MarTech Stack 2026: 4 AI Trends You Must Master

Marketing technology (MarTech) trends are constantly shifting, but the core objective remains: deliver the right message to the right person at the right time. The real challenge is making these complex systems work for you, not against you. Can you truly master the MarTech stack of 2026?

Harnessing AI-Driven Predictive Analytics in Salesforce Marketing Cloud

The future of marketing isn’t just about data collection; it’s about intelligent data application. Predictive analytics, powered by AI, is no longer a luxury but a necessity for understanding customer behavior before it happens. I’ve seen firsthand how this transforms reactive campaigns into proactive, highly effective strategies.

Step 1: Activating Einstein Prediction Builder

Salesforce Marketing Cloud’s Einstein Prediction Builder is where we start. This tool allows us to create custom AI models without writing a single line of code.

1. Navigate to your Salesforce Marketing Cloud instance. On the main dashboard, locate the “Einstein” menu in the top navigation bar.
2. From the Einstein dropdown, select “Einstein Prediction Builder.” This will take you to the Prediction Builder homepage.
3. Click the “New Prediction” button, usually located in the top right corner.
4. You’ll be prompted to name your prediction. For this exercise, let’s call it “Customer Churn Risk 2026.” Add a brief description, such as “Predicting which customers are likely to churn in the next 30 days based on engagement data.”
5. Under “Select Object,” choose the data extension that contains your customer data – typically your primary “All Subscribers” or “Customers” data extension. Ensure this data extension has historical engagement metrics like “Last Purchase Date,” “Email Open Rate (Last 90 Days),” and “Website Visits (Last 30 Days).”
6. Click “Next.”

Pro Tip: Don’t try to predict everything at once. Start with a clear, high-impact prediction like churn or next best offer. The clearer your objective, the more accurate and actionable your model will be.

Common Mistake: Choosing a data extension with insufficient historical data. Einstein needs a robust dataset to learn from. I always recommend at least 12-18 months of consistent data for meaningful predictions.

Expected Outcome: A new prediction model initialized, ready for feature selection and training.

Step 2: Defining Prediction Fields and Examples

This is where we teach Einstein what “churn” looks like. We’ll define the positive and negative examples for our model.

1. On the “Define Your Prediction” screen, you’ll see “Predict a Yes/No field.” Select this option.
2. For “What indicates a ‘Yes’ result?” choose a field that clearly signifies churn. If you have a “Churned (Yes/No)” field, select it and specify “Yes” as the positive value. If not, you might need to create a formula field that identifies customers who haven’t purchased in X days or haven’t engaged in Y days. For our “Customer Churn Risk 2026” prediction, let’s assume you have a boolean field called “Has_Churned__c” in your data extension. Select this field and set the “Yes” value to “True.”
3. For “What indicates a ‘No’ result?” select the opposite value, typically “False.”
4. Under “Segment Your Prediction,” you can optionally filter which records Einstein should consider. For instance, you might exclude brand new customers who haven’t had time to churn. For now, let’s leave it as “All records.”
5. Click “Next.”

Pro Tip: Be precise with your churn definition. A vague definition leads to an inaccurate model. If you define churn as “no purchase in 90 days,” stick to that. Don’t mix it with “no email opens in 60 days” in the same model.

Common Mistake: Not having a clear, historical indicator of the outcome you want to predict. You can’t predict churn if you don’t track who has churned in the past!

Expected Outcome: Einstein understands what “churn” means within your data, ready to identify contributing factors.

Step 3: Selecting Features and Building the Model

Now we tell Einstein which data points it should analyze to make its predictions.

1. On the “Select Fields” screen, you’ll see a list of all fields in your chosen data extension. Einstein automatically suggests relevant fields.
2. Carefully review the suggested fields. Include fields that logically correlate with churn, such as:
   • Last Purchase Date: (Date)
   • Total Purchases (Lifetime): (Number)
   • Average Order Value: (Number)
   • Email Open Rate (Last 90 Days): (Number)
   • Email Click Rate (Last 90 Days): (Number)
   • Website Logins (Last 30 Days): (Number)
   • Support Tickets (Last 60 Days): (Number)
   • Subscription Tier: (Text/Picklist)

   Exclude fields that are unique identifiers (like Subscriber Key or Email Address) or fields that would be known only after churn has occurred.

3. Click “Next.”
4. On the “Review and Build” screen, review your selections. If everything looks correct, click “Build Prediction.”

Pro Tip: Less isn’t always more here. Provide Einstein with a rich set of relevant features. The more context it has, the better it can learn. However, avoid redundant fields.

Common Mistake: Including fields that are a direct result of the outcome (e.g., “Churned_Campaign_Sent_Date”). This creates data leakage and an overly optimistic, but useless, model.

Expected Outcome: Einstein begins training its AI model. This can take anywhere from a few minutes to several hours, depending on data volume. You’ll receive a notification when it’s complete.

Step 4: Interpreting Results and Activating Predictions

Once built, it’s time to understand what Einstein found and put it to work.

