Case Study: AI Assistant Integration for Email Sorting and Response Sentiment Analysis

In today's digital age, where response speed and personalized communication are key to success, many companies face the challenge of managing an enormous volume of emails. Imagine hundreds, even thousands, of messages arriving in your inbox daily – customer inquiries, complaints, business offers, internal communication. Manually sorting these emails is time-consuming, prone to errors, and often leads to staff overload, which ultimately impacts customer satisfaction and operational efficiency. In Slovakia, as elsewhere, companies are striving to find innovative solutions to improve this situation.

Our company, ABRA Consulting, recently met with one of our long-term clients, a medium-sized e-commerce firm with an extensive customer base, who was looking for a way to optimize their email communication. Their main challenge was managing the huge volume of incoming emails while needing to accurately understand the mood and tone of customer responses to react flexibly. This article presents a detailed case study describing the integration of a specialized AI assistant for automatic email sorting and sentiment analysis, and the transformative results we achieved. As academic papers, such as those from TUZVO focusing on applied languages and AI utilization, also state, the potential of artificial intelligence in language processing is immense and opens doors to new possibilities in communication and data analysis.

Why Email Sorting and Sentiment Analysis Are Crucial

Email remains one of the most important communication channels for businesses of all sizes. From simple inquiries to complex complaints, every email represents a potential opportunity to build a customer relationship or, conversely, a risk of losing their trust. However, traditional email processing methods are often inefficient.

Challenges of Manual Email Sorting and Processing

• Time-consuming: Employees spend hours daily manually going through emails, categorizing them, and forwarding them. According to our client's internal data, up to 30% of customer support working hours were dedicated solely to email sorting and initial contact.
• High error rate: The human factor introduces the risk of errors. An important email can be overlooked, miscategorized, or sent to the wrong department, leading to delayed responses and customer frustration.
• Insufficient analysis: Without a systematic approach, it is almost impossible to gain deep insights into what customers truly feel or think. Sentiment data is scattered and difficult to quantify.
• Poor scalability: As the company grows and communication volume increases, problems only worsen. Hiring additional staff for this task is costly and may not bring the expected efficiency.
• Employee overload: Repetitive and monotonous tasks lead to burnout and reduce the motivation of the customer support team.

Why Is Sentiment Analysis an Indispensable Part of Modern Business?

Sentiment analysis, also known as "opinion mining," is a process that uses machine learning algorithms to identify and extract subjective information from source text, determining whether a message is positive, negative, or neutral. For a company, this means much more than just basic categorization.

• Urgency identification: Negatively worded messages (e.g., complaints, grievances) can be immediately marked as priority, allowing for faster response and minimizing potential reputational damage.
• Product and service improvement: Aggregated sentiment data from thousands of emails provides valuable insights into product, service, or process weaknesses that need improvement.
• Personalized communication: Understanding the customer's mood allows for tailoring the tone and content of the response, thereby increasing satisfaction and loyalty.
• Proactive relationship management: Companies can identify dissatisfied customers before their problems escalate, offering solutions even before they write a negative review.
• Milestones in marketing campaigns: Sentiment analysis in responses to marketing emails can accurately determine how campaigns are perceived and whether set goals are being met. As we mentioned in the context of UCM and online rules, email activities are extensive, and their optimization has a direct impact on a company's profile.

Our Solution: AI Assistant Integration

Given these challenges, we proposed a comprehensive solution to our client: the integration of an AI assistant specifically designed for automatic incoming email sorting and content sentiment analysis. The goal was not only to reduce manual labor but also to provide deeper, actionable insights from customer communication.

Architecture and Technologies

The core of our solution was a modular system built on modern machine learning and natural language processing (NLP) technologies.

