Enhance Machine Learning: Digital transformation is already a reality. Now, we are on the way to improving it more and more. In this sense, Machine Learning is fundamental.
Customer service is the concept responsible for making companies more humanized, accessible, and efficient in the face of demanding consumers who live in the era of experience.
Data analysis combined with specific features can create structured support for machine learning. The system learns better and more accurately, and reliably. After all, you will have accurate factual data interpreted correctly and automatically.
Customer Satisfaction Rating
Customer feedback is one of the most precious pieces of data a company can have to understand its audience and know how to deal with it.
For this to happen, Machine Learning needs to collect and analyze this data to get results about the level of consumer satisfaction.
The process occurs through text, and the technology must be used to understand what was written, whether it is positive or negative feedback, as well as capture insights from suggestions or errors.
In this case, there are several ways to assess customer satisfaction, and it is necessary, first, to engage them in giving this answer. At the end of a chat, it is possible to use a bot so that the customer evaluates the conversation through stars or emojis corresponding to some emotions.
Although fast, this system is prevalent. Therefore, although challenging, investing in obtaining complete feedback is a better alternative.
Service Data
Another vital resource to improve Machine Learning is to evaluate what happened in the calls. For example:
- which options for questions or services were most accessed on the chatbot;
- how many times the service was directed to the human attendant;
- what is the frequency of abandonment of support;
- why customers were transferred to traditional service.
All data obtained is essential to create a pattern among customers. Thus, it is possible to identify what works or not, whether in digital or human service.
Speech Analytics
Using Speech Analytics is an excellent way to obtain data and better structure your service system, promoting customer satisfaction and training your employees.
This tool is essential in real-time, as it can analyze the voice of the consumer and the attendant during the call. Thus, it is possible:
- capture emotions;
- detect the language used;
- select keywords;
- identify patterns in care;
- measure conversation time;
- measure call efficiency.
Reinforcement Learning
As seen, one of the ways that machines learn is through reinforcement. The system knows what is correct by receiving feedback on errors and successes. From there, you can present better answers to customers, qualify messages and interpret them with the expected meaning.
Deep Learning
Deep Learning can be considered the next phase of Machine Learning, making it possible to put many of its projects into practice.
It consists of using even more complex algorithms called artificial neural networks. Based on the very structure of the brain, these networks seek learning in deeper layers, making the system more intelligent and accurate.
For example, according to Exome in a 2011 report, the use of a data warehouse and its analysis by AI was able to identify unusual patterns in several companies, which led them to apply new insights into selling products and dealing with customers.
One of the cases portrayed was from a telephone company in the US. The stored data found that four of its large customers accounted for more than half of its system maintenance calls. Also, one was about to leave the service.
With this, it was possible to repair the network and work on the relationship with the customer, convincing him to stay with the company and avoid losing millions of dollars a year.
Train Your Collaborators
Care in training employees is essential for virtual service. After all, these people need to understand not only the use of this technology but also why it will be adopted. So invest in:
- explain the changes incorporated by the company;
- show results;
- detail how the virtual agent or robot will impact the work;
- teach the types of data the team will receive and what to do with it.