1. Once the prediction is built, navigate back to “Einstein Prediction Builder” and click on your “Customer Churn Risk 2026” prediction.
2. You’ll see a “Prediction Score” and “Top Predictors” dashboard. This shows you the model’s accuracy and which fields most influenced the prediction. For instance, “Last Purchase Date” might be identified as a top predictor, indicating that customers who haven’t purchased recently are at higher risk.
3. Under “Activation,” click “Enable Prediction.” This will add a new field to your chosen data extension (e.g., “Customer_Churn_Risk_2026__c”) containing a score (0-100) for each customer, updated regularly.
4. You can then use this score to create segments in Marketing Cloud Journey Builder. For example, create a segment for “High Churn Risk” where “Customer_Churn_Risk_2026__c” is greater than 70.

Pro Tip: Don’t just look at the score. Understand the “Top Predictors.” This gives you actionable insights into why customers are churning, informing broader business strategies, not just marketing campaigns. I had a client last year, a subscription box service, whose Einstein model showed “Number of Support Tickets in Last 30 Days” as an unexpectedly high predictor of churn. We realized their support experience was a major pain point, leading to a complete overhaul of their customer service portal. Churn dropped by 15% in the next quarter.

Common Mistake: Trusting the prediction blindly. Always cross-reference with other data points and your own business intuition. An AI model is a tool, not a guru. Sometimes the data can be misleading if the initial setup wasn’t perfect.

Expected Outcome: A new, dynamically updated field in your customer data, ready for segmentation and targeted campaigns. You’ll have a clear understanding of your customers’ churn risk, enabling you to design specific retention journeys.

Real-Time Personalization with Adobe Experience Platform (AEP)

Generic messaging is dead. In 2026, customers expect every interaction to be tailored to their immediate needs and past behaviors. AEP, with its Customer Data Platform (CDP) capabilities, is the undisputed champion for this. We used to cobble together personalization with various tools, but AEP unifies everything.

Step 1: Ingesting Data into the Real-time Customer Profile

AEP’s strength lies in its ability to unify data from disparate sources into a single, real-time customer profile. This is foundational.

1. Access your Adobe Experience Cloud account and navigate to “Experience Platform.”
2. In the left-hand navigation, select “Sources” under the “Data Collection” section.
3. Click “Add Source” and choose your primary data inputs – typically “Adobe Analytics” for web behavior, “Adobe Campaign” or your CRM for email/CRM data, and potentially a custom source for offline purchases. Follow the prompts to configure each source connector, ensuring you map fields to the Experience Data Model (XDM) schema. This mapping is critical for unification.
4. Once sources are configured, navigate to “Profiles” in the left navigation. Confirm that your customer profiles are being stitched together into a “Real-time Customer Profile.” This means AEP is associating all data points (web visits, purchases, email opens) to a single customer ID.

Pro Tip: The XDM schema is your friend. Spend time understanding it. Proper mapping here prevents data silos later and ensures a truly unified profile. Don’t rush this step.

Common Mistake: Incomplete or incorrect field mapping during data ingestion. This leads to fractured customer profiles and ineffective personalization. We ran into this exact issue at my previous firm, where “email address” wasn’t consistently mapped across all sources, resulting in multiple profiles for the same customer.

Expected Outcome: A comprehensive, real-time customer profile for each user, aggregating data from all connected sources.

Step 2: Defining Segments for Personalization

With unified profiles, we can now create dynamic segments that update in real-time.

1. From the left navigation in AEP, select “Segments” under “Profiles.”
2. Click “Create Segment.”
3. Use the Segment Builder to define your audience. For example, to target “High-Value Shoppers Browsing New Arrivals”:
   • Drag and drop “Purchase History” > “Total Lifetime Value” > “is greater than” > “500” (or your relevant threshold).
   • Add another condition: “Web Interaction” > “Page View” > “URL contains” > “/new-arrivals” (within the last 15 minutes, for real-time).

   You can combine behavioral, demographic, and transactional data.

4. Name your segment “Real-time New Arrivals High-Value” and save it.

Pro Tip: Think about micro-segments. Instead of “all website visitors,” consider “visitors who viewed product X but didn’t add to cart in the last 5 minutes.” The more granular, the more impactful the personalization.

Common Mistake: Creating overly broad segments that don’t allow for meaningful personalization. If your segment is too big, your message will be too generic.

Expected Outcome: Dynamic segments that automatically update as customer behavior changes, ready to be used for targeting.

Step 3: Activating Personalization Experiences with Adobe Journey Optimizer (AJO)

Now we’ll use these segments to deliver personalized content across channels.

1. Navigate to “Journeys” in the left navigation of AEP, under “Orchestration.” This will take you to Adobe Journey Optimizer.
2. Click “Create Journey.”
3. Drag a “Read Audience” activity onto the canvas. Select your “Real-time New Arrivals High-Value” segment. Set the frequency to “Real-time” or “Hourly” depending on your needs.
4. Drag a “Condition” activity. For example, “If (customer’s primary device) is (mobile).”
5. On the “True” path, drag an “Email” activity. Configure the email content using personalization tokens that pull directly from the Real-time Customer Profile (e.g., {{profile.person.firstName}}, {{profile.productRecommendation.mostViewed}}).
6. On the “False” path (desktop users), drag a “Web Personalization” activity. This will integrate with Adobe Target to dynamically change website content. Configure a rule to display a personalized banner featuring new arrivals relevant to their browsing history.
7. Publish your journey.