1. Email Connector: A module for secure connection to the client's existing email inboxes (e.g., via IMAP/POP3 or API for G Suite/Microsoft 365).
2. Text Preprocessing Module: This step is crucial for any NLP task. It includes:
   • Clean Text Extraction: Removal of HTML tags, images, and other irrelevant elements.
   • Text Normalization: Conversion of all letters to lowercase, removal of punctuation (if not relevant for sentiment), diacritics, numbers, and other special characters.
   • Tokenization and Lemmatization: Splitting text into individual words (tokens) and reducing them to their base form (lemma), which aids in pattern generalization.
3. Classification Model (Email Sorting): For email sorting, we used a model based on deep neural networks (e.g., BERT, DistilBERT, or a specialized model for the Slovak language if available), trained on a large corpus of data. The model was capable of categorizing emails into predefined categories, such as:
   • Product Inquiry
   • Complaint/Return
   • Technical Support
   • Business Offer
   • Billing Inquiry
   • General Inquiry
   • SPAM
4. Sentiment Analysis Model: Simultaneously with sorting, the email was also analyzed for sentiment. We used a combination of a lexical approach (a dictionary of words with assigned polarities) and machine learning (a trained classifier for the Slovak language) that could identify sentiment as:
   • Positive
   • Negative
   • Neutral
   • Mixed (in the case of complex messages) For this purpose, we also utilized the benefits of research in natural language processing for Slovak, where new findings are constantly emerging, such as in "Applied Languages in the University Context."
5. Interface and Dashboard: A clear web dashboard allowing employees to monitor incoming emails, their categories and sentiment, as well as view statistics and trends. We integrated it with the client's existing CRM system.
6. Feedback Mechanism: For continuous improvement of the models, a system was implemented where operators could correct automatic categorizations or sentiment analyses. These corrections served to retrain the models and optimize them.

Implementation and Technical Details

The entire implementation process was divided into several phases, which included data collection, model training, integration, and testing.

Phase 1: Needs Analysis and Data Collection (4 weeks)

Initially, we worked closely with the client's customer support, marketing, and sales departments to understand their current processes and define the required categories for email sorting (there were 12 in total). Subsequently, we gathered historical email data (anonymized, of course), specifically approximately 20,000 emails over the last 6 months. These emails were manually annotated by the client's team, who sorted them into the defined categories and assigned sentiment. This step was the most challenging but crucial for the quality of the training data.

Phase 2: Model Training and Optimization (6 weeks)

With the annotated data, we proceeded to train our AI models. We used techniques such as cross-validation and hyperparameter tuning to achieve the highest possible accuracy.

• Classification Model: After training, the email sorting model achieved an accuracy level of 89% for identifying the correct category. For the most important categories such as "Complaint" or "Product Inquiry," accuracy was even above 92%.
• Sentiment Analysis Model: Our Slovak sentiment model achieved an accuracy of 85% in distinguishing between positive, negative, and neutral sentiment on the test data set. Initial results were promising, but we knew that the true value would only be demonstrated in real-world operation.

Phase 3: Integration and Testing (3 weeks)

Subsequently, we integrated the AI assistant into the client's existing IT infrastructure. This included connecting to the email server and the CRM system. During integration, thorough testing was carried out in a pilot operation with a small group of employees. We collected feedback and made minor adjustments to ensure the system functioned flawlessly and was intuitive for users.

Case Study: ABRA Consulting and Client X (e-commerce firm)

Our client is a medium-sized Slovak e-commerce firm (let's call it "EshopPro"), which sells a wide range of consumer electronics and household goods online. On average, it receives 700-1000 emails daily from customers, suppliers, and partners.

Situation Before AI Assistant Implementation

Before the implementation of the AI assistant, EshopPro employed 8 customer support agents. Their daily tasks included:

• Manually sorting incoming emails into the correct categories and assigning them to relevant departments or specialists.
• Reading and understanding the content of emails to determine their urgency and tone.
• Searching for information in the CRM system based on email content.
• Subsequently responding to inquiries or escalating them.

The average time to process one email (from delivery to initial assignment/response) was approximately 15-20 minutes. Many lower-priority emails remained unanswered for several hours, sometimes even a day. Up to 15% of emails were incorrectly categorized at the first contact, leading to delays and additional work. Sentiment analysis practically did not exist; it was only intuitive and subjective.

Implementation Process and Training

After successful pilot operation, we launched the system for the entire team. This included an intensive series of training sessions for the customer support team, totaling 12 hours, where they learned how to effectively use the new dashboard, how to interpret sentiment data, and how the AI feedback mechanism works. Emphasis was placed on the AI assistant being a tool to support them, not replace them.

