Case Studies Nahdi Medical - Customer Success Story

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	Nahdi Medical - Customer Success Story

Nahdi Medical - Customer Success Story

Technology Category	Analytics & Modeling - Natural Language Processing (NLP) Analytics & Modeling - Predictive Analytics Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries	Healthcare & Hospitals Pharmaceuticals
Applicable Functions	Business Operation Quality Assurance
Services	Data Science Services System Integration
Challenge	Nahdi Medical Company faced the challenge of analyzing a vast amount of feedback from both employees and patients, which included comments on various topics such as employee salaries, vacation time, online pharmacy orders, delivery & pricing, and appointments. The primary issue was finding an error-free, accurate system that could analyze all the comments from patient voice data and surveys in Arabic without losing the nuances of the language. Existing text analytics solutions that translated non-English text into English first for sentiment analysis were not effective, as they diluted the nuances of Arabic text. Nahdi needed a solution that could seamlessly understand Arabic and its dialects, classify and categorize valuable feedback, and provide insights for strategic and focused decision-making. Read More
About Customer	Nahdi Medical Company is a Jeddah-based clinic and pharmacy chain that operates a nationwide network in Saudi Arabia, covering 145 cities and villages. It is one of the fastest-growing companies in the region, providing advanced medical care in key areas such as radiology, oncology, cardiology, and pediatrics. In addition to its physical clinics and pharmacies, Nahdi offers online video consultation services for patients who are unable to visit doctors in person. The company is committed to delivering high-quality medical care and personalized attention to its patients and customers. Read More
Solution	Repustate developed a customized aspect-based sentiment model specifically for Nahdi Medical Company to capture both the voice of the employee (VoE) and voice of the customer (VoC) data. This solution was designed for the Arabic-speaking populace and could understand major Arabic dialects such as Gulf Peninsular, Egyptian, and Levantine Arabic. The model could analyze data from various sources, including videos, audio channels, patient vlogs, social media vlogs, comments, reviews, and surveys. The solution featured a dedicated Arabic part-of-speech tagger, Arabic lemmatizer, and Arabic-specific sentiment models. By leveraging Arabic natural language processing (NLP) and named entity recognition (NER), the model could identify topics and themes in the data for more granular sentiment analysis, leading to key business insights. Repustate's aspect-based sentiment analysis model provided Nahdi with the ability to semantically cluster and group data into several distinct categories. For each comment received, the model computed the sentiment and classified the comment into one or more aspects. Nahdi then processed the output into their larger data warehouse to visualize the results and take appropriate actions. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Natural Language Processing (NLP)

Analytics & Modeling - Predictive Analytics

Application Infrastructure & Middleware - Data Exchange & Integration

Applicable Industries

Healthcare & Hospitals

Pharmaceuticals

Applicable Functions

Business Operation

Quality Assurance

Services

Data Science Services

System Integration

Challenge

Nahdi Medical Company faced the challenge of analyzing a vast amount of feedback from both employees and patients, which included comments on various topics such as employee salaries, vacation time, online pharmacy orders, delivery & pricing, and appointments. The primary issue was finding an error-free, accurate system that could analyze all the comments from patient voice data and surveys in Arabic without losing the nuances of the language. Existing text analytics solutions that translated non-English text into English first for sentiment analysis were not effective, as they diluted the nuances of Arabic text. Nahdi needed a solution that could seamlessly understand Arabic and its dialects, classify and categorize valuable feedback, and provide insights for strategic and focused decision-making.

About Customer

Nahdi Medical Company is a Jeddah-based clinic and pharmacy chain that operates a nationwide network in Saudi Arabia, covering 145 cities and villages. It is one of the fastest-growing companies in the region, providing advanced medical care in key areas such as radiology, oncology, cardiology, and pediatrics. In addition to its physical clinics and pharmacies, Nahdi offers online video consultation services for patients who are unable to visit doctors in person. The company is committed to delivering high-quality medical care and personalized attention to its patients and customers.

Solution

Repustate developed a customized aspect-based sentiment model specifically for Nahdi Medical Company to capture both the voice of the employee (VoE) and voice of the customer (VoC) data. This solution was designed for the Arabic-speaking populace and could understand major Arabic dialects such as Gulf Peninsular, Egyptian, and Levantine Arabic. The model could analyze data from various sources, including videos, audio channels, patient vlogs, social media vlogs, comments, reviews, and surveys. The solution featured a dedicated Arabic part-of-speech tagger, Arabic lemmatizer, and Arabic-specific sentiment models. By leveraging Arabic natural language processing (NLP) and named entity recognition (NER), the model could identify topics and themes in the data for more granular sentiment analysis, leading to key business insights. Repustate's aspect-based sentiment analysis model provided Nahdi with the ability to semantically cluster and group data into several distinct categories. For each comment received, the model computed the sentiment and classified the comment into one or more aspects. Nahdi then processed the output into their larger data warehouse to visualize the results and take appropriate actions.

Impact #1	With Repustate’s Arabic aspect-based sentiment analysis model, Nahdi employees felt that their voices were heard, as the management took effective measures to meet employee satisfaction goals.
Impact #2	The client gained direct insight into which areas, clinics, and pharmacy locations needed attention, allowing for improved medical care and personalized attention for patients and their caregivers.
Impact #3	The automated sentiment analysis model enabled Nahdi to efficiently analyze a large volume of feedback without manual intervention, saving time and reducing costs.

Impact #1

With Repustate’s Arabic aspect-based sentiment analysis model, Nahdi employees felt that their voices were heard, as the management took effective measures to meet employee satisfaction goals.

Impact #2

The client gained direct insight into which areas, clinics, and pharmacy locations needed attention, allowing for improved medical care and personalized attention for patients and their caregivers.

Impact #3

The automated sentiment analysis model enabled Nahdi to efficiently analyze a large volume of feedback without manual intervention, saving time and reducing costs.

Benefit #1	Nahdi Medical Company operates a network covering 145 cities and villages in Saudi Arabia.
Benefit #2	The aspect-based sentiment analysis model can understand major Arabic dialects such as Gulf Peninsular, Egyptian, and Levantine Arabic.

Benefit #1

Nahdi Medical Company operates a network covering 145 cities and villages in Saudi Arabia.

Benefit #2

The aspect-based sentiment analysis model can understand major Arabic dialects such as Gulf Peninsular, Egyptian, and Levantine Arabic.

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Overview

Nahdi Medical - Customer Success Story

Operational Impact

Quantitative Benefit