The Role Of AI In Customer Service

Introduction

Customer Service Systems provide assistance to the customer for planning,  training, and troubleshooting of a product. Depending on the type of business, there are different channels of interaction like a web page, e-mail, on-site support, android application, telephone. Business giants like Netflix, Amazon, Zomato, Uber where customers need their issues to be solved quickly cannot entirely depend on the manual solution for every customer. An automated solution helps in clustering similar issues and forming a base level auto solver for common problems in the product. If any particular issue requires attention then a manual solution can be provided.

For any business, the Customer Lifetime Value(CLTV) is very important for Overall growth, Reputation, and Brand Marketing. Better Customer Experience forms a positive image of the brand. Artificial Intelligence provides 24/7 availability, quick response using Machine Learning, load scalable systems, etc in Customer Service.

The following are the areas where AI can help at the different levels of Customer Service Systems.

Customer Ticket Generation

• To initiate the conversation, customers can interact through an OMNICHANNEL(chatbot, email, smartphone, website, etc) platform. Integrating multiple channels helps the customer agent to track the complete journey of the customer on a single page.
• The main focus of Artificial Intelligence is to reduce the customer effort at shopping to payment levels. Automated Sentence completion is the latest NLP application used in almost every area. From writing an email, blog, code snippet to chat reply on Linkedin, Google Chat, we get suggestions for the next word/phrase in our sentence. This creates a positive impact on customers. Recurrent Neural Networks(RNN) are widely used in language translation. This helps to break the language barrier for native customers.

Techniques – Seq2Seq Models, Encoder-Decoder RNN.

• Along with a better creating experience, if the customer gets to see context-based most similar issues or FAQs, they can find the solution based on the similar ticket responses. Stack Overflow is an excellent example where you get similarly asked questions when you try to create one. Eventually, with a large dataset of issues, the redundant issues are solved before even reaching the support agent level.

Techniques – Sentence embedders like Doc2Vec, BERT, GloVe.

• Automated Ticket Tagging – Suggesting the title, subtitle, and subject to customer ticket based on the context is a better option compared to asking customers to select from the dropbox/ explicitly writing one. Keyword extraction, Word to Vector for mathematical operations like similarity calculations are major subtasks involved in the topic detection.

Techniques – Keyword Extraction – TF-Idf, RAKE, CountVectorizer.

Word embedders – Word2Vec, BERT.

Similarity – Euclidean distance, Cosine Similarity, Pattern matching.

Ticket Analysis

• Content-based urgency tagging for the support ticket can help to create a priority value. The priority value can help in reducing negative experiences with respect to time as urgent tasks are completed first.
• Content-based sentiment tagging plays a very important role in customer service. Customers usually get frustrated over false payments and technical issues. This affects the overall conversation with the support manager. Based on what type of issue is dealt with and what is the sentiment level of the context helps the team to create a mindset for that particular customer. Also, the requirement of the senior agent can be predetermined.

Techniques – Many to One RNN models, Support Vector Machines, Decision Trees.

Ticket Response

• Auto-Generated Emails, Chatbots helps in creating 24/7 services. Customers can get help from AI-based Assistant for basic queries like the price of product, description, comparison with other similar items. Everything above is possible through a Chatbot system.

Chatbot Techniques – Application integration (like Messenger, WhatsApp), RASA, NLP for analysis models.

• Agents can get suggestions to reply to the customer based on the ongoing conversation. Manually training every agent to solve every issue is a very costly and time-consuming task particularly when it comes to technical support. With the automated responses, Agent can crosscheck his response and also get new responses if he/she has failed to recognize.

Techniques for reply generation – Context-based recommendation system created for conversation dataset.

• During the customer and support manager live conversation, a real-time Speech Emotion Recognition, Speech to Text followed by Topic detector accelerates the total process of issue solving. The support manager’s performance analysis becomes easy with these responses recorded in real-time.

Techniques – Support Vector Classifier, LSTM, Convolutional Neural Networks.

Post Analysis  

Once the conversation is closed an auto-generated unified report can be created. This helps the organization to understand the areas of improvement for the system as well as the agent. Report if converted to a metric value using the sentiments, average resolution time, average response time, etc as parameters reduce the task of the senior agent. If the report itself mentions the areas of improvement, forms a complete product for enhancing customer satisfaction.

General Architecture

The customer service system is the backbone of any business. Today, Customer Service as a Service has provided opportunities for businesses to add the AI element to their customer relationship with investing the development time.

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