Blog

Your customers' motivations to buy often shift overnight when a new product launches, a social trend spikes, the weather changes, or a major event is announced. The challenge for many marketing teams is whether the system can match the moment to the right customers before it passes.

Lifecycle marketing systems are built to optimize against known data. They execute against predefined logic and historical behavior. That works when customers repeat past actions, but it doesn’t solve the challenge of creating relevance without the context of historical behavior. Will this customer want this new offer? 

A customer in Austin bought hiking boots in November and a canvas tote in January. In March, you launch a linen dress collection. Should she see it?

Your segment builder says no.

That logic is technically sound, but it breaks down when you need to assess a customer's likely interest in something new. 

Rules require explicit evidence. Predictive audiences require labeled training data for each product–customer pair. When the product is new, a campaign concept is untested,  or the customer has never entered the category, there is nothing reliable to score.

But customers can care about something long before your data reflects it. Closing that gap requires the ability to evaluate a match between a customer's broader behavioral patterns and an emerging opportunity.

That is what AI Personas and the Affinity Agent are designed to do. Together, they enable an Agentic Marketing workflow that matches customers to real-world moments as they emerge rather than waiting for history to catch up.

AI Personas: The behavioral abstraction layer

AI Personas represent a behavioral model of each customer, created from first-party data. Simon AI analyzes demographic attributes, purchase behavior, browsing depth, recency, frequency, and cross-category patterns stored in your data warehouse. Then, customers are grouped based on multi-dimensional behavioral similarity. 

Each group or cluster of behavior becomes an AI Persona, a structured behavioral profile that captures tendencies in a human-readable and inspectable way. In Simon AI, you can review narrative descriptions, representative customers, and the signals that contributed to a particular group. 

The system continuously evolves. Assignments refresh as behavior changes and new customers are evaluated based on early signals and similarity to existing groups. Every contact receives a persona ID that becomes a stable attribute for segmentation and activation, enabling persona logic to be immediately operational in your existing workflows. You can rename, reorganize, or hide personas to reflect your business, and convert any persona into an activatable segment directly.

Is this possible without agentic workflows? A capable data science team could build clusters inside Snowflake and score them against new products. The practical difference is maintenance and scale. Recalculating affinity for every new opportunity, refreshing persona assignments, and preserving scoring context across campaigns requires constant manual work.

That is the power of an agentic system that operates on live data (in Snowflake, for example), removing manual work that doesn't scale and allowing marketers to act quickly and independently of data teams.

The Affinity Agent: The real-time scoring engine

AI Personas clarify who the customer is behaviorally. The Affinity Agent evaluates what they are likely to care about next.

The Affinity Agent scores how well a specific moment or opportunity aligns with each persona's behavioral profile. It does not require the customer to have purchased, browsed, or clicked in the relevant category. It evaluates fit based on the patterns the AI Persona already encodes.

It answers a real-time question of “who should see this right now?” in five structured steps.

1. Inputs. The Affinity Agent takes in the opportunity being evaluated (a product launch, campaign theme, or cultural signal), along with candidate SKUs and live persona profiles from your first-party data. Scoring is recalculated for each opportunity, so every evaluation reflects its specific context.

2. Meaning matching. The system interprets what the opportunity represents. It compares semantic attributes rather than exact keywords. Those attributes are evaluated against the behavioral tendencies encoded in each AI Persona.

3. Structured reasoning. For each persona, the agent evaluates the likelihood of engagement and the messaging fit. Because AI Personas encode cross-category behavior, this evaluation works even when direct purchase history for the item is limited or nonexistent.

4. Affinity scoring. Each persona receives a probabilistic relevance score for the opportunity, ranging from 0 to 100. Customers inherit the score associated with their persona. This cascade allows opportunity-to-persona alignment to be computed once and applied across all associated customers. Marketers apply confidence thresholds to balance reach with expected intent.

5. Ranked outputs and persistent signals. The system produces engagement-ranked audiences and recommended products aligned to high-scoring personas. Customer-by-opportunity scores are stored, building a persistent affinity matrix that grows more precise as additional opportunities are evaluated. As new campaigns run and new moments emerge, prior scores provide compounding context for future targeting decisions.

This is different from traditional, supervised predictive audiences trained on historical conversions. Persona-based scoring evaluates fit across broad behavioral patterns, so new launches and customers with limited history can be included from the start. 

How it works: A retail walkthrough

Consider a marketplace brand with a broad design catalog and a seasonal surge of gift buyers. In January, engagement declines. The team wants to re-engage customers without defaulting to broad discounts.

A spring “cottagecore aesthetic” trend around natural textures, handmade details, and earthy palettes begins gaining traction.

Step 1: A cultural moment is detected through Simon AI Social Moments, signaling rising interest in cottage-inspired design themes.

Step 2: The Affinity Agent maps that theme to relevant SKUs across the catalog, identifying products aligned with attributes such as handmade styling and sustainable materials.

Step 3: The Affinity Agent evaluates each AI Persona against the opportunity. This process represents a structured “interview” of the personas that leads to an Affinity Score (such as Conscious Minimalist: 87, Seasonal Gifter: 74, Bold Maximalist: 31)

Step 4: Customers inherit their persona’s Affinity Score. A high-confidence threshold surfaces a focused audience of customers strongly aligned with the trend. A moderate threshold expands reach for testing while preserving relevance.

Step 5: Ranked audiences are paired with products that match the moment and feel natural for each persona segment. Simon AI then activates campaigns into engagement platforms such as Braze.

Within the high-confidence group are lapsed buyers who never purchased cottage-themed items before. They surface because their broader behavioral profile aligns with the opportunity.

Customer + moments in practice: SeatGeek

SeatGeek needed to match millions of fans to thousands of Tier 2 events that couldn't justify manual audience builds. Simon AI created fan AI Personas and Affinity Agents scored how well each new event aligned with those personas. 

Audiences were pushed directly into Iterable and refreshed continuously as new events launched. 

As a result, SeatGeek achieved a 70 percent increase in GTV per contact versus manually built audiences.

Read the full SeatGeek case study.

Match your customers to moments with Agentic Marketing

Most CDPs help you activate the segments you already know how to define. AI Personas and the Affinity Agent help you identify which audiences should exist in the first place, helping your team to understand  "who will care next."

See how AI Personas and the Affinity Agent work on your own customer data and walk through a real moment end-to-end. Get in touch here.

‍

When I bring up using social trends to trigger CRM campaigns, I hear one of two things: "Is that even possible?" or "We've tried. We can't move fast enough."

A creator posts about a product. A meme takes off that aligns with your brand. A trend spikes. "Very demure, very mindful" sends modest workwear sales soaring. The Oasis reunion sends 14 million fans scrambling for 1.4 million tickets. The “Albanian Riviera” goes viral and tourism doubles overnight. Canvas tote bags go from grocery sacks to status symbols and sell out in minutes. Your catalog has exactly what people want. The demand is there for the taking, often for only a brief moment. But acting on it is slow or impossible for most CRM marketing teams. 

Social is where customer attention lives. But it sits outside the way CRM marketing works. Systems are built to organize known data and execute against predefined logic. They're not designed to capture what customers are responding to right now, or why their behavior is changing.

That makes relevant messaging harder. Social activity, emerging fashion, cultural shifts, new events influence customer behavior immediately. Long before they show up in your CRM. By the time they do, they've been flattened into historical attributes or lagging indicators. And without a way to connect live context to your first-party data, you can't connect a trend to the customers who care about it.

So the pattern repeats. Your team spots a trend, files a data request to check relevance, waits on SQL or a ticket to build the audience, then another 48 hours to brief creative. Within a week, you're ready. But the moment is gone, or at best, you show up too late to seem relevant.

Trends can emerge in hours. Campaigns move at the pace of planning cycles. Observing a trend is not enough if you cannot quickly answer:

• Is this moment relevant to our customers?
• Do we have a product or message that fits?
• Who actually cares?
• What should they see?

Most teams either miss the moment entirely or blast everyone with a generic message, hoping for the best. Noise, not relevance. 

Introducing Social Moments

Simon AI™ Social Moments enables timely, personalized marketing by detecting viral social media trends and cultural moments that matter to your customers. It pinpoints relevant trends, breaking news, weather, and local events, then automatically surfaces the customers most likely to be interested. 

How moment-driven activation works

Detect. Simon AI Moment Agents monitor social media signals for spikes that indicate emerging relevance, then match them against your customer data and product catalog. They evaluate how quickly engagement is accelerating to catch trends as they rise. When a signal crosses a threshold you set, the system generates an AI Moment, a structured trigger specifying what's happening and why it matters.

Identify and Assess. Affinity Agents take AI Moments and match them to customers using AI Personas to score relevance, even for customers with limited purchase history. You get engagement-ranked audiences with relevance scores and predicted metrics, including CTR, conversion rate, and average order value.

‍

Activate. Automation Agents route high-relevance audiences directly to your engagement platform (Braze, Iterable, Attentive, etc.) with content and product recommendations ready to deploy. All this happens without traditional manual setup, segmentation tickets, and scheduling delays.

In practice, you see a ranked list of emerging trends matched against your product catalog, with audience size. Click into a trend to see customers scored by likelihood to engage, ready to sync directly to engagement platforms. No data ticket. No SQL. You're live the same day.

Social Moments playbook

Trend-aware abandoned cart

Turn a stalled cart into an opportunity. That forgotten item became the thing everyone wants.

Trigger: A product in abandoned carts starts trending on social.

Audience: Customers with the trending item in cart, prioritized by Affinity Score.

Activation: Enhance your existing abandoned cart flow with timely context.

Personalization: Trending SKU highlighted, plus complementary products based on cart contents.

KPIs: Recovered carts, AOV lift, time-to-conversion improvement.

Trending event digest

Same weekly send, now it feels handpicked. Each fan sees the events that match what's buzzing for them.

Trigger: Scheduled send combined with real-world trend signals.

Audience: Full send list, with events personalized by Affinity Agents.

Activation: Your existing recurring campaign featuring trending artists and surprise tour announcements matched to each customer.

Personalization: Each recipient sees the events most relevant to their profile.

KPIs: Engagement lift vs. baseline, CTR improvement, ticket conversion rate.

Viral hero SKU flash

A product is spiking. You have hours. Reach the customers who'll care while the trend is rising.

Trigger: A creator post or social mention drives sudden demand for a specific SKU.

Audience: Customers most likely to engage, scored by Affinity Agents.

Activation: Flash campaign across email, push, SMS and website while the trend is still rising.

Personalization: Variant and creative chosen based on relevance. VIP customers get early access.

KPIs: Time-to-activate, CTR to conversion, sell-through rate.

Trend-powered re-engagement

Bring back lapsed customers with a product that's trending right now.

Trigger: A pop culture moment or viral trend aligns with products in your catalog.

Audience: Customers who haven't purchased in 60+ days, especially seasonal or gift buyers, whose past behavior suggests affinity for the trending theme.

Activation: Re-engagement campaign featuring catalog items matched to the moment. No new creative required. The trend is the hook.

Personalization: Each recipient sees products matched to both the trend and their individual purchase history.

