AI Marketing: 2026’s 25% Engagement Boost

By 2026, if your marketing isn’t demonstrating clear, tangible value, you’re going to be ignored. It’s no longer about just having a product. It’s about survival, and that means proving your worth. Artificial intelligence is frankly the only way to do this at scale, letting us finally get away from generic marketing blasts and start having hyper-personal conversations that connect with what each customer actually needs.

Configuring AI for Hyper-Personalized Audience Segmentation in Google Ads

You can’t sell “value” into a void. You have to know who you’re talking to in a market this obsessed with it. Generic targeting is dead because customers have learned to tune it out. Here’s a breakdown of how to use the advanced AI in Google Ads for deep audience segmentation, so your message actually gets to the people who will see its value.

Accessing Audience Manager and Creating Custom Segments

1. Navigate to Tools & Settings: In your Google Ads account, click the wrench icon (Tools & Settings) in the top right.
2. Select Audience Manager: Find the “Shared Library” column and click Audience Manager. This is your command center for all audience data.
3. Initiate Custom Segment Creation: On the left, choose Custom segments, then hit the blue plus button (+ Custom segment) to start building.
4. Define Segment Parameters with AI Suggestions:
   • Segment Name: Be descriptive. Think “Value-Seeking B2B SaaS Founders” or “Eco-Conscious Urban Millennials.”
   • “People with any of these interests or purchase intentions” Section: This is where you let the AI do the heavy lifting. Start typing broad interests that get at your product’s core value. For an energy-efficient smart home gadget, you might type “sustainable living” or “energy cost reduction.” Google’s AI will immediately start suggesting related interests and intentions based on its massive trove of behavioral data. Pick 5 to 10 good ones that fit your value prop, and the system’s predictive analytics will find users who show strong signals for them.
   • “People who searched for any of these terms on Google” Section: Get specific here. Enter search queries that show someone needs what you’re selling, like “best CRM for small business efficiency.” The AI then does the work of analyzing these and finding people with similar intent, even if they used slightly different words.
5. Review and Save: Google Ads gives you an estimated audience size. Give your interests and search terms a final check, then click Save segment.

Pro Tip: Create a bunch of very specific custom segments. A single, broad “small business owners” segment is almost useless. Instead, make one for “small business owners seeking cost savings” and another for “small business owners prioritizing growth tools.” This is how you set yourself up for tailored messaging later. Being lazy with broad categories just kneecaps the AI’s ability to find real value seekers.

Common Mistake: Feeding the AI too few or overly generic interests. This just creates a broad, useless segment and wastes the AI’s precision. On the flip side, getting so specific that your audience size is a dozen people isn’t helpful either. You need to find the balance where the AI has enough data to work with without being boxed in.

Expected Outcome: You should end up with a set of finely tuned audience segments. Each one represents a group with a high chance of appreciating a specific part of your product’s value. These are now ready to be activated in your campaigns.

Implementing Predictive Content Personalization with Adobe Marketo Engage

Okay, so you found your value-seeking segments. Now you have to show them content that proves you understand them. The AI-powered predictive content engine inside Adobe Marketo Engage lets you do this dynamically, making sure each person sees the most relevant message and reinforcing the specific value you offer them.

