Positive Customer Experience (CE) is critical for any product or service. CE is the mirror of the user’s perception of the product. Engaging and robust CE is a sign that everything is going according to the plan. It is a direct result of every element of the product working together for the bigger whole.
Customer experience is what keeps the business going. To achieve quality results (besides caring for the users), marketers and business people use numerous modern technologies, from service personalization, conversational UI up to natural language processing and semantic search.
One of the most prominent elements in the grand scheme of customer experience is customer support. Let’s explain why.
Customer service is the sign of a responsible company that respects its users. It is one thing to develop a product and leave users on their own, and it is an entirely different thing to step in and help users to understand how to use the product and solve their emerging issues.
The direct purpose of customer support is to solve emerging user issues appealingly and constructively. The nature of the problems goes far and wide. It might be:
- Minor misunderstanding of the products design scheme.
- Incorrect use of the product.
- Actual technical problems of the product.
Сustomer service is user feedback forum designed to research general perception of the product and also find out what features and elements require further improvement and polishing.
However, the workflow of the customer support department is not without its issues.
- When the product is widely used - the amount of various user feedback is inevitably overwhelming. It takes time to sort it out and process.
- It takes time to train new operators and get them into the ins and outs of the product.
- It also often takes a lot of time and effort to interact with the customer to figure out the nature of the issue.
- Then, it takes some time to find appropriate responses from the knowledge base.
Because of that, customer support often gets a lot of criticism for trying to be what it is. But there is a solution.
How did we manage to improve customer support service by implementing semantic search features?
MSP was a customer support system that handled user feedback and solved emerging issues across the product line-up of the client. The system was barely capable of doing its job, and the overall workflow was too clumsy.
There was a notion that the current MSP workflow was holding the system back and that directly affected the quality of the customer service. Due to the scope of the system - it was apparent that the system just wasn’t designed for the scope it was handling.
The issues included:
- The response time was too slow during high workload periods - that resulted in the appearance of the bottlenecks.
- The system slowed down considerably during high-load periods.
Because of that, it was decided to make an overhaul of the system:
- Make it more scalable to process large quantities of data
- Streamline and optimize the workflow, so that the operators would have easy access to the knowledge base and deliver responses to the customers in less time.
- Simplify the training process of the new employees by letting them study the existing knowledge base with ease.
The APP Solutions was approached to develop a suitable solution that would handle these issues.
Our global task on the project was to develop a flexible solution that would streamline the workflow of the customer support operation and at the same time, make it more valuable in terms of insights.
On the ground level, the tool would simplify the workflow of the customer support operator and make it easier for him to find the right answers from the database in a short time.
In addition to that, we wanted to provide an analytical component that would show the trends and uncover insights into the customer issues in an understandable form.
Due to the implementation of fundamental natural language processing features - this also allowed to analyze the database and extract valuable insights regarding product use and related issues.
The main challenge of the project was to figure out how to make a tool that would streamline the workflow of the customer support operator and make it as efficient as possible. The critical requirement was accessibility.
It was apparent that the solution required an application of natural language processing. Semantic search feature seemed to be perfect for the cause. This approach enables finding relevant information in the database from the basic input. The operator needs to give the system a specified request, and the system will find the stuff he’s looking for.
The question was which approach was the most fitting for the task.
To determine the best approach - we have tried and tested several natural language processing tools:
- At first, we have tried Elmo Embeddings. The results were okay, but the performance left a lot to be desired.
- Then we tried GloVe. It was good but too complicated for its purpose. We needed a leaner solution.
- Finally, we have tried Doc2Vec, which proved to be the most optimal solution both in terms of performance and overall flexibility.
After figuring out the optimal solution, we needed to make it work within the customer support database.
The primary function of the application was to:
- Find dialogues and responses in the database that are the most relevant to the input query.
- This information would serve as a basis for further customer-operator interaction and save a significant amount of time in the process.
The model was trained on the body of texts made out of customer support conversations. The backbone of the model was developed in Python NLTK.
To configure the system, we have applied TF-IDF scoring to determine relevance to the user’s queries. Besides, we have performed bigram and trigram corpus analysis and basic polarity check, which provided a foundation for further database processing with Doc2Vec. After that, the application was deployed with Flask API.
The secret of the effective semantic search is thorough testing and continuous optimization of the model. The thing is - NLP applications are like diamonds in the rough. Even the simplest models require polish to do its job as it was supposed to do.
The critical factor is the flexibility of the model in recognizing the specific aspects of the text and finding matches for the user’s queries.
This process required the use of several training scripts and comparative analysis of the results. Each scenario was designed for specific detail.
- One script to analyze the general context of the conversation
- The other to extract keywords relevant to the service
- Third to determine the tone of discussion at the start, throughout the interaction and after solving the issue.
After a thorough polish, the system became flexible enough to find a selection of relevant replies even from the broadest queries.
Slow responses are one of the chief complaints about customer support services. Delivering results on both ends on time is one of the critical factors of efficient operation. This required the ability of the system to scale according to the workload.
From the business standpoint, - scalability is one of the columns of the well-oiled and smooth customer service. In the context of r search, this being able to process a large amount of data and find the relevant selection in a short time.
To make the system capable of processing multiple database requests and deliver fast results - we have used Google Cloud Platform’s autoscaling features.
With this intact - scalability became a non-issue so that the users could focus on issues that are more integral to their working process.
- Google Cloud Platform
- NLTK for Python
- Flask API
- Project manager
- Web developer
MSP project was an excellent showcase of how to find a reliable and straightforward solution to the complex problem of customer support workflow.
It was an opportunity for our team to implement new skills in practice and develop a solution for this type of projects.
Another important thing is that we have managed to realize the project in a relatively short time frame. In over a month, we already had a working prototype, and by the end of the second month, the system was fully operable.