KPIs: Reactivation rate, revenue per re-engaged customer, engagement lift vs. generic winback.

Ship your products while the trend is spiking

Social Moments shrinks the time between spotting a trend and acting on it. Detection, audience scoring, data prep and activation happen in a single workflow, so your team launches faster, with relevance that enhances your brand. 

Behind Social Moments, AI agents work continuously on your live customer data via the Simon AI Composable CDP. Personalization is built on your first-party data. The guesswork and data requests that used to slow you down are handled autonomously. 

Ready to see how it works? We'd love to talk through Social Moments use cases for your brand.

The following recap pulls from our recent webinar, How Winning Teams Use AI to Turn BFCM Shoppers into Repeat Buyers, featuring Katharine Toll and Paige Rotar from Simon AI. Both work closely with direct-to-consumer brands, helping marketing teams use agentic AI to launch campaigns faster, uncover new revenue opportunities, and personalize at scale.

You'll acquire 30-40% of your annual customers this Black Friday / Cyber Monday. Most will never buy again.

Not because your product failed them or because they didn't like your brand. But by the time you figure out who they actually are and what they need next, the moment is gone. You spent millions on acquisition, but your tools can't process changing customer intent and real-world context fast enough to convert them into repeat buyers.

After 90 days, first-time buyers who haven't returned rarely become valuable customers. If you convert just a fraction more during this window, you’ll fundamentally change your growth trajectory. The stakes are high. The window is narrow. And most teams can't act fast enough to capture it.

Why most CRM teams can't keep up

By early December, you’re looking at tens of thousands of first-time Black Friday / Cyber Monday buyers grouped into a single “BFCM 2025” segment. Yet, they certainly don’t all belong in the same follow-up journey.

Some shoppers came in for the discount and are unlikely to respond to full-price offers this month. Others were buying gifts and probably won’t re-engage until they start shopping for themselves later in the season. Then there’s a sizable group who spent real time exploring new categories, browsing repeatedly, adding items to their carts, and reading reviews. These are all signals that they may be open to a second purchase soon.

Your job is to separate these patterns so each customer gets the follow-up that actually fits their intent.

The data you need is in your warehouse, but accessing it means submitting a ticket to engineering. By the time the fields get built, it's mid-December. The 90-day window is half gone. Even teams that already have the right data face the same slowdown because segment creation is still manual, rules-based work.

Traditional marketing operates on rules and schedules. But customer intent ebbs and flows, changing quickly. The difference between a team that can process all these signals while they're fresh and one that's always two weeks behind is the difference between profitable growth and expensive churn.

How leading brands are solving this with AI

Working with retail brands through their highest-stakes acquisition moments, I've seen the teams that are winning this race for repeat buyers do something different. They're not trying to move faster manually. They're using AI to process customer signals and real-world context at machine speed so they can respond to intent with value and relevance while it's still fresh.

Three AI plays are fundamentally changing how this work gets done. Together, they deliver personalization that matches how customers actually shop and give CRM marketing teams what they need to turn first-time BFCM buyers into repeat customers.

Each play helps you surface intent earlier, respond while it’s still fresh, and keep more of your newly acquired shoppers engaged during the 90-day window when repeat behavior forms. AI turns data into live attributes, activates campaigns when real-world signals align with intent, and evaluates every customer individually.

‍

Here's what each play does, and how they work together:

Play 1: AI-powered audiences — building and scaling actionable signals.

Most CRM teams work with the same static segments—"Deal-Seeker," "Lapsed," "High-Value"—not because those buckets represent the only signals that matter, but because those are what is wired into their systems. Product reviews, support transcripts, browsing patterns, and sentiment signals can be difficult to access and understand, require engineering tickets, and risk long turnaround times. 

AI-powered audiences transform the marketing workflow from static labels to executing with dynamic, predictive attributes called AI Fields. Instead of just knowing someone was a "Deal-Seeker," you can layer on social-trend affinity, post-peak purchase propensity, or channel preference shift. This represents a shift from manual, slow, rule-based segmentation to automatic, fast, and adaptive relevance for the real-world signals you need to identify who is most likely to buy again soon.

How AI-powered audiences work

AI-powered audiences run through four ongoing steps that update audiences as customer behavior changes:

• Analyze live data - Agentic AI brings together what customers browse, buy, and respond to, along with the signals they leave in reviews or support chats.
  
• Uncover hidden patterns - As behavior shifts, AI picks up emerging themes, changes in sentiment, and early signs that interest or loyalty is moving in a new direction.
  
• Assign actionable attributes - These signals become AI Fields that continuously update to reflect what a customer tends to prefer, how they engage, or which channels they lean toward.
  
• Create dynamic audiences - When similar patterns show up across customers, new audiences form automatically. They stay current because they use live data to update based on what people are doing now, not old segment rules.
  
  

For example, if someone moves from weekly to monthly purchasing, the system notices the shift immediately, AI Fields and audiences are updated, becoming immediately actionable rather than discovered weeks later.

Play 2: Contextual activation — triggering messages when real-world context and intent align

Your winter apparel campaign runs on a schedule. Every Monday at 10 am, everyone who browsed jackets gets the same message, regardless of whether it's 65 or 30 degrees in their city. Your segments are static. They can't keep up with changing conditions like weather shifts, trending styles, and local events. By the time your campaign launches, the moment may have already passed for a customer. 

Contextual activation is powered by AI agents that create dynamic, always-on optimization that incorporates real-world signals, like weather, social trends, and local events. Instead of calendar-driven campaigns, you combine audience intelligence with context to trigger messages when intent and conditions align. The result is hyper-relevant engagement that is based on the moment, not just the audience.

Simon AI Social Moments is a powerful example of contextual activation, using live social and cultural signals to trigger messages when demand is emerging, not after it’s already passed.

How contextual activation works

The system works in a steady loop:

1. Signal fusion - AI combines what customers are doing with what’s happening in the world around them, weather shifts, local events, seasonal changes, or trends that may influence why they’re shopping.
   
2. Intent detection -  As those signals overlap, the system notices when real-world conditions line up with a customer’s interests. A cold snap, an upcoming storm, or a regional event can turn past browsing into a meaningful moment.
   
3. AI Moments - When the timing is right, the system sends timely messages tied to that context. If temperatures drop in Denver, customers who have been exploring outerwear receive cold-weather recommendations. If conditions stay warm, the message waits until it’s actually relevant.

Campaigns are triggered by real-world signals to match the when and why a customer is likely to make a purchasing decision. Post-BFCM engagement is driven by propensity and relevance, so your CRM marketing team is sending fewer emails to people who aren't ready, and perfectly timed messages to those who are. Open rates climb. Conversion improves. You're building trust through relevance, not eroding it through spam. Contextual activation helps your team reach customers when their interest is strongest, improving repeat-purchase conversion without increasing send volume.

Play 3: Synthetic consumers — Individual-level prediction and decisioning at scale

Traditional segmentation infers intent from past behavior: Someone bought winter gear six months ago, so route them into the winter gear campaign. AI Fields surface smarter patterns—deal-seeker affinity, channel preference shifts, emerging interests—but you're still working backwards from data points, trying to guess what someone wants based on what they've done.

Synthetic Consumers create AI personas that think like your customers. Instead of inferring intent from historical actions, you build a dynamic model of each individual that simulates how they actually make decisions. Then you can ask it directly: "How interested is this person in this specific product right now?"The model reads the customer’s full pattern of behavior and produces an affinity score that reflects their level of interest. It’s not a spreadsheet of data points. It’s a way of asking a representation of the customer how likely they are to engage.

How synthetic consumers work

Synthetic consumers operate through three connected steps. Together, they create a representation of each customer you can query directly and activate in real time.

1. Dynamic customer models - The system builds a working profile, an AI persona,  that acts like the customer would across different situations. It interprets past activity, context, and trends, and turns them into actionable predictions you can use for highly relevant targeting. 
2. Intelligent interest scoring - You can ask this model about specific products or campaigns. Instead of simple yes/no logic, it returns an affinity score that reflects how likely the customer is to care about what you're offering.
   
3. Context-aware reactivation - Those scores are combined with real-world context. When the timing makes sense, AI agents determine when to re-engage the customer. When it doesn’t, the message waits.

Even customers in the same segment—deal-seekers, loyalists, gift buyers—have very different attributes and interests. Synthetic consumers let you personalize at the individual level while making thousands of decisions automatically. This lets you prioritize high-affinity customers during the 90-day window when repeat behavior forms, instead of relying on broad segments that treat all BFCM buyers the same.

Depending on your data, goals, and readiness, all three of these plays can work together. Here's what that looks like in practice. 

How AI turns a Black Friday browser into a repeat buyer

Taylor browses your site on November 29th. She's 34, lives in Denver, and has abandoned cart three times researching insulated jackets for backcountry skiing. She finally makes a first purchase during your 40% off sale: a mid-weight jacket, first-time customer.

Now, AI agents scan her complete behavioral profile. She viewed technical gear, not fashion outerwear. Her browsing history shows skiing and hiking categories. Cart abandons suggest research, not price-hunting. Purchase timing coincided with ski resort announcements. She gets dynamically categorized into "High-Value First-Time Buyers" and "Cold Snap Layering Seekers", live attributes that appeared the day the pattern emerged.

From December 1, AI monitors external signals for contextual activation. Regional temperatures forecasted to drop December 4-6. Local ski resorts are announcing opening day. Her browsing showed interest in base layers she didn't purchase. The system holds the message, waiting for context to align with intent.

On December 4, temperatures hit 28°F in Denver. Ski season opens. The system queries Taylor's synthetic consumer model: "What's her affinity for technical base layers right now?" The model evaluates her complete profile and responds: "I'm gearing up for another ski season in the Rockies. I need reliable gear that can handle backcountry conditions. I'm looking for technical outerwear that performs in extreme cold but won't overheat me on the uphill."

Taylor’s affinity score reaches 89/100 for technical base layers.

Taylor receives a "Comfort starts at the base" email featuring technical base layers, triggered by demonstrated interest, weather alignment, and individual-level affinity scoring. The product recommendation matches what she's planning this weekend. No discount needed because relevance is the value.

At the same time, AI is busy processing similar scenarios for tens of thousands of other BFCM customers, routing gift buyers to March campaigns, suppressing discount-seekers from full-price offers, and matching product enthusiasts to relevant categories.

One journey template becomes thousands of individually timed moments. This level of precision requires clean data foundations and clearly defined campaign goals. But once configured, these decisions happen automatically.

"Getting to that why is important, but being able to systematically do that across your entire customer database is the real challenge." ^{Katharine Toll, Head of Sales, Simon AI}

Explore how AI can improve your BFCM performance

Ready to explore what's possible for your brand? Book a 45-minute AI Use Case Workshop with our team. We'll walk through high-impact AI use cases tailored to your goals, identify fast-start ways to scale personalization, and show you how AI connects customer behavior with real-world context to turn Black Friday / Cyber Monday buyers into repeat customers. You’ll walk away with three use cases to help you activate real-world signals and convert more of your BFCM shoppers into repeat buyers. Request your use case workshop here.

