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How AI enhances content suggestions

AI transforms content presentation from a static, one-size-fits-all approach to a dynamic, user-centric experience:

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Behavior-based recommendations

AI tracks and analyzes user activity—visited pages, time spent on content, previous purchases, and engagement with past recommendations—to suggest content or products users will likely engage with. Benefits:
  • Users tend to spend more time on sites showing relevant content
  • Personalized recommendations can improve conversions
  • Visitors find what they need faster
  • AI insights help businesses understand what works best

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Dynamic real-time content delivery

Instead of showing everyone the same static content, AI personalization adapts websites in real time based on who is visiting. Changes can range from tweaking headlines to completely overhauling messaging and UI elements. Example: A first-time visitor might see an introductory message with a site tour, while a returning customer could be greeted with exclusive discounts based on their past interactions.

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Building content suggestions with ButterCMS

Personalization within ButterCMS can be achieved using the Collections content type. By creating a Reference field in a component, you can programmatically populate content from collection records based on user segments.

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Step 1: Create a personalization Collection

Create a Collection to store personalized content recommendations: Suggested fields:
  • Segment Name (Short Text) - Customer grouping/segment from your CRM or CDP
  • Product Recommendations (Reference) - Links to recommended products or content
  • Cookie Target (Short Text) - Used to match frontend cookies for displaying content

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Additional fields to consider

FieldTypeUse Case
Promo Banner TextShort TextDisplay personalized promotional banners
Top Product RecommendationReferenceSpotlight products in footers or sidebars
Content RecommendationsReference (Multiple)Suggest related articles or pages
Call-to-Action TextShort TextCustomize CTAs per segment
Hero ImageImageShow different hero images by segment

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Implementing personalized suggestions

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Frontend implementation

Here’s how to fetch and display personalized content based on user segments: 
import Cookies from 'js-cookie';
import { useState, useEffect } from 'react';

function PersonalizedContent({ cookie_target }) {
  const [mappedProducts, setMappedProducts] = useState([]);
  const [loading, setLoading] = useState(true);

  useEffect(() => {
    if (cookie_target) {
      const fetchPersonalizedProducts = async () => {
        try {
          const response = await getPersonalizedProducts(cookie_target);
          setMappedProducts(response);
        } catch (error) {
          console.error('Error fetching personalized products:', error);
        } finally {
          setLoading(false);
        }
      };
      fetchPersonalizedProducts();
    }
  }, [cookie_target]);

  // Conditionally render based on cookie presence
  if (Cookies.get(cookie_target) !== 'true') {
    return null;
  }

  return (
    <div className="personalized-recommendations">
      {/* Render personalized content */}
    </div>
  );
}
You can set cookies based on URL query parameters (from ads or emails) or when users log into their accounts.
Privacy Note: When using cookies for personalization, ensure compliance with GDPR, CCPA, and other privacy regulations. Implement proper consent mechanisms and clearly disclose data usage in your privacy policy.

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AI-powered content recommendation strategies

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Predictive analytics

AI tools can predict what users may need or want, often before they realize it themselves:
  • Timely Offers - Suggest discounts, subscriptions, or complementary products at the right moment
  • Churn Prevention - Detect patterns that signal potential drop-offs and trigger retention strategies
  • Demand Forecasting - Understand seasonal trends and prepare content accordingly
  • Optimal Timing - Reach users when they’re most likely to engage
Traditional search uses basic keyword matching. AI-powered adaptive search understands user intent and analyzes past behavior:
  • Context-aware suggestions help users find content faster
  • Relevant options increase conversion likelihood
  • Continuous improvement based on user behavior and trends

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Content topic suggestions

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Using AI for content planning

The ButterCMS AI Assistant can help suggest content topics based on:
  1. Existing Content Analysis
    • Identify gaps in your content coverage
    • Find opportunities for content clusters
    • Suggest internal linking opportunities
  2. Audience Interest Signals
    • Analyze which existing content performs best
    • Identify trending topics in your industry
    • Understand seasonal content patterns
  3. Competitive Analysis
    • Discover topics competitors are covering
    • Find underserved content niches
    • Identify differentiation opportunities

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AI prompts for content ideas

Use these prompts with the AI Assistant: 
"Based on this blog post about [topic], suggest 5 related
topics we could cover to create a comprehensive content
cluster."

"Analyze this content and identify questions readers might
have that we haven't addressed."

"Suggest 10 content ideas for [industry/niche] that would
appeal to [target audience] during [season/event]."

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Third-party AI personalization tools

For advanced personalization, consider integrating specialized AI platforms with ButterCMS:

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Braze

Customer engagement platform for personalized messaging across email, SMS, mobile, and paid media. Key features:
  • Predictive analytics for customer behavior
  • Real-time personalization with “Action Paths”
  • Connected content pulling from APIs
  • AI-powered recommendations across channels

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Adobe Target

AI-powered testing and personalization within Adobe Experience Cloud. Key Features:
  • Automated A/B and multivariate testing
  • Rule-based personalization for audience segments
  • Real-time user identification and personalization
  • Same-page and next-page personalization

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Dynamic Yield

AI-powered platform for individualized digital experiences. Key Features:
  • AI-driven navigation personalization
  • Cross-channel recommendation engine
  • Targeted email promotions
  • AI-powered product recommendations

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Headless CMS + AI personalization

A headless CMS like ButterCMS is a powerful enabler of AI content personalization:
  • API-Driven Integration - Easily connect with AI tools like Dynamic Yield, Adobe Target, or Braze
  • Omnichannel Delivery - Deliver personalized content across web, mobile, email, and beyond
  • Structured Content - Define reusable components that AI can mix and match
  • Localization Ready - AI can dynamically serve correct language versions based on user preferences

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Best practices for AI content suggestions

Privacy First: Always obtain user consent and follow regulations like GDPR when collecting data for personalization.

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Implementation tips

  1. Start simple - Begin with basic segmentation before implementing complex AI
  2. Test thoroughly - A/B test personalized vs. non-personalized experiences
  3. Monitor performance - Track metrics like engagement, conversion, and satisfaction
  4. Iterate continuously - Refine algorithms based on performance data
  5. Maintain fallbacks - Always have default content for new or unidentified users

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Quality assurance

CheckWhy it matters
Relevance testingEnsure suggestions actually match user interests
DiversityAvoid filter bubbles by occasionally introducing variety
FreshnessInclude new content alongside proven performers
AccuracyRegularly audit AI recommendations for quality