Real-Time Personalization at Scale: Powering Operational Excellence with AI-Native MDM

In today's retail landscape, personalization is no longer optional. In both B2C and B2B contexts, customers expect experiences tailored to them—with timely offers, relevant products, and contextual messages. 

But here's the catch: Personalization is only as good as the data behind it. Fragmented customer profiles, stale product attributes, inconsistent purchase records, and lack of behavioral insights can derail even the best personalization strategies.

That’s where Tamr comes in. Tamr is an AI-native master data management (MDM) platform that unifies data to deliver holistic customer views that power real-time personalization at scale.

The Challenge: Personalization Breaks Without Clean, Current Data

Customers today expect retailers to know who they are and to use that information to deliver experiences that align with their interests, intent, and purchasing behaviors. But delivering personalization requires retailers to quickly answer complex questions such as:

• Who is this customer, really?
• What have they purchased?
• What content and products have they viewed and engaged with across channels?
• What products or services might they care about right now?

However, when it comes to delivering the personalized experiences customers expect, most organizations struggle with challenges such as:

• Siloed systems: CRM, marketing automation, e-commerce, loyalty platforms, and other critical systems frequently have overlapping but inconsistent data.
• Latent data: Personalization efforts often rely on nightly batch processing so customer data can quickly become out of date. 
• Inconsistent identities: One customer might have multiple IDs across systems.

These issues wreak havoc on customer experiences—resulting in irrelevant recommendations, frustrating cross-channel experiences, and lost revenue caused by missed or mistimed engagement. 

How Tamr Enables Real-Time Personalization

Tamr solves issues related to real-time personalization by building and maintaining clean, complete, and operational master data and updating it continuously. Using Tamr, retailers can realize benefits including:

1. Unified Customer Profiles, Continuously Refreshed

Tamr uses machine learning for entity resolution—connecting and clustering customer records from many sources, including:

• Web and mobile activity
• Purchase and support history
• CRM and third-party data feeds

Unlike traditional MDM systems that rely on static rules or manual intervention, Tamr learns patterns from the data and updates customer profiles in real time as new data arrives.

2. Operational APIs that Serve Data in Real Time

Once Tamr unifies and enriches the data, it then makes it available through low-latency APIs that downstream systems like customer data platforms (CDPs), personalization engines, product recommendation tools, and marketing automation platforms can call as needed.

Using these APIs, retailers can:

• Gain immediate access to up-to-date customer attributes
• Personalize communications at the moment of interaction
• Trigger real-time messages—including email, SMS, or push notifications—based on the most current data
• Eliminate the need to wait on nightly or weekly batch processes

3. Context-Rich Decisions with Record History

Tamr does more than unify data—it also provides explainability. When a team asks why the retailer delivered a particular product or message to a customer, Tamr’s record history reveals:

• The original data sources and lineage
• The match confidence scores from the machine learning models
• Any manual edits or approvals made along the way

Example: Personalizing Product Recommendations in B2C Retail

Imagine a consumer named Maria is browsing sustainable baby products on a retailer’s website. A week earlier, she purchased a stroller in-store using a different email.

Using Tamr, the retailer can unify her profiles from both interactions and enrich them with additional attributes including behavioral data, regional preferences, and loyalty status.

During the same online visit, the retailer’s personalization engine can access Tamr’s API in real time and retrieve Maria’s unified profile. With her complete data, including in-store purchase history, the system can make a decision to display product recommendations tailored to Maria’s needs and interests, such as a canopy for the baby stroller she purchased previously.

Example: Customizing Supplier Outreach in B2B Retail

Personalization is just as critical for B2B relationships. For example, a procurement team wants to send targeted outreach to suppliers based on performance history, product fit, and geography. Using Tamr, the B2B retailer can:

• Connect supplier records across procurement, finance, and logistics
• Enrich them with historical and contextual data
• Make this information accessible instantly via API

This approach allows procurement to send personalized, meaningful messages that improve vendor relationships and drive strategic sourcing.

Operational Personalization, Not Just Reporting

Many companies attempt personalization using traditional business intelligence stacks. But to do so, they must run a batch job, create customer segments, and then export the segments to a campaign tool. This approach is outdated and inefficient. 

With Tamr, organizations can operationalize personalization efforts by tapping into unified and real-time master data, delivered directly into systems of engagement, and powered by AI with full transparency. This approach leads to higher conversion rates, stronger retention, and smarter decisions across both customer and supplier touchpoints.

Real-Time Personalization Starts with Clean, Unified Data

Real-time personalization is necessary in order to deliver the kinds of experiences customers expect. To do it well, retailers need more than an AI-based recommendation engine. They need AI-powered MDM that delivers accurate, timely, and accessible data in real time. 

Tamr enables that transformation—and with it, retail master data management that supports real-time, personalized experiences at scale. As a solutions engineer, I’ve seen organizations unlock incredible value by embedding Tamr into their personalization workflows. With clean, connected, and responsive data, every interaction becomes relevant and every decision more informed—leading to strong customer relationships and better business outcomes.