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Business Use Case Study: Interzoid's Cloud-Native Data Matching Solution for Call Centers


Executive Summary

This case study explores the integration of Interzoid's Generative-AI powered Individual Name Matching API (and other available, optional matching APIs) into a call center environment. The primary issue addressed is the proliferation of duplicate/redundant call center records that occurs due to name variations, misspellings, and other data inconsistencies. This leads to significant operational inefficiencies, including wasted effort, disjointed communication, incomplete analytical views, and unnecessary costs. By adopting Interzoid's innovative Generative-AI powered matching capabilities, call centers can significantly reduce the occurrence of duplicate records, streamline operations, decrease costs, and enhance customer experience.

Background

In a bustling call center, handling hundreds or thousands of calls daily, data integrity is paramount. Representatives often face the challenge of accurately identifying callers, a task complicated by name variations, name misspellings, and other data inconsistencies provided by a caller and/or present in the call center database. Consequently, this frequently leads to the creation of multiple records for the same individual, resulting in operational inefficiencies, inaccurate analytics, miscommunication, and a disjointed customer experience. Recognizing the need for an advanced, yet easy-to-incorporate solution to this pervasive issue, Interzoid's Generative-AI powered Individual Name Matching APIs emerge as a transformative solution for call centers.

The Problem

Duplicate records in call center databases due to name variations and inconsistencies pose several problems:

• Effort Duplication: Reps spend unnecessary time managing and reconciling duplicate records.
• Disjointed Communication: Multiple records for the same individual lead to fragmented customer interactions.
• Analytical Challenges: A full view of customer interactions is obscured, hindering insightful analysis.
• Increased Costs: Resources are wasted in managing and storing redundant data.

 There is an annual estimated loss in the US economy of over $3 trillion per year due to poor data quality.

The Solution

Interzoid's solution leverages Generative AI, Machine Learning techniques, specialized algorithms, and extensive knowledge bases to create a canonical similarity key for names data. This generated string-based key remains consistent across similar permutations, misspellings, and other inconsistencies in all entries of name data, either stored in the call center database or collected by reps at the time of data entry. The process involves:

• Key Generation: The API is used to generate a similarity key for each record in the database, ensuring a unique identifier for each individual regardless of data inconsistencies.
• Real-time Matching: Upon receiving a call, the call center inputs the caller's information into the call center application which in turn calls the Interzoid API, generating a similarity key to search the database for records containing the same similarity key, significantly reducing the chances of creating duplicate records.

Implementation

The integration of Interzoid's APIs into the call center's software package is straightforward and easy. The APIs are available as Cloud APIs on a per-call usage basis, a batch/dataset Cloud Data Connect product with database and file connectivity, or as an AWS Machine Image for EC2, enabling unlimited usage within a customer's Virtual Private Cloud (VPC), offering maximum flexibility and scalability.

 Interzoid is using novel techniques such as Generative AI and Machine Learning to significantly enhance and improve quality, usabilty, and value of an organization's data assets with a next-generation approach.

Value Proposition

The adoption of Interzoid's Individual Name Matching API presents numerous benefits:

• Reduced Duplicate Records: Significantly lowers the incidence of duplicate customer records.
• Enhanced Customer Experience: Streamlines customer interaction by providing a unified view of the customer's history.
• Operational Efficiency: Saves time and resources by eliminating the need to manage duplicate records.
• Improved Analytics: Offers a complete and accurate dataset for analytics, enabling better decision-making.
• Cost-Effectiveness: The pay-as-you-go pricing model makes it easy to get started and allows for efficient cost management based on need, with options suitable for businesses of all sizes.

Opportunities

Employing Interzoid's matching technology opens up several opportunities for businesses using call centers:

• Personalized Customer Service: A unified customer view enables personalized interactions, improving customer satisfaction and loyalty.
• Strategic Insights: Accurate data analysis leads to informed strategic decisions, enhancing business operations and growth.
• Operational Scalability: The solution easily scales with the business, supporting growth without the proportional increase in data management challenges.
• Competitive Advantage: Streamlined operations and enhanced customer service position the business ahead of competitors.

Conclusion

Interzoid's Generative-AI powered Individual Name Matching API provides a powerful solution to a common yet challenging problem faced by call centers. By ensuring the integrity of customer data through advanced matching techniques, call centers can significantly improve operational efficiency, customer satisfaction, and accurate analytical capabilities and insights. This case study demonstrates the tangible benefits and opportunities offered by Interzoid's name matching technology, making it an indispensable product for any call center looking to optimize its operations and deliver superior customer service.


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