October 1, 2024

CRM data quality improvement through automated duplicate detection

Leveraging Machine Learning for streamlined database integrity

CRM data quality improvement through automated duplicate detection

At a glance

Challenge

In the realm of Customer Relationship Management (CRM), maintaining the accuracy of client registration data is essential for successful retention campaigns. The challenge arises from a decentralized process that draws data from various sources, increasing the risk of duplicate records and data inconsistencies. Existing tools focus on exact matches of unique personal data, but this approach overlooks other critical details like invalid entries or near-matches that may indicate errors. To address this, we partnered with a car retailing group to establish a unified client database, enhancing the accuracy and usability of the CRM for streamlined marketing and sales efforts.

Solution

The strategy to manage duplicate client records in CRM involves four steps:

  1. Selecting potential duplicate pairs based on shared personal data like phone numbers and emails.
  2. Classifying duplicates using similarity scores to assess character and phonetic similarities.
  3. Creating duplicate groups, ensuring each group is represented by a single contact.
  4. Consolidating client information by adopting the most reliable and updated data for the chosen contact in each group.

Results

Our approach to managing duplicate records used advanced machine learning models to detect duplicates, ensuring practical implementation through stakeholder collaboration. We consolidated the remaining contacts to retain the most trustworthy data and recommended a standardized format for invalid phone numbers and emails. Additionally, we proposed a robust unique key system to minimize duplications, achieving a 12% reduction in duplicate contacts within the CRM database.

Challenge

In the realm of Customer Relationship Management (CRM), maintaining the accuracy of client registration data is essential for successful retention campaigns. The challenge arises from a decentralized process that draws data from various sources, increasing the risk of duplicate records and data inconsistencies. Existing tools focus on exact matches of unique personal data, but this approach overlooks other critical details like invalid entries or near-matches that may indicate errors. To address this, we partnered with a car retailing group to establish a unified client database, enhancing the accuracy and usability of the CRM for streamlined marketing and sales efforts.

Approach

Solution

The strategy to manage duplicate client records in CRM involves four steps:

  1. Selecting potential duplicate pairs based on shared personal data like phone numbers and emails.
  2. Classifying duplicates using similarity scores to assess character and phonetic similarities.
  3. Creating duplicate groups, ensuring each group is represented by a single contact.
  4. Consolidating client information by adopting the most reliable and updated data for the chosen contact in each group.

Results

Our approach to managing duplicate records used advanced machine learning models to detect duplicates, ensuring practical implementation through stakeholder collaboration. We consolidated the remaining contacts to retain the most trustworthy data and recommended a standardized format for invalid phone numbers and emails. Additionally, we proposed a robust unique key system to minimize duplications, achieving a 12% reduction in duplicate contacts within the CRM database.

Our
AI-generated
summary

Our AI-generated summary

Our AI-generated summary

In the domain of Customer Relationship Management (CRM), ensuring the reliability and quality of client registration information stands as a critical factor for the success of targeted retention campaigns. The challenge is exacerbated when a decentralized registration process draws data from diverse sources, leading to an increased susceptibility to duplicated records and insertion errors.

Existing tools support detecting duplicate entries – however that process often relies on an exact match of a particular key, comprising personal unique data such as phone numbers and email information. This process overlooks other essential details – the presence of invalid information or characters in certain fields, or similarities (not exact matches) that almost certainly are mistyping errors, allowing for potential inconsistencies in the database.

Our AI-generated summary

Our AI-generated summary

Recognizing the urgency to rectify this pervasive issue, we partnered with a car retailing group in a project whose primary goal is to establish a unified client database, while enhancing the overall accuracy and usability of the CRM. Aiming to streamline direct marketing actions and sales efforts, the project seeks to implement a more efficient and automated approach to rectify duplicates, acknowledging the imperative role data quality plays in targeted retention campaigns.

The strategy followed can be summarized in four steps:

  1. Selecting potential duplicate contact pairs: Identify pairs of contacts that may be duplicates based on shared personal information such as phone numbers, emails, or ownership of the same vehicle.
  2. Classifying duplicates using similarity scores: Assess the similarity between contact pairs through metrics like character and phonetic similarity. Use these scores to train a classification model, determining the probability of duplication for each pair.
  3. Creating duplicate groups: Group identified duplicate pairs. Each group should ultimately be represented by a single contact entry in the database.
  4. Consolidating client information: For the chosen contact in each group that will remain in the database, consolidate the client information by adopting the most reliable and up-to-date data available.

Our approach to addressing duplicated records involved leveraging sophisticated machine learning models to detect duplicates. Through meticulous stakeholder discussions, we ensured a seamless transition from theoretical frameworks to real-world applications, bridging the gap between assumptions and practical implementation.

The manual, one-to-one correction of duplicated contacts proves to be a tedious and time-consuming task.

As a result of these efforts, we were able to detect and remove 12% duplicate contacts in the CRM database.

The remaining contacts in the database were consolidated, to keep the most trustworthy data for each field. We also proposed a detailed process for converting invalid phone numbers and emails into a standardized format, strengthening the database against potential inconsistencies. Moreover, we recommended the adoption of a more robust unique key system, minimizing the risk of duplications and ensuring long-term database integrity.

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