Prevent Duplicate Data from Harming Your Business—and Your AI Making It Worse

Duplicate records in the Salesforce^® platform can create significant challenges for a business, impacting efficiency, decision-making, and customer relationships. When AI tools, such as Agentforce or other solutions, are integrated for customer acquisition and support, these issues can be amplified.

Below is a breakdown of the dangers and how AI worsens them.

What Are the Dangers of Duplicate Data in Salesforce?

1. Inaccurate Customer Insights:

• Duplicate records lead to fragmented customer profiles, making it hard to get a unified view of customer behavior, preferences, or history.
• Example: Two records for the same customer might show conflicting purchase histories, skewing analytics and reporting.

2. Operational Inefficiency:

• Staff waste time navigating duplicate records, reconciling discrepancies, or contacting the same customer multiple times unnecessarily.
• This increases labor costs and reduces productivity.

3. Poor Customer Experience:

• Customers may receive duplicate or erroneous communications (e.g., emails or calls), which can annoy them and damage brand perception.
• Inconsistent data might lead to mismatched offers or incorrect support responses, eroding trust.

4. Flawed Decision-Making:

• Duplicate data distorts metrics like customer lifetime value, churn rates, or campaign performance, leading to misguided strategies.
• Example: Overestimating the customer base due to duplicates can result in inflated revenue forecasts.

5. Compliance and Security Risks:

• Duplicate records increase the risk of mishandling personal data, potentially violating regulations like GDPR or CCPA.
• Redundant storage of sensitive information can also heighten exposure to data breaches.

How Do AI Tools Amplify The Problems Caused by Duplicate Data?

AI tools for customer acquisition (e.g., predictive lead scoring, targeted marketing) and customer support (e.g., chatbots, sentiment analysis) rely heavily on clean, accurate data. Duplicate data introduces noise that undermines their effectiveness and compounds the problems.

1. Skewed AI Predictions and Targeting:

• Customer Acquisition: AI models for lead scoring or audience segmentation may treat duplicate records as unique customers, leading to inaccurate predictions. For example, a single high-value customer with multiple records might be misclassified, causing the AI to prioritize low-value leads instead.
• Negative Outcomes: AI amplifies this by scaling erroneous targeting across campaigns, wasting ad budgets on redundant or incorrect outreach.

2. Degraded Customer Support Quality:

• Customer Support: AI-driven chatbots or ticketing systems may pull conflicting information from duplicate records, resulting in incorrect responses or failure to resolve issues. For instance, a chatbot might not recognize a customer’s prior interaction if it’s logged under a duplicate profile.
• Negative Outcomes: AI’s automation can propagate these errors at scale, frustrating customers and increasing escalations to human agents.

3. Overloading AI Processing:

• Duplicate data increases the volume of records AI tools must process, raising computational costs and slowing performance. Storing excess or unneeded data causes the same problems.
• Negative Outcomes: In real-time applications (e.g., dynamic pricing or live chat), delays caused by sifting through duplicates can disrupt customer interactions.

4. Amplified Bias in AI Models:

• Duplicates can overrepresent certain customers or behaviors in training data, introducing bias into AI models. For example, if a single customer’s purchases are recorded multiple times, the AI might overprioritize similar profiles.
• Negative Outcomes: Biased models perpetuate flawed strategies, such as targeting the wrong audience or misallocating resources.

5. Compounded Compliance Issues:

• AI tools processing duplicate data may inadvertently mishandle personal information, increasing the risk of regulatory penalties.
• Negative Outcomes: AI’s ability to rapidly process and share data can spread compliance errors across systems, making violations harder to contain.

How Can You Mitigate the Risks of Duplicate Records and Improve Data?

To address these dangers, businesses should:

• Implement Data Deduplication: Use tools to identify and merge duplicate records based on unique identifiers (e.g., email, phone number).
• Standardize Data Entry: Enforce consistent formats and validation rules to prevent duplicates at the source.
• Monitor Data Quality: Regularly audit Salesforce data and AI outputs to catch inconsistencies early. Streamline the entire data set by avoiding the storage of excess or unneeded data and instituting a data deletion strategy.
• Integrate Systems: Ensure CRM, marketing, and support platforms share a single source of truth to avoid siloed duplicates.

Data Quality Helper App Elevates Use of Salesforce and AI

The free app, Data Quality Helper, is one of three different Admin Helper Apps from Passage Technology that can help with improving data quality across the organization (also Rollup Helper and Lookup Helper). Data Quality Helper excels at customizable data deduplication and data validation, allowing Admins to create customizable rules that empower Salesforce users to standardize data entry and clean up existing data. The app also enables optimal data storage management with its archival and deletion capabilities.

By elevating and maintaining data quality, businesses can maximize the return on CRM investment and the effectiveness of AI tools, improve customer experiences, and make informed strategic decisions.