Tamr Insights
Tamr Insights
AI-native MDM
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Updated
| Published
July 23, 2024

Customer Data Cleansing

Customer Data Cleansing

Summary

  • Clean customer data is crucial for business success
  • AI-native tools like Tamr make customer data cleansing faster and easier
  • High-quality customer data allows for tailored marketing campaigns and personalized experiences
  • Prioritizing cleaning customer data leads to increased revenue and reduced costs

Clean customer data is critical for modern businesses. Without it, marketing campaigns fail, customer experiences suffer, and revenues decline.

However, ensuring that your customer data remains accurate, consistent, and complete is no small feat. According to ZoomInfo, 94% of businesses suspect that their customer and prospect data is inaccurate, while a study by MarketingSherpa reports that 25-30% of contact data is considered incorrect each year.

Why Customer Data Cleansing Matters

Cleaning customer data requires organizations to identify, correct, and remove errors, inconsistencies, duplicates, and inaccuracies in order to improve the data's quality, integrity, and consistency. It's hard work, but work that is absolutely critical to your business

When companies have high-quality customer data, they can:

  • Tailor marketing campaigns based on specific customer needs
  • Deliver personalized experiences that drive customer loyalty
  • Spot new opportunities to grow revenue
  • Streamline operations and reduce costs
  • Safeguard the brand from reputational harm

How to Clean Customer Data

Ensuring that your customer data remains clean, accurate, and complete takes time. The good news, however, is that new, AI-native tools like the ones Tamr provides make the processes of cleaning and consolidating customer data much faster and easier than in the past. As a result, you can achieve a true, Customer 360 view across disparate systems and sources in five simple steps:

  1. Connect to your sources, including cloud object stores and data warehouses. For example, Tamr can read and write data from sources including GCS, Synapse, S3, BigQuery, or Snowflake. 
  2. Configure a data product template with industry-specific schemas and enrichment services. Map the attributes from each source into the unified schema. From there, Tamr will recommend a value for use in the golden record.
  3. Enrich your data using trusted, third-party sources. Choose the providers you wish to use to enrich firmographic information from the ones for which your company has a license. In addition, Tamr's out-of-the-box enrichment capabilities include country code normalization, phone number validation, global address validation, email address validation, and company name normalization.
  4. Curate your results by engaging humans for stewardship. Tamr's AI performs the majority of the work, achieving over 90% accuracy and scalability when creating a golden record. However, it's important to keep humans in the loop by engaging data stewards to review and edit the mastered results, ensuring any remaining gaps are addressed using Tamr's curation capabilities.
  5. Publish to downstream systems and enable Tamr's realtime APIs to make your golden records accessible across your organization in support of analytical and operational use cases.

Why You Should Prioritize Cleaning Customer Data

Organizations that invest in improving the quality of their customer data see real results. Take Western Union as an example.

Western Union had a goal: to consolidate records from online and retail channels to form a 360-degree customer view. Historically, they relied on traditional, rules-based master data management to clean their data. However, when the company tried to connect large amounts of highly variable data at scale, this approach quickly fell apart. They found that traditional approaches helped them to clean 80%-90% of their data, but that was no longer enough to impact the bottom line. 

Western Union knew they needed a better, more modern approach that could handle the scale of their data. Using Tamr, Western Union cleaned their customer data, deduplicating and enriching 375 million customer records in a matter of months (not years). As a result, agents gained access to a holistic, 360° view of the customer which allowed them to identify top customers, tailor their experiences, and reduce marketing spend.

Read the full case study to learn more about Western Union’s journey to deliver better customer experiences using clean, holistic trustworthy customer data. And, if you want to see Tamr's AI-powered data mastering solution in action, watch this 12-minute video.