparation
• Enable marketers and ecommerce teams to self-serve product ingestion
• Generate representative product collections automatically with AI-powered scraping

To learn more, read our documentation.

Bring household, account, and other group-level data into BlueConic—and activate it back to your warehouse.

BlueConic now supports Group import and export for both Snowflake Secure Data Sharing and Databricks connections. This means you can work with more than just individual profiles—bringing in and activating relationships like households, companies, or accounts directly from your data warehouse.

Groups allow you to model real-world relationships between profiles, with customizable types and properties. With this update, those structures can now flow seamlessly between BlueConic and your warehouse. This removes previous limitations and enables teams to fully represent and activate complex data models—without workarounds.

Benefits:
• Imports and exports group-level data for Snowflake and Databricks
• Supports complex relationships like households, companies, and accounts
• Enables fully customizable data models with group properties and types
• Removes blockers for enterprise adoption and advanced use cases

Learn more:
Snowflake Secure Data Sharing | Databricks Delta Sharing

Connect BlueConic to Amazon Redshift to import, unify, and activate data where it already lives.

BlueConic now supports a native Amazon Redshift connection, making it easier to work directly with data stored in AWS environments. Teams can import profile and group data using either tables or custom queries, and export enriched profiles back to Redshift with flexible options like incremental sync, full table replace, or append-only updates.

By working directly with your warehouse, you reduce data movement, simplify workflows, and accelerate time-to-activation. 

Benefits:
• Connects directly to Amazon Redshift for streamlined data access
• Supports flexible imports via tables or custom queries
• Enables multiple export strategies, including incremental sync and append
• Reduces data movement and speeds up activation workflows

To learn more, read our documentation.

Import Mailchimp engagement data faster and more efficiently with campaign-level reporting.

You can now enable campaign-based reporting when importing Mailchimp mail activity data in BlueConic. This new step appears automatically when mapping fields like emails received, opened, or clicked—giving you a more scalable way to bring engagement data into profiles.

Instead of relying on per-member activity lookups, BlueConic aggregates this data at the campaign level. This significantly improves import performance, especially for large audiences. You can also filter which campaigns to include, giving you more control over the data you bring in and how it supports downstream segmentation and activation.

Benefits:
• Speeds up data imports by using campaign-level aggregation
• Reduces load and improves performance for large datasets
• Enables filtering by specific Mailchimp campaigns
• Keeps engagement data ready for faster segmentation and activation

To learn more, read our documentation.

Track how AI-driven conversations influence engagement, conversions, and revenue — directly in Google Analytics 4.

You can now connect VWAM to Google Analytics 4 (GA4), giving you full visibility into how users interact with your AI assistant and how those interactions impact business outcomes. This integration sends structured conversation events—like engagement, responses, and redirects—directly into GA4, so you can analyze AI performance alongside your existing analytics data. 

By bringing conversational data into your analytics ecosystem, marketers can better understand how AI contributes to metrics like conversion rate, revenue, and average order value. Whether embedded on your site or running as a standalone experience, this integration helps unify customer journeys and unlock more accurate attribution across channels. 

This makes it easier to move from interaction data to actionable insights—turning AI engagement into measurable growth within your Customer Growth Engine.

Benefits:
• Measure AI-driven engagement, conversion, and revenue in GA4
• Track key events like first interaction, conversation depth, and product clicks
• Build funnels and attribution models that include AI interactions
• Customize tracking at the chat level for campaigns and experiments

Learn more in the GA4 integration setup guide.