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Zaxby's, a fast-food franchise, was receiving approximately 10,000 customer reviews per week via email. The process of manually extracting data from these reviews into an Excel spreadsheet and creating reports using pivot tables was extremely time-consuming and inefficient. This manual process hindered Zaxby's ability to quickly identify and address customer concerns and operational issues across their franchise locations.
Zaxby's recognized the need to automate their customer feedback analysis and reporting process. They sought a solution that could efficiently process the large volume of customer reviews, provide actionable insights, and visualize trends across their franchise locations. By leveraging AI and machine learning technologies, Zaxby's aimed to transform their weekly data exports into a live dashboard of survey insights.
Add Value Machine (AVM) developed a solution for Zaxby's that utilized Large Language Models (LLMs) hosted in AWS Bedrock to analyze customer data. The solution included:
1. Sentiment analysis to gauge overall customer satisfaction and identify locations with negative feedback.
2. Topic modeling to surface recurring issues and themes in customer reviews.
3. Interactive maps to visualize trends and performance across franchise locations.
4. A self-contained, fully-functioning dashboard application built using R Shiny and Python scripts.
The dashboard can ingest past and future weekly survey exports and present results over any desired timeline, enabling Zaxby's to quickly identify and address areas of concern.
AVM's AI-powered customer feedback analysis solution addresses a common challenge faced by many businesses in the food and hospitality industry. By automating the process of analyzing large volumes of customer reviews, businesses can:
1. Save time and resources previously spent on manual data analysis.
2. Quickly identify and address customer concerns and operational issues.
3. Monitor trends and performance across multiple locations or franchises.
4. Make data-driven decisions to improve customer satisfaction and operational efficiency.
AVM's solution demonstrates the potential of AI and machine learning technologies to transform customer feedback analysis and reporting processes in the industry.
For Zaxby's, AVM's AI-powered customer feedback analysis solution provides several key benefits:
1. Increased efficiency in analyzing and reporting on large volumes of customer reviews.
2. Timely identification of struggling locations based on negative feedback, allowing for swift corrective action.
3. Improved ability to surface recurring issues through topic analysis, enabling targeted operational improvements.
4. Enhanced visualization of trends as operational changes are implemented, facilitating data-driven decision-making.
These benefits ultimately lead to improved customer satisfaction, as Zaxby's can more effectively address customer concerns and optimize their operations based on the insights provided by the AI-powered dashboard.
For Zaxby's customers, the key benefits include:
1. Faster resolution of their concerns and complaints, as Zaxby's can quickly identify and address issues raised in customer reviews.
2. Improved overall customer experience, as Zaxby's can make data-driven operational improvements based on customer feedback.
In summary, AVM's AI-powered customer feedback analysis solution, built using LLMs hosted in AWS Bedrock, demonstrates the potential of AI and machine learning technologies to transform customer feedback analysis and reporting processes in the food and hospitality industry. The solution provides significant benefits to both Zaxby's and its customers by enabling more efficient, data-driven decision-making and ultimately improving the overall customer experience.
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