Setting Up Predictive Content Rules in Marketo Engage

1. Access the Content AI Module: Log into Marketo. In the main nav, click Analytics, then pick Content AI. This is where you’ll manage the AI recommendations.
2. Define Content Assets: First, make sure your content is organized. Go to Design Studio > Files and Images. Upload your assets (posts, case studies, etc.) and tag them consistently with terms like “cost-saving,” “efficiency,” or “sustainability.” The AI needs these tags to understand what your content is actually about.
3. Create a New Predictive Content Stream:
   • In the Content AI module, click Content Streams on the left.
   • Click + New Content Stream.
   • Stream Name: Name it something clear, like “Value Proposition Stream – SaaS Efficiency.”
   • Target Audience: Select the audience you already built in Google Ads (e.g., “Value-Seeking B2B SaaS Founders”). Marketo syncs with Google Ads so this is pretty smooth.
4. Configure Recommendation Logic:
   • Under “Recommendation Settings,” you need to pick an objective. For this kind of value marketing, you want to choose “Maximize Engagement with Value-Aligned Content.”
   • Content Sources: Tell the AI where to get the content, pointing it to the folders or tags you set up, like everything tagged “efficiency” and “ROI.”
   • Weighting Factors: You’ll see sliders for things like “Recency,” “Popularity,” and “Relevance to User Profile.” For a market focused on value, I usually crank the weight on “Relevance to User Profile” up to about 60%. This tells the AI to prioritize the content that directly matches the person’s needs, even if it’s not your newest or most popular piece.
5. Implement Dynamic Content Blocks:
   • Open your landing page or email editor in Marketo and drag in a “Content AI Recommendation” block.
   • Point it to the content stream you just built.
   • That’s it. The AI now populates that block for each visitor with what it thinks is the most relevant content based on their profile and what they’ve done on your site.

Pro Tip: Check the performance reports in the Content AI module regularly. Marketo will show you which content is hitting the mark with which segments. This is free feedback for your content strategy. For example, if you see that your “ROI calculator” is crushing it with B2B segments while your “feature list” page is a dud, that’s a massive hint to create more tools that quantify value.

Common Mistake: Not having enough tagged content for the AI to work with. If your content library is sparse or poorly organized, the AI can only serve up generic recommendations. You have to feed the machine. Make sure you have content that covers all the different angles of your product’s value.

Expected Outcome: People coming to your site or opening your emails will see content that feels like it was picked just for them. This leads to them sticking around longer, engaging more, and seeing your product as more relevant. That’s how this increases conversion rates.

AI-Driven Pricing Optimization in Salesforce Commerce Cloud

Value is a function of both features and price. In markets where people are obsessed with value, the “right” price is never static. It has to adapt based on the competition, the customer segment, and the perceived value of your product. The AI inside Salesforce Commerce Cloud gives you the tools to create dynamic pricing that reflects this reality.

Configuring Einstein Pricing Recommendations

1. Enable Einstein Features: In your Salesforce Commerce Cloud admin, go to Site Development > Einstein > Settings. Find “Einstein Pricing Recommendations” and make sure it’s toggled on. This turns on the AI engine.
2. Define Pricing Rules and Constraints:
   • Go to Site Development > Einstein > Pricing Rules and click + New Pricing Rule.
   • Rule Name: Call it something like “Segment-Specific Value Pricing.”
   • Target Segments: Link the rule to one of your key customer segments, like the “Eco-Conscious Urban Millennials.” Commerce Cloud can pull this from your CRM data.
   • Pricing Strategy: You’ll see a few options. For differentiating on value, the “Value-Based Dynamic Pricing” strategy is usually most effective. This lets the AI weigh the product’s perceived value for that specific segment when setting a price.
   • Price Floors and Ceilings: This is important. You have to set a hard Minimum Price (Floor) and Maximum Price (Ceiling). This stops the AI from giving away your product or pricing it into the stratosphere. It’s how you protect your margins but still give the AI room to work. A floor of 10% over cost and a ceiling of 2x your standard price is a reasonable starting point.
   • Competitor Data Integration: If you’re scraping competitor prices, you can feed that data to Einstein under “External Data Sources.” It can then factor in what competitors are charging for similar value.
3. Configure A/B Testing for Pricing:
   • Go to Site Development > Einstein > A/B Testing and create a new test.
   • Test Type: Choose “Pricing Optimization.”
   • Variants: You need at least two. One is your control (your normal pricing). The other uses your new Einstein-powered pricing rule.
   • Success Metric: Set your goal to either “Conversion Rate” or “Average Order Value” so you can measure the real financial impact.
4. Monitor and Refine: The Einstein dashboard shows you performance in real time. Check the “Pricing Impact Report” to see how different segments are reacting. I always tell my clients to let these tests run for at least 30 days. You need statistically significant data before you go making permanent changes to your pricing.

