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GDPR and CCPA hold firms accountable for handling customer information responsibly. Inaccurate information results in:
- Compliance lapses
- Legal penalties
- Reputation losses
The aim of precision assists firms in staying compliant and still benefitting from the trust of their customers. The year 2023 witnessed a groundbreaking GDPR fine surpassing €1.2 billion to Meta (formerly known as Facebook). This was due to inaccurate customer data that was not compliant with GDPR regulations.
Reliable third-party data providers and first-party data collection methods (such as surveying consumers and following websites) give companies access to high-quality, validated data. Poor quality external data can introduce errors, so it is critical to carefully vet sources.
Implement Strong Data Governance
Firms must create data create a simple friendly script
governance policies such that their data is consistent across all their marketing touch points. This includes establishing standards on gathering, storing, validating, and protecting the data.
Key steps:
- Appoint data stewards to oversee data quality programs.
- Create data gathering and maintenance guidelines that are clearly defined.
- Have strict controls for access to prevent unauthorized changes.
Governance that is well-defined enables best practice implementation by each group of employees to reduce error.
Routine audits help rectify customer database errors and detect them. Duplicate elimination, removal of old data, and verification of contact data improve data quality overall. Best practices:
- Schedule routine data quality checks on a monthly or quarterly basis.
- Use automated software to locate and eliminate duplicate records.
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- Use AI-based data validation software to monitor data accuracy continuously.
- Use machine learning algorithms to foresee and rectify data discrepancies.
- Use natural language processing (NLP) to extract insights from unstructured data sources.Verify email addresses, telephone numbers, and customer data.
Use Automation and AI
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platforms and artificial intelligence enable real-time validation of data. Predictive analytics based on AI also provide deeper insights to enable better-informed decisions.Steps to implement:
Departmental siloed data can lead to inconsistencies. Businesses have to combine marketing, sales, and customer service data to create one correct picture of the customer. Collaborative working guarantees all the teams are working with the same high-quality data.
Integration strategies:
- Institute customer data platforms (CDPs) to unify customer experiences at touchpoints.
- Encourage cross-functional collaboration on data validation processes.
- Establish firm-wide standards for data sharing and updates.