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Within your database lies a wealth of customer data ready for exploration. If you are excited about launching campaigns or crafting strategic reports, here is a key point to consider: the risk of encountering corrupt or duplicated data. Imagine the challenges posed by multiple records of the same person in your database. Sorting through and eliminating these duplicates is a sizable and error-prone task. However, machine learning offers a solution.
As businesses accumulate data from various sources and channels, the likelihood of encountering duplicate records increases significantly. These duplicates can stem from typos, variations in data entry, merging of databases, or even changes in customer information over time. Left unchecked, duplicate customer records can compromise customer relationships, lead to missed opportunities, and ultimately impact the bottom line.
Implementing Customer Deduplication using Machine Learning offers a multitude of benefits:
Our new software feature leverages machine learning and data science to systematically identify and manage duplicate customer records within Maxxton’s Customer Care module. By analysing the intricate patterns within your data, the system intelligently discerns true customer identity from coincidental similarities. Machine Learning unfolds in seven steps, providing a smooth path to unravel and optimise your data landscape.
1. Data Preprocessing
The initial step involves preprocessing raw customer data to eliminate inconsistencies, standardise formats, and improve data quality. Examples include converting text to uppercase, applying a consistent template for phone numbers, and removing special characters such as umlauts or diacritics. This process is essential as it establishes the groundwork for precise deduplication.
2. Feature Extraction
Key features are extracted from the customer records, such as names, addresses, phone numbers, and email addresses. These features serve as the basis for assessing customer similarity.
3. Similarity Calculation
Machine learning algorithms are utilised to calculate the similarity between pairs of customer records. The system examines a multitude of factors, assigning higher weights to features that are more reliable identifiers.
4. Rules Customisation
For enhanced customisation, the system utilises rules that can be adjusted for each client to specify conditions such as requiring a match in email addresses or stipulating a certain percentage match for last names (e.g., 90%). This ensures adaptability to meet specific business requirements. We recognise that typos may happen in front office or telephone bookings, including variations like Lynn, Linn, and Lin. Additionally, there could be country-specific special characters, for instance, Kreidestrasse – Kreidestr. – Kreidestraẞe. Our system calculates whether these instances represent typos or genuinely indicate a different name or street.
5. Threshold Determination
The calculated similarity scores are then compared against a predefined threshold. Records that exceed this threshold are flagged as potential duplicates.
6. Manual Review and Confirmation
While the system significantly reduces false positives, the flagged records are not automatically merged. Instead, they are presented to users for review. This ensures that no genuine customer data is inadvertently altered.
7. Continuous Learning
The system provides suggestions on whether to merge data, and users offer feedback by making changes or maintaining the status quo. This feedback contributes to improving the system’s recommendations. The machine learning model continuously learns and refines its algorithms, progressively enhancing accuracy over time.
Automatic Deduplication is a crucial solution for precision matching criteria, notably achieving a minimum threshold of 100%. This tool efficiently merges duplicates, automatically enhancing consolidated data with the latest and pertinent information. Prudently, you should impose restrictions on the number of automatic merges per day, allowing for a judicious review of outcomes. Exercise control over deduplication group sizes, reserving manual intervention for larger clusters. Notably, the tool ensures transparency through traceability, revealing merged customers and the selected data, thereby instilling order and clarity in the formal realm of data management.
An independent organisation specialising in user data, privacy, and GDPR assesses procedures, conducts audits, and has affirmed that the working methods comply with current guidelines. This ensures an effective and efficient work approach while responsibly handling personal data.
Our new Customer Deduplication using Machine Learning feature represents a significant advancement in data management and integrity. By harnessing the capabilities of data science, we’re empowering businesses to maintain a single, accurate view of their customers, enhancing operational efficiency and enabling data-driven decision-making. With this innovative tool at your disposal, you can focus on what matters most: building stronger customer relationships and driving sustainable growth, while upholding data privacy and compliance.
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