Every marketer feels the same pressure as Black Friday and Cyber Monday approach: too much data, too many touchpoints, and too little time. What used to be a four-day promotional burst has become a high-stakes, multi-week competition for attention, engagement, and conversion.

Historically, brands coped by throwing more staff, more promotions, and more budget at the problem. But that approach no longer scales. The limits of manual planning, static segments, and fixed calendars become painfully clear during BFCM, when behavior shifts by the hour and last year’s “best practices” simply can’t keep up.

More brands are now turning to Agentic AI to change this equation. These AI systems use multiple autonomous “agents” that collaborate to analyze data, generate content, and make decisions in real time. Marketers still define the strategy, guardrails, and brand standards; Agentic AI simply executes the operational complexity. The result is segmentation and journeys that optimize as customers move through the funnel… not after the fact. And while these capabilities shine during the BFCM rush, their real value emerges once the season ends, when the intelligence gathered during “peak” becomes the foundation for long-term loyalty.

Here are five ways Agentic AI is reshaping the BFCM playbook and extending its impact well into the new year.

1. Smarter segmentation and micro-segmentation

The Challenge

Traditional segmentation schemas can’t keep pace with BFCM activity. Increased engagement carries with it more signals for each customer. Additionally, purchase intent and customer behavior shifts dramatically during the BFCM window. A “VIP” customer in September might act like a deal seeker in November. A low-frequency browser might suddenly exhibit high-intent behaviors that would normally take months to emerge. If your campaigns are wired to historic behavior, you’ll miss the real intent in front of you. 

The result is a familiar holiday problem: irrelevant messages, poorly timed incentives, and wasted impressions during the most expensive week of the year.

How Agentic AI Helps

Agentic AI transforms segmentation from a static categorization exercise into an adaptive, real-time intelligence system. Instead of marketers defining each segment manually, data and insight agents continuously propose, test, and refine dozens of micro-segments based on what is actually working. Specifically, these agents:

• Ingest behavioral, transactional, and contextual signals (browse paths, cart actions, device, time of day) and update segments on the fly.
  
  
• Identify moment-in-time intent clusters, such as “Gifts for Dad researchers,” “high-margin category explorers,” “early morning mobile shoppers,” or “repeat browsers showing second-purchase potential.”
  
  
• Continually re-evaluate and refine micro-segments based on behavioral, transactional, and contextual signals: what people browse, how often they return, whether they show price sensitivity, what channels they use, etc.
  
  
• Connect directly to your engagement platforms, so each micro-segment is routed to tailored messaging flows.

2. Offer optimization without eroding margin

The Challenge

BFCM has always created tension between revenue and margin. The instinct to “win the weekend” pushes teams toward deeper and broader discounts, at the expense of eroded brand equity and compressed margins exactly when paid media is at its most expensive. Without precise insight into who needs a discount and who doesn’t, brands err on the side of over-discounting. The result: strong top-line performance paired with weakened profitability and increased discount dependency among customers.

How Agentic AI Helps

Agentic AI treats offer decisioning as a continuous optimization engine. Rather than relying on a pre-defined promotion calendar, the system dynamically adjusts incentives at the micro-segment level as new data points emerge. To achieve this, AI agents:

• Continuously test discount levels, offer framing, and incentive types (percent off, dollar off, gift with purchase, early access) across myriad micro-segments.
  
  
• Adjust offers dynamically based on predicted purchase probability, historical LTV, and price sensitivity.
  
  
• Auto-suppress discounts for customers who are likely to buy at full price or with lighter incentives such as early access.
  
  
• Surface new offer concepts that would be difficult to test manually at scale, then recommend the winners for roll-out, with supporting data.

In practical terms, you can set guardrails around margin and brand standards, then let AI explore the space within those constraints. This leads to smarter use of margin, not blanket erosion.

3. Product recommendations powered by real-time inventory

The Challenge

Every holiday season puts extraordinary strain on inventory. Products sell out quickly, supply chain snafus cause late arrivals, and inaccurate inventory data lead to a poor customer experience. Traditional recommendation engines typically rely on static models and stale inventory snapshots, resulting in promotions for items that are out of stock or hard to ship before key holiday deadlines.

This disconnect between personalization and inventory creates unnecessary friction. Customers click on items they can’t buy, and brands miss opportunities to move high-margin or well-stocked products.

How Agentic AI Helps

Tapping into AI’s ability to reconcile multiple datasets and structures on the fly, these systems merge personalized recommendations with real-time inventory levels. In terms of activity, these agents:

• Integrate live inventory feeds with recommendation models, incorporating stock levels, sell-through velocity, and margin into product rankings.
  
  
• Prioritize in-stock and high-margin products, while automatically suppressing or deprioritizing low-stock SKUs that are at risk of going out of stock.
  
  
• Suggest intelligent substitutes and complements when popular products sell out, based on similarity and historical attach patterns.
  
  
• Factor fulfillment windows into recommendations, highlighting items that can still arrive by key dates such as Christmas or New Year’s.

This turns recommendations from a static “You might also like” widget into a real-time merchandising engine, tuned to both customer and business opportunities, all while avoiding the poor customer experiences that undermine long-term loyalty.

4. Customized creative at scale

The Challenge

The creative output required during BFCM has outpaced what even the best teams can produce manually. Daily deal waves, multiple micro-segments, and omnichannel execution demand a constant stream of variations: subject lines, product callouts, onsite messaging, SMS copy, and paid media creative. Yet teams are still expected to maintain brand consistency while increasing personalization. It’s almost an impossible combination.

The result is predictable: generic campaigns, reused assets, and missed opportunities to tailor messages to specific behaviors.

How Agentic AI Helps

Agentic AI amplifies the creative team’s capabilities by generating high-quality, on-brand variations at scale. Creative teams establish the voice, guidelines, and narrative direction; AI systems handle the multiple variation complexity. To achieve this, they:

• Generate tailored subject lines, headlines, CTAs, and product copy for each segment or intent cluster.
  
  
• Adjust tone, urgency, and framing based on region, engagement level, and lifestyle context (e.g. single, couple, family).
  
  
• Work from pre-approved brand guidelines and guardrails, so messages stay on voice and within legal and regulatory boundaries.
  
  
• Run continuous multivariate creative tests, then roll out winners automatically while retiring underperformers.
  
  
• Support channel-specific creative, such as shorter, more urgent SMS copy, richer email stories, and tighter paid media variants.

By marrying expressive AI capabilities with automation and journey agents, these systems help marketers reap the benefits of truly customized content, without sacrificing brand guideline consistency.

5. Dynamic journey and channel optimization

The Challenge

Holiday campaign performance changes by the hour. Email may spike in the morning and fall flat by mid-afternoon. SMS may surge right before shipping deadlines. Paid media ROI may swing dramatically based on time of day, audience saturation, or creative rotation. Yet most marketing calendars are locked weeks in advance and journeys are built as locked decision trees, not adaptive systems. Manual optimization isn’t just inefficient, it’s near impossible at BFCM scale.

How Agentic AI Helps

Agentic AI turns the entire journey into a responsive system. Optimization agents watch for shifts in engagement, identify friction points, detect fatigue, and work with automation agents to adjust communications accordingly:

• Monitor engagement across email, SMS, app, web, and paid media in real time, looking for shifts in performance.
  
  
• Adjust send timing, frequency, and channel mix automatically, based on live response curves and diminishing returns.
  
  
• Move budget between channels where you see better incremental return, especially in costlier paid media and retargeting platforms.
  
  
• Detect funnel friction, such as spikes in cart abandonment or checkout exits, and trigger immediate re-engagement using the channel most likely to work.
  
  
• Coordinate messaging across platforms for each micro-segment to reduce overlap and avoid bombarding the same customer with redundant promotions.

You still set the strategy and guardrails, but you are no longer manually dialing every lever during the highest-pressure week of the year. Instead of making decisions in hindsight, the system acts in real time, exactly when the opportunity exists.

Conclusion: BFCM as the launchpad for lifelong loyalty

Agentic AI transforms BFCM from a high-pressure holiday sprint into a continuous intelligence loop. The same systems that optimize segmentation, offers, creative, and journeys during peak season become the backbone of long-term customer value immediately afterward. Brands can carry forward micro-segments, elasticity models, inventory signals, creative learnings, and journey insights into the new year.

The net effect is that winning BFCM no longer means a mad rush of offers and batch-and-blast promotions. It means creating systems that deliver a customized holiday experience for each user. And done right, these systems keep working long after the surge ends, turning seasonal shoppers into loyal, long-term customers who stay engaged well beyond December.

‍

One of the most important collaborations in customer experience is the partnership between data and marketing teams. After all, there’s no “data-driven marketing” without data. In my own experience as eCommerce and marketing leader, my data partners have been some of my most important collaborators, helping my team and I to both understand our customers better and mobilize those insights into true 1:1 experiences.

And yet, I’ve also witnessed marketing / data relationships that were strained and ineffective. Marketing teams that felt slowed down by data team timelines, and data teams that felt reduced to “report pullers,” saddled with tactical asks instead of being viewed as strategic partners. 

The stakes of this misalignment are high: missed opportunities, slower decisions, wasted spend. In a world where customer expectations evolve daily and competitive advantage is often won by speed AND precision, no company can afford friction between these two critical functions.

The good news is that it doesn’t need to be this way. There are a handful of blockers that get in the way of an effective partnership, and all are addressable. This article explores the "why" and the "what" at the heart of this: Why these cross-functional alignments happen and What marketers can do to address those factors. Along the way, we’ll look at real-world examples, practical frameworks, and the role that modern martech tools (from CDPs to AI) can play in making the partnership hum.

Why marketing and data teams are often misaligned  

To fix the disconnect between data and marketing, we first need to understand the root causes. In my experience there are a handful of common ones.  

1. Differing mandates  

On the surface, marketing and data should be perfectly aligned: both exist to leverage insights that improve the business. But in practice, their priorities diverge. Marketing focuses on speed and iteration, using rapid testing cycles to capitalize on evolving markets and gauging success in base growth and revenue. Data teams focus on precision and structure, with success measured in accuracy and stability.

This creates the classic “fast vs. accurate” tension. Marketing pushes to launch in days. data prefers more time for model validation and testing protocols. Both are right in their context, but without alignment, each can feel the other is an obstacle.

2. Timeline expectations

Modern marketers live in days and weeks, driven by aggressive campaign calendars and test-and-learn cycles. data teams often work in weeks and months, driven by structural design, thorough validation, and robust governance. Ironically, those same safeguards are what ensure the data reliability that marketing depends on.

When expectations collide, frustration builds. Marketers complain data is too slow. Analysts see marketing as reckless. Without a shared rhythm, collaboration stalls.

3. Language barriers

Every business discipline has its own lexicon. Marketing talks brand, funnels, and storytelling. data teams talk models, regressions, and confidence intervals. Each side’s jargon can feel opaque to the other, creating artificial barriers when they try to compare notes.

The problem isn’t just language; it’s cultural. Marketing often prioritizes creativity and narrative. data prioritizes precision and statistical truth. Neither is wrong, but the disconnect can foster a sense that the two teams are talking past each other.

