AI content suggestions

ButterCMS does not ship a recommendation engine. The built-in AI assistant works on the text you select in a WYSIWYG field. Behavior-based recommendations and real-time personalization come from your own code or from the third-party tools listed further down, with ButterCMS supplying the content through its API.

How AI enhances content suggestions

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

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

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.

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.

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

Additional fields to consider

Field	Type	Use Case
Promo Banner Text	Short Text	Display personalized promotional banners
Top Product Recommendation	Reference	Spotlight products in footers or sidebars
Content Recommendations	Reference (Multiple)	Suggest related articles or pages
Call-to-Action Text	Short Text	Customize CTAs per segment
Hero Image	Image	Show different hero images by segment

Implementing personalized suggestions

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>
  );
}

getPersonalizedProducts is your own function that queries the personalization Collection by segment through the Collections API. 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.

AI-powered content recommendation strategies

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

Adaptive search

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

Content topic suggestions

Using the AI assistant for content planning

The AI assistant sees only the text you select and the request you type. It cannot read your other pages, analytics, search trends or competitor sites. Within that limit it is a quick way to brainstorm from a draft you already have:

Select a finished post and ask for related topics to build a content cluster.
Select a section and ask which reader questions it leaves unanswered.
Open the AI Assistant dialog with nothing selected and ask for ideas for an audience and season.

The answer lands in the WYSIWYG field, so run these prompts in a scratch page or delete the output after you have copied the ideas.

AI prompts for content ideas

Type these into the AI Assistant dialog:

"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]."

Third-party AI personalization tools

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

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

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

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

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

Best practices for AI content suggestions

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

Implementation tips

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

Quality assurance

Check	Why it matters
Relevance testing	Ensure suggestions actually match user interests
Diversity	Avoid filter bubbles by occasionally introducing variety
Freshness	Include new content alongside proven performers
Accuracy	Regularly audit AI recommendations for quality

Overview

Guides and tutorials

Creating and editing content

Managing media

Publishing lifecycle

Versioning and history

Authentication

Collaborating with teams

Localization

Multi-site and environments

SEO

AI assistant

How AI enhances content suggestions

Behavior-based recommendations

Dynamic real-time content delivery

Building content suggestions with ButterCMS

Step 1: Create a personalization Collection

Additional fields to consider

Implementing personalized suggestions

Frontend implementation

AI-powered content recommendation strategies

Predictive analytics

Adaptive search

Content topic suggestions

Using the AI assistant for content planning

AI prompts for content ideas

Third-party AI personalization tools

Braze

Adobe Target

Dynamic Yield

Headless CMS + AI personalization

Best practices for AI content suggestions

Implementation tips

Quality assurance

​How AI enhances content suggestions

​Behavior-based recommendations

​Dynamic real-time content delivery

​Building content suggestions with ButterCMS

​Step 1: Create a personalization Collection

​Additional fields to consider

​Implementing personalized suggestions

​Frontend implementation

​AI-powered content recommendation strategies

​Predictive analytics

​Adaptive search

​Content topic suggestions

​Using the AI assistant for content planning

​AI prompts for content ideas

​Third-party AI personalization tools

​Braze

​Adobe Target

​Dynamic Yield

​Headless CMS + AI personalization

​Best practices for AI content suggestions

​Implementation tips

​Quality assurance

How AI enhances content suggestions

Behavior-based recommendations

Dynamic real-time content delivery

Building content suggestions with ButterCMS

Step 1: Create a personalization Collection

Additional fields to consider

Implementing personalized suggestions

Frontend implementation

AI-powered content recommendation strategies

Predictive analytics

Adaptive search

Content topic suggestions

Using the AI assistant for content planning

AI prompts for content ideas

Third-party AI personalization tools

Braze

Adobe Target

Dynamic Yield

Headless CMS + AI personalization

Best practices for AI content suggestions

Implementation tips

Quality assurance