Pro Tip: Test your personalization extensively. Use AEP’s built-in preview functions and run A/B tests on different personalization strategies. Small changes can yield massive results.

Common Mistake: Over-personalization that feels creepy. Balance relevance with respect for privacy. Don’t display data the customer might find too intrusive.

Expected Outcome: Customers receive highly relevant, real-time personalized content across their preferred channels, leading to increased engagement and conversion rates.

Automating Cross-Channel Journeys with Braze

The fragmented customer experience is a relic of the past. Modern MarTech demands seamless transitions between email, in-app, SMS, and push notifications. Braze excels at orchestrating these complex, personalized customer journeys. It’s truly built for the mobile-first world we live in.

Step 1: Defining a Canvas and Entry Rules

A Braze “Canvas” is where you build your multi-channel journeys.

1. Log into your Braze dashboard. In the left navigation, click on “Journeys” then “Canvas.”
2. Click “Create New Canvas.”
3. Name your canvas, for example, “Abandoned Cart Recovery – 2026.” Add a clear description.
4. Under “Entry Rules,” define who enters this journey. For an abandoned cart:
   • Select “Custom Event” as the entry trigger.
   • Choose your “Product Added to Cart” event.
   • Add a filter: “Custom Event” > “Product Purchased” > “has NOT been performed by user” > “since entry into Canvas.” This ensures only users who added to cart but didn’t purchase enter.
   • Set a delay: “Delay for 30 minutes” after the “Product Added to Cart” event.

Pro Tip: Your entry rules are everything. They determine the relevance of your entire journey. Be as specific as possible to avoid sending irrelevant messages. I’ve found that a 30-minute delay for abandoned carts is a sweet spot, giving the customer a chance to complete the purchase organically but still catching them while the intent is high.

Common Mistake: Overlapping entry rules for different canvases, leading to customers receiving multiple, conflicting messages. Ensure your entry rules are mutually exclusive where possible.

Expected Outcome: A clearly defined target audience enters your canvas based on a specific behavioral trigger.

Step 2: Designing the Multi-Channel Flow

Now, we build out the sequence of messages and actions.

1. Drag and drop a “Message” step onto the canvas. Choose “Email.”
   • Configure the email content: subject line “Did you forget something?”, personalized product images using Braze’s Liquid templating (e.g., {{event_properties.product_name}}), and a clear call to action to return to cart.
   • Set a delay of “1 day” after this email is sent.
2. Drag a “Decision Split” step. This allows you to create conditional paths.
   • Set the condition: “If (User Attribute) > ‘Last Purchase Date’ > ‘is greater than’ > ‘1 day ago’ (meaning they purchased since the last email).”
3. On the “No” path (they still haven’t purchased):
   • Drag another “Message” step. Choose “Push Notification.”
   • Configure the push notification: “Your cart is waiting! Get 10% off your next order.” Include a deep link back to the cart.
   • Set a delay of “2 days.”
4. On the “Yes” path (they purchased):
   • Drag an “Exit” step. This removes them from the journey.
5. Add a final “Exit” step at the end of the “No” path as well, after the push notification.

Pro Tip: Use A/B testing on each message within the canvas. Test different subject lines, creative, and calls to action. Braze makes this incredibly easy and provides clear performance metrics.

Common Mistake: Creating too many steps without clear value, or not offering an exit path for users who complete the desired action. You don’t want to annoy customers who’ve already converted.

Expected Outcome: A sophisticated, automated customer journey that responds to user behavior across multiple channels.

Step 3: Launching and Optimizing the Canvas

The final step is to put your journey live and continuously improve it.

1. Review your entire canvas flow for logic errors or missing content.
2. Click the “Launch Canvas” button, usually located in the top right.
3. Monitor the Canvas Analytics within Braze. Look at entry rates, conversion rates at each step, and message performance.
4. Use these insights to iterate. For example, if your push notification has a low click-through rate, consider changing the copy or offering a stronger incentive.

Pro Tip: Don’t just set it and forget it. Canvases are living entities. Optimize them regularly based on performance data. Even small tweaks can significantly boost conversion rates. What nobody tells you about these sophisticated tools is that they still require human intelligence and ongoing iteration to truly shine.

Common Mistake: Failing to monitor and optimize. A canvas isn’t a static campaign; it’s a dynamic system that requires continuous refinement.

Expected Outcome: A fully automated, cross-channel customer journey that drives engagement and conversions, with ongoing opportunities for improvement.

In 2026, mastering these MarTech trends isn’t about being a tech wizard; it’s about understanding how to use powerful tools to connect with your audience more intelligently and effectively than ever before. It’s about moving beyond basic automation to true, predictive personalization that drives measurable results.