4. The “request vs partnership” trap

One of the biggest sources of tension is how requests are framed: “Can you pull me a list?” or “Can you generate a report?” Unsurprisingly, analysts feel undervalued when reduced to servicing tickets instead of helping shape strategy.

The irony? Your data team can be a powerful strategic partner when you include them in the planning. Most data professionals want to solve complex problems, not just extract CSVs. And their deep knowledge of the data can help unlock insights you didn’t think to ask for.

Why all of this matters  

The cost of an ineffective marketing / data partnership goes beyond tense relationships and messy email threads; it results in lost opportunities and real business impact. Even mild misalignment limits what the partnership could achieve.

Poor targeting can waste ad spend, leaving growth on the table. Campaigns fall behind competitors because targeting analyses take too long. New datasets get delayed because requirements are either undefined or over-defined to the point of paralysis.

Ultimately, trust erodes. And with it, go both speed and collaboration

How to build a high-performing partnership  

The good news is that this relationship doesn’t have to be a tug-of-war. With the right practices, the marketing / data partnership can become a powerful strategic engine.

So let’s look at some of these practices:

Shift the relationship from service desk to strategic partner

The first, and perhaps most important, mindset adjustment is for marketers to avoid treating the data team as a service desk. Instead:

Learn data as a second language

In an age of data-driven decision-making, modern marketers need to be as comfortable with data concepts as they are with brand concepts. The more you understand how data works and how to talk about it, the easier it will be to frame your questions and requests in a way that the data team can respond to. And once that happens, barriers give way to partnership.

For example, a marketer may be interested in testing different creatives, but they’ll benefit from understanding test cell sizing and the parameters of statistical significance to ensure that the data team can analyze the results with the reliability and timeliness they’re looking for.  

Or a marketer may be looking to quantify the stages of the customer journey, from email open through purchase. But to achieve that, their analyst partner may need to combine (or “join” in data-speak) a few different “event tables.” Just understanding that extra layer of complexity allows you to talk through the challenges and get to a solution. It may even result in modifications that will make similar analysis easier in the future. 

In its simplest form, marketers think in “stories” (profiles, audiences, journeys) and Analysts think in “structures” (tables, rows, events). But data is foundational to both. And an expanded vocabulary builds on that foundation.

Define clear roles and expectations

In arenas from sports to the military, high-functioning teams are built on an understanding of roles (who does what) and expectations (when and where they do it).

Business is no different, which underscores the importance of marketing and data teams being aligned on each other’s roles and expectations.

Who drives test definition? Segmentation rules? Data governance? Without clarity on these, both sides will duplicate effort, or worse, expect the other team to do it and drop the ball.

Equally important is aligning on SLAs, or service level agreements, which is a fancy way of saying “define timelines for deliverables.” In our world of multi-tasking and competing priorities, “as soon as possible” is simply not sufficient for either a request or a promise. Rather, effective teams establish expectations around when requests will be reviewed and engage in a respectful negotiation around practical timelines for both sides.

Align on the metrics that matter

At the end of the day (or quarter), business outcomes are what matter, and both teams should rally around the goals and metrics that drive those outcomes. That means structuring analytics around key metrics like customer acquisition cost (CAC), lifetime value (LTV), average order value (AOV), and average revenue per user (ARPU). Other metrics like impressions, click-through rates, and conversion are important diagnostic tools, but both teams should agree on where they fit in the hierarchy of campaign analysis.

Equally important, both teams should agree on the one primary “success metric” for a campaign or program. In some cases, it may be conversion, but in others it may be AOV. Crisp analysis and decision-making rely on a clear “north star” metric that will determine success for each campaign, test, or pilot program.

And lastly, both teams should agree on a “source of truth” for analysis and results. Even if the marketing team runs its own analysis, the data should come from a source that has been blessed by its data partners. Nothing erodes trust faster throughout the organization than two analyses or dashboards that show different numbers.

Build rituals and celebrate wins

Effective collaborations don’t happen overnight. They require structure and repetition to build muscle memory and reinforce behaviors. Here are a few elements that I’ve found to be effective tools in the marketing / data partnership:

• Weekly syncs between marketing and data: These ensure that key projects are moving forward, provide a forum for surfacing and resolving blockers, and enable reprioritization when required.
• Joint retros for big campaigns: Tying inputs to outcomes is one of the most effective ways to motivate people. Sharing the revenue or customer engagement that came from the data team’s segmentation schema or pro forma analysis both motivates them and crystallizes the marketing partnership. 
• Co-presenting results to leadership: When marketing and data teams are aligned in their presentations to the executive team, it sends the signal that they are in it together and have each other’s backs. As an added bonus, it gives the executive team greater confidence in the numbers being shared. 
• Celebrate wins together: Strong collaborations are powered by collective wins. By celebrating successful outcomes together it strengthens the bonds between both the teams and the individuals among them.

Where modern martech can help

Technology can’t fix a poor relationship, but it can provide the kind of sound foundation that good partnerships are built upon. And there are three key capabilities to consider when powering the marketing / data partnership.

CDPs as the bridge between marketing and data

A CDP (customer data platform) unifies customer data into a single source of truth, transforming raw warehouse data into marketing-ready datasets and unified customer profiles. In doing this, it centralizes, cleans, and standardizes multiple data sources, resulting in a database that both teams can trust.

This “democratizes the data” by making it more accessible to the marketing team, without sacrificing the rigors of data governance the Data team requires. Marketers can define and size segments without pulling analysts into every request. Analysts can empower marketers by setting up datasets applicable for multiple use cases, rather than fielding tickets for every segmentation request.

For example, marketers want to launch a win-back campaign. Without a CDP, back-and-forth is required to define “churned.” With a CDP, “churned” is a standardized audience segment, and is adjustable with additional filters and variables set up by the data lead and controlled by the marketer.

This shift turns conversations from “Can you pull me a list?” to “How should we define and govern this segment together?”

Engagement platforms leveraging CDP inputs

Once a CDP is established as the source of truth for customer data, marketers can set up event triggers that automatically send audience segments and supporting data to engagement platforms that drive marketing-defined campaigns and customer communications. These platforms can drive activation on paid media (Meta, Google, Tradedesk), CRM channels such as email and SMS, and even direct mail and Customer Care outreach. 

If the engagement platform is rich enough with channel integrations, it can even enable multi-channel communications and customer journeys. And with a CDP (like Simon AI) supplying rich data feeds, the emails, SMS messages and other touchpoints in those journeys can be highly customized. 

For example, a customer browsing a product without purchasing triggers a CDP (Simon AI) to launch a follow-up email via an engagement platform, like Braze, that is personalized with customer data, product info, and customer offer (based on predicted customer value). After a day without a reply, Braze automatically triggers a follow-up SMS (as part of the journey set up by marketing) with options for the customer to either purchase at a special price (based on predicted customer value from the CDP) or text with a customer care rep. And if the customer chooses to text with a customer care rep, Braze automatically triggers a follow-up text from the customer care team’s platform, establishing direct rep-to-customer communication.

And all of that happened without any custom data feeds or targeting criteria from the data team. The result? Marketers move faster, Data teams focus on higher-value analyses, and customers receive a true 1:1 experience.

Agentic AI frees up data teams further

The newest evolution is Agentic AI: AI agents embedded within CDPs and engagement platforms. These platforms exponentially accelerate the customization and scale of 1:1 customer journeys.

Agentic AI can auto-build high-intent segments based on campaign goals, prep data for personalization, and draft entire cross-channel campaigns (timing, copy, visuals) for marketer review and approval.

This shift empowers both sides. The data team gains time to focus on higher-order work like modeling LTV frameworks or refining marketing spend attribution. Meanwhile, marketers are able to envision campaigns around myriad customer moments, delivering micro-journeys at a previously impossible scale.

A practical framework for marketers

To make all this tangible, here’s a quick checklist marketers can use to build a stronger partnership with their data teams.

‍
The power of partnership

Marketing and data teams are better together, but only when it’s a true partnership. And marketers can take the first steps to make that happen. That means investing in the relationship, not just the requests. Share context. Learn about data. Build trust.

Because the best marketing stories aren’t just crafted with creative copy or eye-catching visuals. They’re written in partnership with the data team, where friction gives way to flow, and collaboration drives growth.

Today, Simon AI has announced the availability of the Simon AI™ Agentic Marketing Platform as a Connected Application on the Snowflake Marketplace.

This launch extends our commitment to helping enterprises bring agentic, AI-driven marketing directly to the Snowflake AI Data Cloud, empowering teams to reason over live data, detect key signals, and act in real time, without ever moving data or relying on complex integrations.

“Marketers have been promised AI for years, but the breakthroughs only come when data and intelligence operate as one. With this Connected Application, we’re giving enterprises a way to realize that vision—AI that acts on live data, securely and intelligently, right where it lives.” ^{Jason Davis, CEO and Co-founder, Simon AI}

What’s new

Simon AI is a connected app that leverages Snowflake Cortex AI to deliver marketing intelligence in place, within your governed Snowflake environment.

Composable Simon AI Agents reason over live customer data to surface insights, build audiences, and trigger personalized actions in real time. These agents are modular and flexible, allowing marketers to compose intelligent workflows that adapt as data changes.

This Connected App represents a major step toward autonomous, data-driven marketing, without breaking governance or moving data across systems.

Why it matters

Traditional marketing platforms extract and replicate data, slowing execution and introducing risk. Simon AI brings computation to the data, ensuring both speed and compliance.

The result is a fully governed, AI-enhanced activation layer, where marketing, data, and AI collaborate directly inside the Snowflake AI Data Cloud.

It’s a shift from manual segmentation to agentic systems that learn, adapt, and act, enabling marketers to operate at the same velocity as their data.

How it works

Composable Simon AI Agents form the building blocks of modern marketing orchestration:

• Insights Agents discover hidden patterns and opportunities from behavior, sentiment, or contextual data.
  
  
• Data Agents translate complex Snowflake tables into actionable marketing attributes.
  
  
• Automation Agents continuously trigger personalized messages and journeys across channels.
  

Together, these agents create a dynamic, composable framework that enables continuous optimization and 1:1 personalization.

What customers can expect

• Build and activate high-impact audiences up to 10x faster.
  
• Achieve up to 6x higher conversion rates 
• Operate on live data with full security, governance, and lineage intact.
  
• Empower marketing teams to act independently, while data teams retain oversight and control.
  

This is what agentic marketing looks like: faster, smarter, and governed by design.

See it in action

Explore the Simon AI Connected Application on the Snowflake Marketplace.

Learn more from our CTO’s guest post on the Snowflake Medium blog:
Why Simon AI Chose Snowflake Cortex AI to Deliver 1:1 Personalization.

And revisit our earlier announcement:
Simon AI Launches Composable AI Agents for Marketers, Built on Snowflake Cortex AI.

Our vision

Marketing is evolving toward agentic marketing, where AI systems understand context, reason over live data, and act autonomously.

With Simon AI, a connected application on Snowflake, we’re helping organizations operationalize this future: combining live data, governance, and composable AI into a single, secure framework for intelligent customer engagement.

