In the digital age, customers expect personalized experiences across every interaction—whether browsing a website, engaging with a chatbot, or making a purchase. Delivering real-time recommendations based on user behavior, preferences, and historical data is no longer a luxury—it’s a competitive necessity.
Microsoft Dynamics 365 Customer Insights provides a powerful platform to unify customer data and power intelligent, real-time personalization. This article explores how real-time recommendations work in Customer Insights, their architecture, key use cases, and how to implement them effectively.
What Is Dynamics 365 Customer Insights?
Dynamics 365 Customer Insights is Microsoft’s Customer Data Platform (CDP). It helps organizations unify customer data from various sources to create a 360-degree customer profile, enriched with AI-driven insights.
Customer Insights has two major components:
- Data Platform (Data Unification + Segmentation) – Brings together customer data from CRM, ERP, web, and external sources.
- Journeys and Personalization (Real-Time Customer Journey Orchestration) – Enables contextual messaging, triggers, and real-time decisioning.
Real-time recommendations fall into the second category, using AI and behavioral data to dynamically suggest products, services, or actions.
What Are Real-Time Recommendations?
Real-time recommendations are personalized content or product suggestions generated dynamically in response to user actions. These are based on algorithms that evaluate:
- Behavioral data (clicks, searches, page views)
- Demographic data (location, age, gender)
- Transaction history
- Contextual signals (device type, time of day)
- Segmentation and affinity models
Examples include:
- “You might also like” sections on e-commerce sites
- Personalized product listings in marketing emails
- Suggested knowledge base articles during customer support interactions
In Customer Insights – Journeys, these recommendations are powered by AI models that operate in real time, updating based on the most recent user interactions.
Key Benefits of Real-Time Recommendations
1. Personalized Experiences at Scale
Whether you have 100 or 1 million customers, AI-driven recommendations deliver unique experiences for each person without manual effort.
2. Higher Engagement
Personalized product or content suggestions have significantly higher click-through rates (CTR) and conversion rates than static content.
3. Increased Revenue
Upselling, cross-selling, and reducing cart abandonment with intelligent recommendations directly boosts revenue.
4. Customer Retention
Timely, relevant suggestions based on behavior and preferences improve satisfaction and foster loyalty.
5. Omnichannel Consistency
Recommendations remain relevant across email, website, mobile apps, and customer service—ensuring a consistent experience.
How It Works: Architecture Overview
Let’s break down the architecture of real-time recommendations in Customer Insights.
1. Data Ingestion and Unification
Customer data is brought into Customer Insights from multiple systems:
- Dynamics 365 CRM/ERP
- Website and mobile analytics
- E-commerce platforms (e.g., Shopify, Magento)
- Email marketing tools
- POS systems
The unification engine matches identities and merges data into unified customer profiles.
2. Behavioral Tracking
Real-time interactions (e.g., page views, product clicks) are captured using:
- JavaScript SDK on web apps
- SDK for mobile apps
- Server-side tracking APIs
This behavioral data is mapped to the customer profile in real time.
3. Recommendation Models
Customer Insights provides built-in models for:
- Frequently Bought Together
- Trending Products
- Customer Similarity (Collaborative Filtering)
- Personalized Ranking
These models are pre-trained but also allow customization for industry or business-specific needs.
4. Real-Time Decision Engine
When a customer interacts with your website or opens an email, Customer Insights triggers the real-time decision engine to:
- Evaluate the customer context
- Query recommendation models
- Return the top recommended items/content
- Insert those items into the channel experience
This entire process takes place in milliseconds, enabling truly real-time personalization.
5. Integration with Channels
Recommendations can be embedded into:
- Marketing emails (via Dynamics 365 Marketing)
- Websites (via JavaScript SDK or Power Pages)
- Mobile apps
- Chatbots and virtual agents
- Contact center tools (Omnichannel for Customer Service)
Example Use Case: E-commerce Product Recommendations
Scenario:
An online retail brand uses Dynamics 365 Customer Insights to personalize customer experiences.
Flow:
- A customer visits the website and logs in.
- The tracking SDK sends data (visited products, time spent, previous purchases) to Customer Insights.
- A real-time recommendation model processes this data.
- The website dynamically displays a “Recommended for You” carousel.
- The customer adds a suggested item to their cart, increasing order value.
Result:
- Increased average order value (AOV)
- Higher conversion rates
- Better customer experience
Example Use Case: Support Knowledge Suggestions
Scenario:
A SaaS company uses Customer Insights to reduce support load.
Flow:
- A user contacts support through a live chat interface.
- Their profile, recent usage patterns, and past support tickets are sent to the decision engine.
- Relevant knowledge base articles are suggested in real time.
- The user resolves their issue without human intervention.
Result:
- Reduced support ticket volume
- Improved self-service rates
- Faster resolution times
Implementing Real-Time Recommendations
Step 1: Enable Real-Time Journeys in Customer Insights
- Ensure you’re licensed for Customer Insights – Journeys (formerly Dynamics 365 Marketing with RTM features).
- Set up the environment and connect to your Dataverse or CRM system.
Step 2: Set Up Tracking
- Add the Customer Insights JavaScript SDK to your website or integrate with apps via API.
- Configure events like product views, cart updates, and searches.
Step 3: Define Recommendation Models
- Use out-of-the-box models or customize your own using AI Builder or Azure ML.
- Optionally, feed external data via Azure Data Factory or Synapse.
Step 4: Embed Recommendations
- In marketing emails: use dynamic content blocks tied to recommendation APIs.
- On web/mobile: use APIs to request recommendations based on session or customer ID.
- In support channels: integrate with Omnichannel for Customer Service to display suggestions.
Step 5: Monitor and Optimize
- Use real-time analytics dashboards to monitor clicks, conversion rates, and engagement.
- A/B test recommendation strategies to see what drives better outcomes.
- Continuously retrain models using updated customer behavior data.
Advanced: Using Azure ML for Custom Models
For complex recommendation needs, you can connect Azure Machine Learning with Customer Insights:
- Export unified customer and behavioral data to Azure ML.
- Train custom models (e.g., deep learning, NLP-based recommendations).
- Deploy models as REST endpoints.
- Call those endpoints from Customer Insights workflows or websites.
This approach is ideal for industries like healthcare, finance, or high-complexity retail.
Privacy and Ethics Considerations
Real-time recommendations require sensitive customer data. Always follow best practices:
- Comply with GDPR, CCPA, and other data regulations.
- Provide opt-out options for tracking and personalization.
- Clearly communicate how customer data is used.
- Use AI transparency features to explain why recommendations are made.