Financial services | Case study

Know your customers, drive your growth


Trusted records help Western Union deliver better customer experiences to improve the bottom line




To better identify and serve high-value customers, Western Union needed to match and deduplicate hundreds of millions of records as well as enrich them to ensure that email domains and phone numbers were accurate.



In a few months, Western Union deduplicated and enriched 375 million customer records and increased the number of customer record matches by 8%. Support agents now have a person’s full transaction history and Western Union better understands which customers are driving the most revenue.

You’re on Western Union’s website trying to send money and having trouble with the transaction. You head to the site’s chat function and get help from the support agent – who not only solves the problem but also offers a discount on the money transfer fee since you’re a high-value customer.

Providing this level of service requires knowing your customers and the full history between them and an organization. To obtain these complete customer views and make sure different systems records are “talking” to each other, Western Union depends on Data Mastering from Tamr.


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“We want to better understand who we're dealing with

and their relationship to Western Union. We brought in Tamr because we could not solve that problem using our existing technology, which was primarily simple and deterministic matching”

Harveer Singh

Western Union’s Chief Data Architect


Why knowing 90% of your customers isn’t enough

To better serve customers, Western Union is transitioning from a retail-first organization – in the U.S. alone there are 57,000 Western Union agent locations – to a digital-first one. As part of this transformation, the financial services company is integrating records from digital and retail channels to form 360-degree customer views.  

But when 1.2 billion people have used your services to send and receive money in the last decade and 375 million in the past two years via multiple channels (retail stores, a website, point of sales transactions, Interactive Voice Response, and a mobile app) – data volume, variety and quality issues make obtaining those single customer views challenging. 

“You may have five profiles but you are the same person. And I should be able to give you the same service whether you're using the mobile app, the retail channel or the website. We want to bring all of that information together and offer a great omni-channel engagement,” said Harveer Singh, Chief Data Architect, Western Union. But without accurate customer data, providing an elevated level of service ”was absolutely impossible.”

Western Union did have some insight into their customers. They used a 10-year-old rules-based master data management tool that cleaned around 80% to 90% of the customer data. However, that wasn’t enough to impact the bottom line.

“You make a real difference in that 10% to 20% zone. That’s when you can reduce fraud or increase revenue. Our product has remained the same so to have a business result, we have to look at how much money we can make from those customers or how much risk we can reduce,” he said.

Conventional rules-based systems can be effective on a small scale, relying on human-built rules logic to generate master records. However, rules quickly fall apart when tasked with connecting large amounts of highly variable data (like millions of customer records) at scale.

“To master 375 million records using the traditional way of writing rules, we’d be doing it for 10 years and probably still wouldn’t be able to do it,” Singh said.

Adopting a modern approach to MDM

To adopt a modern approach to MDM, Western Union deployed Tamr Mastering. With Tamr’s next-generation solution, machine learning handles a majority of the manual data preparation work. Humans are kept in the loop to provide feedback and boost confidence in the machine learning model.

"The way mastering is done, our data analysts and data scientists were blown away by it. Most of their time was spent writing rules and here they were building experiences for our customers while bringing Tamr along that journey to build that heart of our data foundation,” said Singh.

Western Union also uses Tamr to enrich records with external data to standardize mailing address formats, validate phone numbers, and check that email addresses have valid domains. Combining data enrichment with data mastering means that not only are data sources automatically cleaned, they are also enhanced with valuable commercial information while avoiding the extremely time-consuming and manual work that goes into incorporating external data.

Each customer record is also assigned a Tamr ID, a persistent identifier that serves as a reference to an entity across multiple data sources assigned by Tamr. The customer records created by Tamr are loaded into the Snowflake Data Cloud, providing Western Union with a single source of truth to power both analytical and operational applications.

“Tamr is becoming the heartbeat of our organization because that Tamr ID gets propagated to downstream systems as well as front end systems,” Singh said. “Having customer records that we know are right helps us get more value from Snowflake.”

Western Union runs Tamr on its AWS instance, allowing them to take advantage of the platform’s flexibility and scalability. This combination of using AWS, Snowflake and Tamr together fits with Western Union’s plan of using modern technologies to better serve customers.

“We don’t want to be in the hardware business. We want to be in the business of building products for customers. With AWS and Snowflake, we have the flexibility and scalability of the cloud and less hardware costs and administration. Tamr offers machine learning so we don’t spend time managing a lot of rules. Using them all together helps us with our ultimate goal of understanding our relationships with our customers,” he said. 

Main challenges:

  • Matching, deduplicating hundred of millions of customer records using a rules-based MDM solution
  • Developing 360-degree customer views to boost user experience
  • Overcoming data quality issues like inaccurate email domains and mailing addresses

Tamr Results:

  • Increased customer record matches by 6% to 8% for improved marketing campaigns, greater insight into fraudulent activities
  • Deduplicated, enriched 375 million customer records in months
  • Provide the Snowflake Data Cloud with trusted data to power downstream applications
  • Enable agents to offer superior customer experience by providing them with a person’s full transaction history

“To master 375 million records using the traditional way of writing rules, we’d be doing it for 10 years and probably still wouldn’t be able to do it.”

- Harveer Singh

Delivering better customer experiences for bottom line growth

One front end system that leverages the Tamr ID is the one used by the support agents who field questions submitted via the chat function on Western Union’s website. The Tamr ID  provides the agents with the customer’s full transaction history. Since people sometimes have multiple accounts, viewing only one of them won’t show if a person is a top customer.

“We’re popping in that Tamr ID and showing the agents the customer’s full relationship with us and total lifetime value. The account they’re calling about could have low profitability, but they could have other accounts that are highly profitable. We need to see all of the person’s accounts and treat them accordingly,” Singh said. 

Tamr-mastered records are also being used in an initiative around communicating with customers using Salesforce Marketing Cloud. A feature of this marketing automation software is the ability to send messages by text and email as well as in Western Union’s mobile app. Successfully reaching a customer with the right message on the right channel requires accurate email addresses and phone numbers, Singh said, adding that landline numbers often end up in the mobile number field.

With Tamr, Western Union increased customer matches from their retail and online databases by six to eight percent. While that figure may not seem significant, Singh points out that the business impact is substantial given the size of the customer base.

“For an active customer base of 200 million people, this is huge. I now know a lot more about that six to eight percent of customers. I know these are my real customers. I spend less on marketing because I’m not reaching out to the same person over and over again. These could be the ones committing fraud or spending the most money. This makes a very big difference between our top line revenue and bottom line growth.”

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