“Composable AI is the bridge between today’s marketing automation and tomorrow’s adaptive intelligence. Our vision is to empower every brand to build its own network of AI agents that collaborate across the data cloud to deliver experiences that feel truly human.” ^{Jeff Walton, VP of Product, Simon AI}

This launch represents more than just a Marketplace listing—it’s a milestone in the journey toward truly adaptive, data-driven marketing at scale.

A deep partnership with Snowflake

As an Elite Tier Snowflake Partner—the highest level of partnership within Snowflake’s ecosystem—Simon AI continues to push the boundaries of what’s possible with data and AI collaboration.

Our work with Snowflake Cortex AI has been recognized in the 2026 Modern Marketing Data Stack report, where Simon AI is featured for redefining how brands connect insights to action.

We were among the first companies to offer a Connected Application on Snowflake, and received Snowflake’s Powered by Snowflake Partner of the Year Award for our leadership in this new architectural model.

To see how this partnership delivers measurable impact, explore Snowflake’s official case study:

‍Simon AI: Composable Agents for Agentic Marketing on Live Data in Snowflake.

“Our collaboration with Snowflake has always been about more than technology, it’s about helping joint customers move faster while staying fully governed. This Connected Application shows what’s possible when two platforms align around a shared vision of bringing AI directly to enterprise data.” ^{Alex Greer, Strategic Partnerships Leader, Simon AI}

Together with Snowflake, we’re empowering enterprises to turn live data into live experiences, securely, intelligently, and at scale.

Learn how at simon.ai or contact our team to explore how Agentic Marketing can accelerate your data strategy.

‍

Marketing leaders at retail brands are under pressure to prove AI can drive revenue now. At Shoptalk Fall 2025, we heard the same sentiment over and over—urgency to show results, hope that AI can deliver them, and confusion about how to get there.

Retail brand marketing teams are expected to elevate the customer experience and drive both conversion and LTV with the same or fewer resources. AI has moved fast from experiment to mandate.  Leaders expect AI to finally solve the personalization challenges that have slowed their marketing teams for years.

But most teams aren’t ready. Teams are lean, data is often locked away or incomplete, and many depend on data teams (and sometimes they don’t have them). The promise of personalization still feels more like an aspiration than a reality to many marketers. 

We’ll sum up what we heard from marketing leaders in dozens of meetings last week in Chicago: 

1. Data is still the biggest barrier

Only about one out of three marketers we spoke with had their data in a good place, running both a cloud data warehouse and a customer data platform. Too many are still treating their CRM as the database of record. That’s a barrier to AI success. 

Without a modern data foundation, AI can’t deliver. AI works as well as the data it operates on. Personalization at scale requires data that’s unified, accessible, and ready to act on. With that in place, AI agents can handle complex work and deliver value. 

• Simon AI helps marketers explore all their data - first, second, and third-party data, structured and unstructured. It turns messy data into marketer-ready fields in minutes, not weeks. Learn more about the AI-first Composable CDP and the new operating model enabled by Composable Simon AI Agents. 

2. Context is the signal marketers don’t realize they’re missing

Marketers spend most of their time wrestling with first-party data, with some dabbling in second-party data. But bring up third-party signals like weather or social trends, and the energy changes. Everyone agreed context is powerful, and most immediately asked, “How would we do that?”

Traditional data sources and CDPs tell you what happened (clicks, opens, drop-offs) to what customers, but they can’t tell you why. Without context, personalization is generic.

Simon AI activates real-world signals like weather, inventory, and social spikes to make campaigns timely and relevant. Simon AI™ Social Moments applies this idea to social context, detecting rising social demand and preparing audiences while interest is still building. Learn more about contextual personalization. 

3. Execution is the everyday struggle that forces a painful trade-off

This was the most consistent frustration we heard. Even with data in place, teams struggle to move from insight to action. Campaigns take too long to build, and there’s too much dependency on technical teams. By the time a campaign goes live, the customer moment has passed.

Most marketing platforms force a painful trade-off. When you focus on volume, performance drops. As you personalize to convert better, volume drops. All your effort is on a few big bets. That’s the reality holding brands back.

The Simon AI Personalization Studio ends that trade-off — using Agentic AI on an AI-first Composable CDP to automate attributes, audiences, and workflows on live data while marketers stay in control.

4. The CDP market is noisy — and confusing

On the expo floor and in conversations with marketing leaders, it's clear that the CDP category has never been more crowded. Every vendor promises personalization. But most are layering on point-solution AI — faster content, better predictions, next-best actions.  

That isn’t solving the real problem. Real personalization depends on two giant leaps forward: 

• Marketer access to live, usable, complete data that is ready for a campaign
• The ability to execute hundreds or thousands of contextually relevant campaigns at speed and scale

Instead, most CDPs still leave marketers stuck with limited, predefined data, an inability to generate insights quickly, and a heavy dependence on data teams. This results in slow execution and a default to generic campaigns for segments. Campaigns take six to eight weeks to launch, long after the customer moment has passed.

• Simon AI cuts through data complexity with an AI-first Composable CDP and Data Agents that transform messy inputs into usable fields and live audiences. Learn more about Simon AI Data Agents that are live with customers now. 
  

‍

Turn messy data into marketer-ready attributes in hours, not weeks.

5. The cost of inaction is rising — differentiation depends on AI now

Marketers told us competition is fiercer than ever, and the gap between their investments and ROI is only growing. Time is essential — fast movers that use AI to solve customer experience problems today will have the edge in a future defined by rapid innovation and learning cycles.

For the moment, teams are overwhelmed with data but too slow to activate it, leaving campaigns generic and mistimed. Consumers, meanwhile, expect relevance instantly — whether in email, SMS, ads, or future agent-driven experiences.

Brands that move first will define the new standard for performance. Differentiation depends on using AI to close the gap between signals and execution.

• With Simon AI, brands accelerate differentiation by launching campaigns faster, acting on more signals, and scaling personalization without trade-offs. Learn more about the new marketing workflow that starts with a goal, and agents handle the hard work to wrestle with data and execution.
  

Early adopters of Simon AI are seeing measurable impact:

• Launch contextually relevant campaigns up to 10x faster
• Achieve higher conversion rates through adaptive personalization
• Drive material revenue growth by putting more campaigns in market, faster

Summing it up

Contextual personalization at scale is no longer optional, and the cost of waiting is only rising. Brands that move first are setting the new standard for performance.

That’s our read from Chicago. If you’re wrestling with the same challenges, let’s connect.

Why personalization "that finally works" requires AI embedded in every step of the customer data-to-execution workflow.

AI is transforming how marketing gets done. GenAI sped up content creation. Decisioning engines improved analytics and surfaced next best actions. But the greatest value of AI isn’t faster content or basic next steps, it’s solving the real complexity of personalization.

For marketers, the complexity has always been in the data → signal → execution process. Accessing, exploring, and activating data to turn raw signals into usable attributes across channels at scale has been the bottleneck. Marketing teams are locked in a dependence on technical teams, which inherently means that action happens after a customer moment has passed. The complexity of launching personalization forces painful trade-offs: more campaigns at lower performance, or slower, high-stakes bets with limited reach.

How Agentic AI changes the equation

Agentic AI ends that trade-off. By reasoning over live data, agents can generate new fields, detect signals, and launch campaigns that adapt as conditions change. Such work is impossible for people, or at least simply too slow and unscalable. With the right team of agents, personalization scales and performance improves.

AI needs the right foundation

The catch? AI is only as good as its foundation, the data. Too many AI use cases fail because they sit on incomplete or siloed data.

That’s why Agentic Marketing requires an AI-First Customer Data Platform. An AI-First CDP doesn’t bolt decisioning onto limited infrastructure. It embeds AI into its architecture, agents, and workflows so that marketers can act on a hundred times more signals and customer moments, and make a hundred times the decisions, without slowing down under the weight of complexity.

The CDP, reimagined

The CDP’s role has always been to connect data to customer experience. Now it must evolve. It must adapt continuously on fresh data, generating fields and audiences on the fly, and automating complex downstream execution to create micro-audiences at scale. The AI-First CDP isn’t a point AI solution on top of limited data. It’s the foundation for personalization at scale that finally works.

The evolution of Customer Data Platforms

Let's take a quick tour of how we got to where we are today. CDPs were invented to solve a fundamental problem: customer data was scattered across dozens of systems, with no easy way to unify it, resolve identities, and activate data for meaningful marketing tactics. Marketers struggled to see the full picture of their customers, and data teams struggled to keep up with endless requests for exports, joins, and custom pipelines. The original promise of the CDP was to bring all that data together and make it usable for customer engagement.

Traditional CDP

Traditional CDPs solved an immediate pain point by giving marketers a way to centralize profiles and create data-driven campaigns from one place. The promise was a unified customer view for marketing. But the limitations quickly surfaced. 

Data had to be duplicated into the CDP, leading to latency and governance challenges. Analysts and data scientists were locked out of the loop, and the intelligence inside the CDP couldn’t flow back into the enterprise. These systems improved access for marketers but created silos for everyone else. A step forward. It felt powerful for a time, but data movement created pain.

Composable CDP

The next phase was the rise of the composable CDP. Instead of buying a packaged system, companies started building CDP capabilities directly on their cloud data warehouses. This solved key problems of duplication, latency, and governance. Marketers and data teams could finally share the same source of truth. Composable CDPs became a bridge between marketing and data infrastructure. The benefit was better alignment and fresher data. 

The limitation is that workflows still remain manual. Data teams and marketers still have to define segments and journeys in advance. Campaigns remain a manual process, involving the right data and coordination between teams. Marketers often depend on data pulls or SQL from technical teams. Segments and journeys are delicate, locked to pre-defined rules, and slow to adjust when customer signals shift. Campaign orchestration becomes a series of handoffs, and optimization is still reactive rather than adaptive.

Composable CDP + AI decisioning

As composable CDPs matured, vendors began layering on AI decisioning. These engines promised next-best actions and predictive scores, and some have delivered incremental improvements. But AI decisioning is fundamentally a point solution. It is bolted onto existing systems rather than embedded across workflows. The limitations:

• Narrow scope: They optimize within the data they can see, which excludes the majority of contextual and operational signals.
• Manual dependence: Campaigns still rely on predefined segments and journeys built by marketers and data teams.
• Execution gap: Most AI-decisioning solutions require that recommendations be pushed into other systems for activation, which slows down response times and leaves optimization reactive.
  

The result is smarter recommendations layered on top of static workflows. Personalization is constrained by delicate segments, manual orchestration, and delayed activation. It has been noted that journey delay optimization (the days between the 1st, 2nd, and 3rd touchpoints) is the biggest lever for increased performance within AI Decisioning results. It's better than before, but still far from exercising the potential of AI for adaptive, data-driven marketing.

AI Decisioning is akin to racing down a mountain, but being constrained to take only a single route. If you’re able to explore all routes ahead of time, you may have chosen a different run. As the conditions of the mountain change, you may again revise which run to take. And looking across all skiers on the mountain, the best routes will vary by each person’s skill levels and abilities. 

In short, there’s value in AI Decisioning (and it can also work alongside Simon AI), but it’s an optimization between the trees and doesn’t see the broader mountain.