After Implementation: Quantifiable Results

Three months after full implementation, we evaluated the impact of the AI assistant. The results were impressive:

• Reduced email processing time: The average time for initial email sorting and assignment decreased from 15-20 minutes to less than 2 minutes. This represents a time saving of over 85% per email.
• Reduced error rate: The rate of incorrect email categorization decreased from 15% to less than 3%.
• Increased response speed: Emails are now sorted and redirected immediately upon arrival. Urgent and negatively toned messages are automatically prioritized, which reduced response time for complaints by 40%.
• Increased customer satisfaction: Based on the client's internal surveys and ratings (NPS – Net Promoter Score), customer satisfaction with response speed and quality increased by 18 percentage points.
• Workforce optimization: Thanks to automation, the customer support team could refocus on more complex problems and proactive solutions. Out of 8 employees, 2 could dedicate themselves to developing new customer services instead of routine sorting.
• Deeper customer insights: Through sentiment analysis, EshopPro receives daily and weekly reports on overall customer sentiment, identifies the most common problems, and monitors the impact of product changes or marketing campaigns. For example, after launching a new promotion, they were able to immediately determine that up to 70% of responses were positive, confirming the campaign's success. Conversely, a 15% increase in negative sentiment related to delivery delays helped them quickly identify and resolve a logistics issue.

Sentiment Analysis in Practice

One of the greatest benefits was the AI assistant's ability to analyze sentiment. For instance, if a customer sent an email with phrases like "I am incredibly disappointed with your product" or "Catastrophic service!", the AI system immediately marked the message as "negative" with high certainty and automatically assigned it the highest priority. This email was then instantly escalated to a specialist who could proactively contact the customer and offer a solution. On the other hand, emails with text like "Thank you for the fast delivery, I am thrilled!" were marked as "positive" and served as valuable feedback for the marketing department and to boost team morale.

Benefits and Advantages of AI Integration

The integration of the AI assistant for email sorting and sentiment analysis brought EshopPro a wide range of benefits that go beyond just saving time and money.

• Increased efficiency and productivity: Employees can focus on higher value-added tasks instead of routine processes. This leads to better utilization of human capital.
• More precise and faster decision-making: Immediate access to structured communication and sentiment data allows managers to make more informed and quicker decisions.
• Improved Customer Experience (CX): Faster response times, personalized approach, and proactive problem-solving lead to higher customer satisfaction and loyalty.
• Identification of trends and patterns: AI can uncover recurring themes, problems, or positive aspects that would be easily overlooked in manual analysis.
• Competitive advantage: Companies that effectively use AI for communication processing gain a significant competitive advantage in the market.
• Reduced operational costs: Although an initial investment in an AI solution exists, long-term savings in personnel costs, reduced error rates, and increased efficiency significantly outweigh these costs. In EshopPro's case, we estimated the Return on Investment (ROI) to be 14 months.

Challenges and How We Overcame Them

No large-scale AI implementation is without challenges. Our experience with EshopPro showed us several key points:

• Quality of training data: The biggest challenge was ensuring a sufficient quantity and quality of annotated data for model training. The initial manual annotation was time-consuming. The solution? Close cooperation with the client, detailed instructions, and continuous review of annotations.
• Linguistic specificities: The Slovak language, with its flexible morphology and extensive diacritics, presents a greater challenge for NLP models than, for example, English. Therefore, it was essential to use specialized models for Slovak and train them thoroughly on relevant data.
• Team adaptation: Resistance to change is natural. Some employees feared that AI would replace them. The solution? Transparent communication, emphasizing the benefits of AI as a support tool, not a replacement, and extensive training with practical demonstrations.
• Ongoing maintenance and optimization: AI models are not static. Language evolves, new types of inquiries emerge. The system requires regular monitoring, feedback collection, and occasional retraining to maintain its high accuracy.

Conclusion

The case study with our client EshopPro is clear proof that the integration of an AI assistant for email sorting and sentiment analysis is not just a futuristic vision, but a tangible reality today with measurable business benefits. For companies facing challenges in managing a large volume of communication, artificial intelligence offers an effective path to significantly increased operational efficiency, improved customer experience, and valuable data insights.

If your company also struggles with overloaded email inboxes, slow response times to customer inquiries, or needs to gain deeper insight into the mood of your customer base, integrating an AI assistant might be the right investment. Contact us at ABRA Consulting to discuss how we can help you transform your communication and elevate your business to a new level. Let AI work for you and focus on what truly matters for your business – growth and innovation.