Pro Tip: This isn’t just about finding discounts. Value-based pricing can also tell you where you can charge a premium. The AI might find a segment of customers that prioritizes sustainability and is perfectly happy to pay more for an ethically sourced product. That’s a data point Einstein can use.

Common Mistake: Setting your price floors and ceilings too tight. If you don’t give the AI any wiggle room, it can’t actually optimize anything. Also, not feeding it competitive data means the AI is flying blind to market conditions, which can lead to bad recommendations.

Expected Outcome: You’ll have pricing that’s actually aligned with the perceived value for each customer segment. This should lead to better conversion rates and more revenue, because you’re aligning what you charge with what people are actually willing to pay for the unique value you provide.

Using AI for Customer Feedback Analysis with Medallia Experience Cloud

To really differentiate on value, you have to constantly listen to your customers and adapt. AI-powered sentiment analysis is how you do this at scale, digging beneath surface-level comments to find out what people truly value. For this kind of deep listening, tools like Medallia Experience Cloud are what I’ve seen work best in practice.

Setting Up AI-Powered Feedback Analysis

1. Integrate Data Sources: In Medallia, head to Data Management > Integrations. Hook up every feedback channel you have: surveys, support tickets, social media, product reviews. The AI’s insights are only as good as the data you feed it, so give it everything.
2. Configure Sentiment and Topic Analysis Models:
   • Go to AI & Analytics > Text Analytics.
   • Language Models: Make sure the models are set for the languages your customers speak. Medallia has pre-built ones for over 100 languages.
   • Topic Extraction: Under “Topic Models,” click + New Topic Model. Don’t waste time guessing keywords. Use the unsupervised learning feature. Just upload a few thousand customer comments, and the AI will automatically group them into recurring themes like “ease of use,” “cost efficiency,” or “environmental impact.”
   • Sentiment Labeling: You need to do some quality control. Go through a small set of comments and check the AI’s work, correcting its sentiment labels where needed. This “human-in-the-loop” step is how you teach the AI the nuances of your business which makes it much more accurate.
3. Create Custom Dashboards for Value Insights:
   • In Dashboards > New Dashboard, build a custom view.
   • Add a few key widgets:
     • Sentiment Trend by Topic: This lets you watch in real time how people are feeling about key value drivers like “customer service responsiveness.”
     • Topic Frequency by Segment: This shows you which of your customer segments are talking about which topics the most. It’s how you discover what different groups really care about.
     • Driver Analysis: Use Medallia’s built-in tool to see which topics have the biggest impact on your overall CSAT or NPS scores. This tells you which value points to double down on.
4. Set Up Real-time Alerts:
   • Go to Alerts & Actions and set up notifications for big changes. For example, you can get an email if positive sentiment around “product reliability” suddenly drops 10% in a day. This lets you jump on problems before they get out of hand.

Pro Tip: Don’t just stare at the overall sentiment score. The real gold is in the negative comments about your core value props. One detailed, angry complaint about a feature failing to deliver on its promised value is worth more than a hundred generic five-star reviews. This is where a human analyst still needs to step in and add context to what the AI is finding.

Common Mistake: Only feeding it survey data. This gives you a skewed picture. You have to include the organic, unsolicited feedback from social media and review sites to get a complete view of what people really think and what they value.

Expected Outcome: You get a clear, data-backed map of what your customers genuinely value, where you’re delivering, and where you’re falling short. This is the kind of insight that should be driving your product roadmap, your marketing messages, and your customer service training. It’s how you keep refining the value you offer.

AI’s function in value marketing is precision. It gives us a level of customer understanding and responsiveness that was science fiction a decade ago. When you systematically put these AI-driven strategies to work, you can stop shouting at the market and start delivering hyper-personalized value that actually connects with people, giving you a real edge. This whole change in how people find information demands that marketers rethink content and visibility, especially as more search queries are answered directly by AI, which you can read more about in our piece on AI Answer Engines: 60% of Search by 2026.