The AI-First CDP

The AI-First CDP is different. It builds on the composable model, involves no data movement, and runs directly inside the cloud data warehouse with no replication or hidden black boxes. Unlike traditional CDPs, it doesn’t copy data into a proprietary environment. Unlike some composable add-ons, it goes beyond exposing predefined tables for marketer access. Instead, it makes the warehouse itself the execution engine.

Data composability

This is the role of data composability. By keeping all customer, contextual, and operational data unified in the warehouse, governed and extensible, it creates the foundation for AI to act in place on the full data universe. Data composability ensures agents can reason over the entire available universe of signals, not just a limited subset.

Composable AI Agents

On top of that foundation, Composable AI Agents are the breakthrough. They turn composable data into continuously adapting insights and execution, moving directly from the source of data downstream into engagement platforms. Agents handle enrichment, reasoning, orchestration, and optimization on live data. Marketers no longer have to manually define every segment or trigger. They set goals, and the agents take over. They find patterns, create new fields, detect real-world moments, prepare the data for marketing use, and automate the triggers that launch campaigns.

True unified data, 1:1 personalization at scale

An AI-First CDP extends data unification, analysis, attribute generation, and activation to customer, contextual, operational, and marketplace data in the warehouse itself. This means product inventory, service tickets, reviews, and weather patterns can all become real-time signals that drive personalization.

The result is personalization at real scale: not dozens of batch-built segments, but thousands of adaptive, contextual campaigns, connected to the enterprise data environment.

‍

The core capabilities of the AI-First CDP

The AI-first CDP is a system designed to integrate AI into every step of the data-to-customer experience workflow. To understand what makes it different, let’s review the core capabilities that define the AI-first composable CDP:

1. Zero-copy AI engine: AI where your data lives

An AI-First CDP runs directly in a data cloud, eliminating the need for reverse ETL, the requirement of copying data out of the warehouse into other tools. That means no hidden stores, no replication, and no stale copies. Composable AI Agents tackle their “jobs to be done” on live data and governed data.

• Data never leaves the warehouse, preserving governance and compliance.
• Zero-latency activation enables real-time use cases
• Eliminates fragile reverse ETL pipelines and extra storage layers.

2. Operates on all data, not just customer profiles

Personalization requires more than demographic or behavioral fields. The AI-first CDP unifies operational, contextual, and unstructured data alongside customer profiles, giving AI the raw material to surface hidden drivers of engagement and conversion.

• Combines customer data, business data, and real-world signals and has the power to analyze it, prepare it, and execute on it.
• Escapes the trap of assumptions by letting AI explore all signals, not just the ones marketers predefine.
• Provides a complete view so campaigns can respond to the real factors influencing engagement and conversion.

3. Context is its superpower

The AI-first CDP adapts campaigns to real-world conditions by ingesting contextual signals, tying those signals to customer identity, and acting on these joined data points in near real time. For example, a zip code can be tied to a weather pattern, and a home address can extend demographic context. 

• Pulls in signals such as weather, local events, or product availability
• Detects moments like frustration in support chats or sudden spikes in regional demand
• Responds instantly to what's happening now, not what was true yesterday
• Extends identity knowledge into the context layer, so every campaign can be enriched with a fuller picture of the customer and their context. 

4. Embedded AI across workflows

Rather than simply layering AI on as a recommendation layer, an AI-First CDP embeds AI into the entire workflow, from finding signals to preparing attributes, triggering actions, and optimizing targeting. True AI-enabled personalization can add tremendous complexity to CDP workflows, but the AI-first CDP automates that end-to-end. It fundamentally improves workflows by introducing a new capability set where agents continuously reason and act on live data. Instead of dozens of campaigns, the AI-first CDP can adapt and execute thousands of campaigns at a time. 

• Detects new signals and patterns tied to marketing goals.
• Enriches and structures data into marketer-ready fields.
• Continuously triggers and optimizes thousands of campaigns across channels.

5. Learns and shares with every cycle

The AI-First CDP creates a feedback loop by writing enriched intelligence back into the customer data warehouse so it enhances the source of truth and compounds the value of data over time. Every campaign fuels the next, strengthening the enterprise data foundation with new AI-generated insights and attributes.

• Creates new attributes automatically from raw and unstructured data.
• Writes predictions, performance insights, and enriched fields, audiences, and segments into the warehouse for enterprise use.
• Improves models and performance with each campaign cycle.

6. Makes data marketing-ready with a semantic layer

Even when all the right data is in the warehouse, it is typically still locked in technical schemas that marketers can’t use. The AI-first CDP bridges that gap with a semantic layer where agents translate raw data into business-ready concepts. This makes it possible for marketers to define goals in plain language while AI assembles the fields, audiences, and triggers behind the scenes.

• Enriched attributes: Predictive fields such as churn risk, intent, or product themes.
• Real-time triggers: Contextual signals like inventory changes, sentiment shifts, or spikes in demand.
• Goal-based workflows: Frameworks that translate marketer objectives into audiences, triggers, and campaigns.

7. Deep channel integrations and personalization fields

The AI-first CDP not only discovers insights, but it delivers them directly into every engagement channel. Deep channel integrations mean AI-generated fields and triggers flow seamlessly into email, SMS, push, in-app, and ad platforms without extra pipelines or manual work. Where most CDPs struggle with even basic personalization, like inserting a first name into a campaign, an AI-first CDP activates complex, context-aware personalization across all channels.

• Seamlessly orchestrates personalization fields across channels, enabling AI-generated attributes and triggers tied to resolved identities to flow into email, SMS, push, in-app, and ads without manual work.
• Continuously refreshes personalization fields based on fresh data so campaigns use the latest signals to reach customers at the moments they are most likely to engage and convert.
• Extends personalization fields at scale so thousands of campaigns can adjust automatically across channels.

Together, these elements allow marketers to define outcomes in plain language while the system builds and adapts the underlying orchestration automatically.

Is your CDP built for AI?

Most CDPs weren't. They promised personalization but left marketers stuck with backlogs, static segments, and missed opportunities. Ask yourself:

• Does your CDP run inside your warehouse with zero data movement?
• Does it unlock all customer, business, and contextual signals for AI use?
• Does it tie context to identity so AI can build a full customer picture in real time?
• Does it integrate deeply to create AI-powered personalization fields that work across campaigns and channels?
• Does it translate raw data into marketer-ready fields, audiences, and triggers with the help of agents?

If the answer is no, your CDP isn't ready for AI.

The new foundation for personalization

AI has changed the pace of marketing. Personalization can’t be batch-based or rules-driven anymore. To keep up, you need a CDP built for AI from the start, one that runs inside your warehouse, operates on 100x more data, and powers agentic workflows that adapt in real time.

That’s what we built Simon AI to do. A foundation that makes personalization finally work, at scale. Let’s talk about it. Contact our team, and we’ll show you how the Simon AI Composable CDP will unlock data for 1:1 personalization at your brand.

I owe marketers an apology.

Before Simon AI, I led analytics and ML teams in government, SaaS, and e-commerce. Marketers were always the most demanding stakeholders I worked with. You needed more data, different data, perfectly tailored data. And you needed it yesterday. Too often, my answer was “Open a ticket.”

I didn’t get it. Marketing isn’t fickle. It’s situational. Markets move, customers shift, competitors react, and relevance has a half-life. You knew personalization was how to stand out, but the signals were hidden and the window closed before you could act. I didn’t give you a way to change that.

The Context We Missed: Weather Moments

One September, forecasts called for record heat followed by an early freeze across the Rockies and Plains. In Denver, that meant a 60-degree drop in days. At the e-commerce startup I worked for, we had the inventory customers would need. The marketing team had the right idea— launch weather-aware campaigns, timed around shipping windows.

Then the plan died. You couldn’t see the right signals, build the logic fast enough, or coordinate execution without weeks of work and a stream of tickets. Ironically, the weather feeds existed, but for logistics, not marketing. We missed the moment.

That’s the crisis every marketing team faces today:

• Data is everywhere, but most tools only surface what’s predefined. Untagged signals stay hidden, and teams act on assumptions.
  
• Customer records capture the what but rarely the why. Weather, sentiment, and inventory shifts explain behavior, but sit outside most workflows. Context is missing.
  
• Even when the right data exists, it takes a series of handoffs and tickets with the data team. By the time campaigns launch, weeks later, the moment is gone.
  

Now, AI is accelerating change in marketing. But while it’s being used for content and predications, most teams are not using it where it works with the greatest effect: solving for complexity. Data and execution are where the complexity lies for marketing teams. 

A New Model for Marketing

Let’s solve for complexity in marketing, and let’s start with the toughest problem: personalization. Imagine that your workflow starts with your goal—“reduce churn,” “activate weather-driven demand”—and AI agents do the heavy lifting, surfacing insights, building audiences, orchestrating activation, and optimizing continuously.

Those agents operate securely in your data cloud, translating live customer and contextual signals into campaigns that continuously adapt. without handoffs or lag. Finally, a system that enables you to explore fresh customer moments, operationalize them, and then execute, all with the construct of your goal and your plan guiding the way. 

We built Simon AI to do just that.

The Simon AI Personalization Studio is the workspace where marketers rapidly create and launch contextually relevant campaigns. It gives you a goal-based workflow powered by agents, running on a composable CDP in your enterprise cloud data warehouse. In one place, you can see your goals, signals, audiences, and performance, and interact with agents that handle the complexity behind the scenes.

Here’s how it works:

1. Start with your goal in a Blueprint—a reusable playbook that translates your outcome into a strategy and execution plan. You describe the goal, and the Blueprint generates the audiences, triggers, and scaffolding you need.
   
2. Agents explore your signals. They turn raw customer, product, and contextual data into concepts you can act on:
   
   • AI Fields are attributes about customers or products. They enrich data so you can segment, score, or personalize. Think of a Cold-weather readiness score, an Affinity to heavy hoodies, or a Next-week heat index by ZIP. Put simply,  AI Fields describe what you know right now, based on all the data.
     
   • AI Moments are triggers from real-world events. They mark when something changes that should drive action, like a sudden cold front, a trending hashtag, or an inventory update. Put simply, AI Moments trigger the system to act at the right time.
     
3. Approve with control. Preview logic, audiences, and content before activation. Every step includes transparency, so marketers stay in charge and data teams maintain governance.
   
4. Launch and activate everywhere. Campaigns push across your integrated engagement platforms from one place, and every action ties directly to outcomes like revenue, LTV, repeat purchase, retention, etc.
   
5. Optimize continuously. As new signals stream in and performance shifts, agents automatically adjust targeting and recommendations, keeping campaigns fresh without manual rebuilds.
   

This represents a dramatic shift. Instead of endless tickets, handoffs, and one-off campaigns that trade scale for personalization, marketers now work in a purpose-built workspace with agentic support. You operate with the full universe of customer, business, and contextual data, and you execute with speed and scale. Personalization with context is no longer a costly compromise. It’s the new way of working.

Use Cases in Action

Simon AI is already powering new kinds of campaigns across industries:

• Weather-aware promotions. Forecasts by ZIP trigger localized offers—outerwear ahead of a cold snap, lightweight picks before a heatwave—automatically adapting as conditions shift.
  
• Contextual retail personalization. Large retailers use AI Fields and AI Moments to combine product data, customer behavior, and real-world context. The result is timely recommendations that increase repeat purchases and strengthen loyalty.
  
• Trend-driven marketplaces. Social trend detection surfaces emerging designs or themes. AI Fields match shopper affinities while Blueprints automate outreach, curating what to feature and when without new pipelines.

Simon AI™ Social Moments extends this model to social demand, detecting emerging trends and preparing audiences while interest is still building.

“In the live events business, timing is everything. Simon AI Personalization Studio lets us move at the speed of our market. When a hot artist announces a tour or a big game gets scheduled, our Blueprints automatically identify the right audiences and trigger campaigns across multiple channels. We’re capturing demand in the moment, not days later when the excitement has cooled.”

 — Steve Mastrocola, Senior Director of Audience Marketing, SeatGeek

Why This Matters Now

The same moments that once slipped away, like a sudden weather swing or a surge in demand, are now opportunities you can act on. With Simon AI, those signals don’t just sit in the background. They turn into campaigns that help you stand out, convert more customers, and deliver experiences that actually feel relevant.

These kinds of capabilities weren’t accessible to marketers before. Simon AI changes that. The Personalization Studio is a place to set your goals, interact with agents that handle the complexity, and act on the full universe of customer, business, and contextual data, with the speed and scale today’s market demands.

Request a personalized demo of Simon AI today and see how your workflows transform with Agentic Marketing. 

‍From “What if we had that field…” to “Let’s launch a campaign tomorrow.”

If you’ve ever found yourself staring at your segment builder, knowing there must be a better way to target the right customers for your dream campaign but are unsure how to get there… this one’s for you.

At Simon AI™, we talk to marketers every day who know what they want to do, but they just can’t access the data that would make it possible in time to capture the moment. Need to identify sci-fi superfans for an upcoming collection drop? Too bad your catalog doesn’t tag fandoms. Want to launch a campaign for last-minute gifters or sneakerheads or “cozy fall vibes” shoppers (hello PSL season!)? You’re stuck waiting on your data team to build the fields: a process that can take weeks (or often more).

We’re changing that.

With the launch of the Simon AI™ Data Agent, marketers can now go from messy, unstructured data to usable, intelligence-rich Simon AI Fields and AI Moments in a matter of hours - not weeks. AI Fields and AI Moments power advanced segmentation, contextual personalization, and campaign performance without relying on the data team every step of the way. 

This is the beginning of a new way to work. One where every marketer has domain experts by their side. Agentic domain experts are AI partners that understand the data, spot the patterns, and help launch campaigns faster than ever to ensure you never miss another prime marketing moment again.

Welcome to Agentic Marketing

Data Agents are part of Simon AI’s broader Composable Agent family - a new model for marketing execution built around goals, not guesswork. Instead of requiring rigid rules and SQL logic upfront, agents reason across customer behavior, product data, and real-world signals to create usable, actionable outputs like:

• AI Fields: structured intelligence from messy data (e.g. “Gift Intent Score” or “Affinity to Sci-Fi Themes”)
• AI Moments: triggers from the real world (like weather shifts, social trends, or product searches)
• Blueprints: reusable, always-on playbooks that automate the setup and execution of your goals

Together, composable Simon AI™ Agents act like your own data and execution team, working within your data cloud, under your governance, with no black boxes or handoffs required.  

Data Agents is your key to unlocking domain-specific experts: focused on transforming raw datasets, like product catalogs, reviews, behavioral logs, or even chat transcripts, into marketing-ready intelligence.

What makes Simon AI Data Agents different

Where traditional systems surface what’s already tagged, Data Agents look for what’s been missed when preparing data for marketing.

No predefined rules required

You don’t need to tag every product by hand or define categories upfront. Data Agents uses LLMs and Snowflake Cortex to infer meaning from fields like titles, descriptions, images, and even unstructured sources like product specs or instruction manuals.

Fields you didn’t know to ask for

Want to target “boho holiday shoppers” or “customers who are about to travel”? Data Agents builds fields like Style_Aesthetic and Travel_Readiness_Score based on real data patterns - even when those fields don’t exist in your schema.

No more waiting on data teams

Most marketing teams rely on ticketing queues and requests to their BI teams to get a new field created for use in segmentation and personalization. With Data Agents, what used to be a month waiting for your ticket to get picked up turns into hours or a single day; marketers can launch high-impact campaigns while the moment still matters.

Explainable and governed

Every AI Field comes with metadata and a reason for being. You can inspect lineage, logic, and examples - all while keeping your data securely in place inside your cloud data warehouse.

What’s new for marketers

The launch of Data Agents introduces powerful new capabilities:

• AI Field Creation: Use natural language prompts to request new fields like “sci-fi theme” or “likely gift for dad,” and let the agent do the enrichment work.
• AI Field Discovery: Explore signals you didn’t know you had: inferred intent, passion clustering, co-purchase patterns, and more. 
• Autonomous Use or Blueprint-Ready: Use Data Agents standalone for segmentation and personalization, or seamlessly plug into campaign Blueprints to activate instantly

In Action: Sci-Fi personas, no manual tags required

Let’s say you’re an eCommerce marketer planning a new sci-fi themed drop to coincide with the launch of the new Project Hail Mary movie trailer. You hop into Simon AI™ and type:

“I want to launch a new campaign featuring our sci-fi related products, but I don’t know which of our products can be linked to sci-fi.”

Data Agents scan your cloud data warehouse and respond with:

“I can help with that!  We can infer sci-fi product themes from your product catalog by analyzing product names, descriptions, and images. Want me to create an AI Field for you so you can personalize your campaign with sci-fi related products?”

You click “Approve.”

Within hours, you have a new field called SciFi_Product_Theme, populated and ready to use for personalization. Better yet, you didn’t need to define the rules or wait for data engineering to prioritize your ticket. 

That’s the power of agentic field creation, and it’s only just the start.

More use cases, more possibilities

The first data domains we’re launching with are Product Catalog Enrichment and Weather Moments, and already we’ve seen powerful examples like:

• Pop Culture Mapping: Tag unlicensed products with the most likely 3rd-party brand or franchise association
• Product Clustering: Group products based on upsell or cross-sell opportunity
• Cold Weather Readiness Score: Automatically scores customers based on their likely responsiveness to upcoming cold snaps
• Next Week Heat Index by Zip: Surfaces high-temperature forecasts at the ZIP-code level, enabling precise geo-targeting for warm-weather promotions

We’re constantly expanding to other data domains like weather, social trends, geography-based analysis, and session behavior - each with their own specialized data domain experts. Simon AI™ Social Moments adds social and cultural signals to this mix, detecting rising trends and preparing activation-ready audiences while demand is still emerging.

Why it matters now

Marketers are drowning in data but are starving for insight. They’re sitting on a goldmine of untapped signals - in catalog data, customer behavior, unstructured reviews, and support logs - but lack the tools to activate at speed. 

Data Agents turns that around. 

It delivers on the promise of AI in a way that’s usable, visible, and marketer-field. You don’t need to know SQL. You don’t need to guess what to ask for. You just need to know your goal.

Simon AI™ Agents - starting with Data Agents - take care of the rest.

Ready to unlock your hidden signals?

Request a personalized demo of Data Agents today. Let us show you how Simon AI™ Fields turn your raw data into campaigns that convert.

Contact us

I founded Simon ten years ago to help brands better engage with their customers.

Here’s my little secret: I’m actually a terrible customer.

I have no airline loyalty. Sure, I collect points, but I’ll always choose the fastest or cheapest option. Except for shoes, baseball caps, and Simon AI gear, I don’t wear logos. And I clean my inbox like a hawk. I skip most emails, unsubscribe fast, and report spam if I don’t recognize the sender.

Customers like me are hard to reach

At our AI Summit earlier this year, I spoke with a customer whose repurchase rate was below 25%. His team followed the standard playbook: post-purchase sequences, cross-sells, seasonal offers, and loyalty programs. What happens? High drop-off. Whenever they push harder to hit quarterly numbers, unsubscribes spike. 

As he was talking, I realized two things. 

First, most customers behave like me. They’re distracted, inconsistent, and quick to disengage.

Second, Simon AI is built for precisely this problem.

The reality for modern marketers

Maybe I’m not so terrible after all. I do spend money. I do engage. Just on my terms.

This summer, I bought six tickets on SeatGeek to see the Commanders in DC. A week later, I needed a parking pass. Then my wife reminded me our six-year-old had outgrown his Commanders shirt.

When the weather turned hot, I ordered three swimsuits from Marine Layer. Before heading to Europe with the kids, I picked up a pair of Nike AF1s. Comfortable enough for long walks, but sharp enough for dinner.

The truth is, I’m just hard to reach. Between two kids, two dogs, and running an AI company, it takes a lot for a brand to get my attention.

That’s the reality facing every marketer. And until now, the kind of contextual personalization needed to move someone at their moment of interest has been out of reach.

Marketers are forced into painful trade-offs. As you launch more campaigns, performance drops. As you personalize, volume drops.

Over and over, I hear these challenges from marketing leaders at brands. 

• Data access.  Despite the proliferation of modern cloud data, marketers today still cannot access the data they need or receive it in time to move a customer to act. 
• Execution bottlenecks. Campaigns take too long to launch, leaving teams dependent on blunt, generic tactics. There’s no effect of acting on a customer moment six to eight weeks after it happens. 
• The context gap. First-party data only tells part of the story. The sharpest arrow in the quiver is context: signals from weather, inventory, social trends, events, and thousands of other signals that affect what a customer needs, how they decide, and what you can offer them. This isn’t even customer data, it’s that plus business data and real-world signals. 
• AI acceleration. The pace of change is only increasing. Most teams are still experimenting with surface-level AI apps for copy or analytics. They need help applying AI to solve complex problems.

These aren’t edge cases. They’re the reasons marketers can’t break through today to what they aspire to be, what they know will work. Just listen to Charles at Redbuddle. He gets it.

‍

Think of it this way. Knowing I prefer solid-colored t-shirts is an insights question. But answering it takes coordination with data teams. Knowing I restock when summer hits requires context about weather, season, and past shopping behavior. Usually, that’s where marketing imagination hits the reality of data complexity and dependencies. 

This is where Simon AI comes in.

The Simon AI™ Agentic Marketing Platform

Today, I’m excited to introduce the new Simon AI.

The Simon AI brings together an AI-first composable CDP, composable Simon AI Agents, and the marketer-friendly Simon AI Personalization Studio. It creates a new workflow where marketers set goals, agents turn live customer and contextual data into attributes and triggers, and automated campaign execution that scales and adapts with continuously fresh data.

Simon AI combines these three core components into one unified system:

Simon AI Composable CDP

The AI-first CDP runs natively in your cloud data warehouse. 

• Access to 100x more customer and contextual data than traditional CDPs.
• Zero ETL with the Snowflake AI Data Cloud — live data flows directly from the data cloud into campaigns.
• Identity resolution, audience matching, and predictive insights built in.

Simon AI Agents

Composable AI Agents act as your data and execution team.

• Signal detection. Spot churn risk, sudden demand spikes, social trends, weather, inventory shifts, and so much more.
• Data prep. Messy signals become ready for campaigns with Data Agents that do the heavy lifting, removing typical dependencies on data teams. 
• Enrichment. Write new fields and segments back into the data cloud for enterprise use.
• Orchestration. Automate workflows across Braze, Attentive, Iterable, and more.

As an example, Simon AI™ Social Moments builds on this foundation to bring social and cultural signals directly into your activation workflows.

Agents take on the heavy lifting so your team can focus on strategy, creative, and customers.

Simon AI Personalization Studio

A marketer-friendly workspace that starts with goals, not static segments.

• Blueprints. Translate goals like “increase repeat purchases” into adaptive strategies.
• AI Fields and AI Moments. Agents find the signals and patterns for you, then convert live data into marketing-ready attributes and triggers.
• Adaptive execution. Based on guidance from your Blueprints, campaigns evolve automatically as signals change.

The Personalization Studio feels simple as you prompt your agents to guide you through setting up your campaign. But underneath, the agents and the CDP are doing very hard work so that you can focus on your goal and the performance of your campaign.

Together, these capabilities create a new model for how marketing gets done: Agentic Marketing. The result is higher converting campaigns, faster launches, better customer experiences, and measurable revenue growth. With Simon AI, even small marketing teams perform like large ones while staying focused on strategy and creativity.

Your data, your cloud, your rules

We know some teams are excited about AI but cautious about where their data goes. Simon AI runs directly in the Snowflake AI Data Cloud, and there are deployment options to many other cloud data warehouses. The architecture ensures data is always governed by the same standards your enterprise already uses.

That’s what makes personalization scalable, measurable, and secure.

Why now: our transformation to AI-first

AI is changing marketing forever. The tools of the past can’t keep up with the pace of change, the volume of data, or the expectations of consumers. To compete, marketers need to act on 100x more signals, make 100x the decisions, and execute micro-campaigns at scale.

Going forward, brand differentiation and campaign performance will rely on personalization at scale, and the only way to get there is with AI. 

That’s why we’ve transformed Simon into an AI-first company. Our platform is now Simon AI. And our new home is Simon.ai.

This isn’t a rebrand for the sake of a new look. It reflects the work we’ve done over the past 18 months to solve the most complex problems marketers face.

Join us on this journey

If you’re struggling with any of these issues, we’d love to talk:

• Customers disengaging after the first purchase
• Campaigns that take weeks instead of hours to launch
• You design campaigns based on assumptions, not new insights
• Know AI can help, but not sure where to start

Visit Simon.ai to explore the platform. Let us know you want to talk about how we can help solve your challenges. Or connect with our team directly at Shoptalk this week to see how Agentic Marketing can work for you.

The future of marketing belongs to brands that adapt in the moment. That’s what Simon AI is built for.

Simon AI will change how our customers engage with consumers and how their campaigns perform.

I’m excited for what comes next. See you soon.

It seems like only yesterday that AI burst on the scene in a flurry of buzzwords.  Suddenly, every predictive model and machine learning algorithm was being rebranded as “AI.”

And that was just the beginning. As AI took center stage, expectations surged. Marketers found themselves sorting through a wave of new terms, tools, and claims, without a clear sense of what AI could actually solve beyond writing copy. Could it help with the complex data and workflow challenges at the heart of their biggest execution problems?

The latest development, however, presents an opportunity to change that. “Agentic AI” is the latest step in the rapid evolution of practical AI. It creates a framework by which various AI solutions can work together to do complex work and achieve results, not just copy or recommendations.

To help marketers capitalize on this inflection point, we’ll demystify Agentic AI, explaining not just what it is, but also how it can unlock new possibilities in CRM and one-to-one marketing. 

A quick primer on AI in marketing

To understand Agentic AI, it’s helpful to view it as part of a multi-phase evolution…one that began several years ago.

Phase 1: Predictive AI (machine learning → deep learning)

The foundation of modern customer analytics, this is where AI first gained real traction about a decade ago. It evolved from manually built algorithms using structured data into Deep Learning systems that could draw from much larger datasets, both structured and unstructured, delivering more accurate predictions without the need for step-by-step programming.

Focus: Forecasting future customer behaviors based on historic data 

Under the Hood: Regression models, decision trees, clustering, neural networks

Common Use Cases:

• Product Recommendations
• Customer Churn Prediction
• Lookalike modeling
• Next-best-offer targeting
• Propensity scoring
• Forecasting customer lifetime value (LTV)

While powerful, Predictive AI often required data science resources to build, implement, and optimize. Marketers could leverage the results, but couldn’t improve or expand them without help.

Phase 2: Expressive AI (natural language processing + generative AI)

This is what unlocked AI for the masses. In just the past three years tools like ChatGPT, Gemini, and Claude let non-technical users tap complex algorithms with simple chat-like prompts. The result was the ability to generate copy, images, and plans with a fraction of the time and resources previously required.

Focus: Receiving natural language inputs and generating content (text or images) in response

Under the Hood: Large Language Models (LLMs), Natural Language Processing (NLP), text-to-image, audio & video generation

Common Use Cases:

• Personalized subject lines and copy
• Conversational assistants (chatbots)
• Creative ideation for campaigns
• Dynamic (1:1) content generation for websites or emails
• SEO content and product descriptions

This phase democratized AI and opened the sandbox to folks without a data science degree.  But it still only accelerated tasks and required human prompts to drive every output.

Phase 3: Agentic AI (autonomous agents using predictive + expressive AI)

The newest development in AI’s evolution promises to supercharge what has come before.  Agentic AI combines predictive logic with expressive capabilities and adds autonomous decision-making.  

Focus: Performing multi-step processes to reach user-defined goals‍

Under the Hood: AI “agents” with memory, planning, and self-correction capabilities, along with goal-based orientation

Common Use Cases:

• Autonomous segment building
• Self-optimizing lifecycle campaigns
• AI-driven media buying
• Self-directed A/B testing
• Personalized cross-channel journey orchestration

This is a natural progression: from insight to creation to orchestration. And marks a major turning point in speed, scale, and customization.

With that as the background, let’s take a deeper look at Agentic AI, and how marketers can make it work for them.

The AI Agents: Meet your virtual team

At its core, Agentic AI introduces a new actor into the marketing tech stack: the AI Agent.

An AI agent is a software system that uses artificial intelligence to perform tasks and achieve goals with a degree of autonomy. It’s more than a simple chatbot: it can plan, adjust, and even interact with other tools and systems. Goal orientation is the critical distinction from the narrow, task-based completion of earlier phases. For example, Generative AI can write the marketing copy, but Agentic AI can define and run the entire campaign.

Think of these agents as your virtual team, each focusing on a different skillset to achieve your overall objective. And like all good teams, you don't need to tell them what to do every step of the way.

With that metaphor firmly in mind, let’s take a look at the types of agents that are emerging as key members of the AI-powered team.

‍

Critical to the Agentic AI concept, these agents can even collaborate with each other, passing data, syncing learnings, and adjusting strategies. For example, Automation Agents can tap Insights Agents to understand creative performance and work with Content Agents to automatically adjust future content based on results.

See how Data Agents work inside Simon AI

‍

Why it matters: From bottlenecks to scaled customization

If you’ve ever waited three weeks for a data request or had creative bandwidth stall a campaign idea, you’ll understand the potential of Agentic AI. It removes bandwidth and timelines as gating factors—letting programs and tests go to market faster.

Insights and Data agents mean you no longer need to wait in prioritization queues to understand segmentation opportunities or establish a new targeting dataset. Content agents enable scaled creative development cycles without the additional cost of an expanded creative team or a massive freelancer budget. And Automation agents working with Optimization agents allow you to deploy, test and iterate dozens of campaigns each week, without overloading your team.

As a result, marketers shift from “task executors” to “strategy drivers”. AI handles the time-consuming operations, freeing teams to develop bold ideas and richer customer experiences.

And just as importantly, it unlocks a true 1:1 customer experience, enabling micro-segments and journey moments that have always been on the wishlist, but were too resource-intensive to scale. Now Agentic AI can handle that scale, allowing you to drive customer connections in ways that blast campaigns never could.

What to look for in an AI platform

Since the AI space is evolving by the day, best practices are still being written. One thing is certain, though: the foundation is data. Customer data, campaign data, website data, customer care logs, social trends, weather forecasts, regional events…all fuel the agents that craft targeted campaigns and timely touchpoints, turning engagement moments into opportunities to delight.

That makes choosing the right AI Platform to house, process, and act on that data critical to realizing the full potential of this new frontier. In many ways, this is the natural next step in the evolution of the Customer Data Platform.

With that in mind, here is a checklist to guide your selection:

‍

Your CDP sits at the heart of Agentic AI activation. Agents can only perform as well as the data they operate on. The CDP provides the foundational dataset and it’s increasingly where agents live and operate. The right CDP becomes not just a warehouse, but a launchpad for intelligent action.

Simon AI™ Social Moments is an example of this evolution, using first-party data and real-time signals like social trends to help agents turn context into action.

What marketers should do next

If the first step is to understand the possibilities (and hopefully we’ve helped with that), then the next step is to explore those possibilities:

1. Speak with your CDP or MarTech lead  

Explore what agentic capabilities already exist in your stack.  If none are available internally, then speak with CDPs and platforms that already have capabilities in market and explore a potential fit.

2. Start with one Use Case  

Pick a goal or program that’s been on the roadmap for a while, but has been hampered by lack of resources or limited scale: a microsegment, a reactivation journey, a location-based recommendation series. These will serve as real-world use cases to see what your Agentic AI can do.‍

3. Focus on Human-in-the-Loop Workflows  

Begin with semi-autonomous agents where you review and approve recommendations.  Build personal trust and enterprise confidence in AI capabilities and the potential upside.

4. Be Ready to Course Correct  

As with any new collaboration, there will be growing pains. That is part of the process.  By adjusting and trying again, you’ll improve the Agents’ performance, as well as your own proficiency in leveraging them.

5. Shift Your Mindset  

Think of Agentic AI not as a tool, but as a collaborator. The goal is to give it the right guidance and feedback to let it run and ultimately scale your impact across multiple programs.

The future is now

Agentic AI is here. For marketers and CRM leaders, that’s both daunting and exciting. With clarity about what agents do and how they can help you, you’ll find an opportunity to solve real problems: faster segmentation, smarter campaigns, true 1:1 personalization at scale. It’s about shifting from repetitive execution to bold, strategic direction and a more expansive customer experience.

Is the technology evolving quickly? Absolutely. The technology shift has led to very rapid innovation in how marketing products are being designed today. Broad usage and intense focus is improving performance at a breakneck pace. And as these agents become more capable, the marketers who thrive will be those who lean in early, experiment often, and deploy these new “teammates” with clarity and creativity, unlocking myriad customer moments that feel both personal and limitless.Now is the time to start taking action to learn and choose use cases where Agentic AI can help you. In six months, the landscape will be different, and the brands that make moves now will be ahead of the curve.