For Machine Learning to work, a company must first understand the application of digital fulfillment. It is, therefore, necessary to consider its scope and functionality according to the customer’s needs.
See below the main factors that should be evaluated for implementing this technology!
Service Process
An important point to consider is the experience itself. In this sense, the service process that the consumer goes through must be aligned and integrated so that he can go from one channel to another.
Thus, going through all the stages, it is possible to see the digital and humanized service as a whole, understanding its flaws and positive points.
With these two aspects defined and functional, it is possible to invest in Machine Learning, as there will be enough data and a structured workflow for the system to analyze.
Professional Deployment
Deploying Machine Learning is not simple. Therefore, the company needs professionals or partner companies that understand the field. In this way, it is possible to guarantee, in addition to the operation, the maintenance and updating of the system and the correct monitoring.
Adoption Of Technology By The Company
Artificial Intelligence and, more precisely, Machine Learning in a company must be part of its culture. That is, adopting this technology without the essential people agreeing is impossible. In addition, it is necessary to structure and architect the company’s data so that there is good quality interaction between the new technologies.
This is because it will involve the future of the organization and the day-to-day employees. Thus, this is a particular point to be taken care of, as it can encounter barriers.
One is that some people see the use of Machine Learning as unnecessary. Others, like employees, fear for their job positions.
In that case, the future is uncertain. At the same time that some functions may cease to exist, AI opens an excellent way for new professions focused on technology, relationship, and strategy. Therefore, what companies should choose to do now is rethink functions and train employees.
For example, when using chatbots, costs decrease, and human assistance is directed toward solving more complex issues. With this, the employee is not overloaded and can use their skills on what is most important instead of wasting energy on repetitive tasks.
Use Of Good Data
Machine Learning will not work if the data obtained for learning is not of good quality. Therefore, applying an excellent virtual service capable of capturing data from users’ experiences and receiving feedback is necessary, generating a satisfactory bank for the system to learn.
Another critical point is integrating the data with the company’s various systems to direct and classify them correctly. There is also the security aspect, especially regarding the use of customer data.
WhatsApp, for example, due to a change in its privacy rules, has experienced a significant loss of users. In this way, it is necessary to ensure that the data is protected from cyber-attacks while the system can use it to learn from it.
Faced with such a demanding public, it is necessary to be very transparent about using customer data and information provided in the service. In this way, the client needs to be able to access them if he wants to.
Creating An Ethical System
When using Machine Learning to build a system capable of efficiently interacting with customers, great care is needed. After all, if we are talking about a technology that can learn by itself, there is a risk that something can get out of control.
That’s what happened with the Tay robot in 2016. In Microsoft’s experiment to replicate teenage language on Twitter, the robot received several messages with profanity and racist comments.
By analyzing the words and language, the techniques learned and applied the same uses in the posts of the robot itself, generating controversy for its offensive content.
The company must establish a language standard to prevent its bots from performing incorrect actions based on erroneous learning and a database with malicious messages.
Points Of Attention In Virtual Consultations
Machine Learning can be used in the service segment of different companies. However, it is necessary to keep in mind if it fully serves the business audience. See some points of attention for virtual calls!
Understand Who Your Audience Is
We live in a digital world, in which generation Z ended up giving new meaning to the positioning of companies, including in customer service. However, it is necessary to remember that it is not only Generation Z that integrates its target audience.
People of earlier generations, especially those born before 1980, can find themselves lost with so much technology. Although many are also in the online environment, some have different behaviors; for example:
- not following trends;
- using just a messaging app;
- being outside of social networks or most of them;
- not interacting with robots or shopping online.
Therefore, the approach must be different. In the case of the chatbot, the technology must present accurate and easy-to-follow solutions so that the consumer does their autonomous service. If not, human service should be started right away to avoid customer frustration.
It is also essential to be present on several channels.
Don’t Give Up What You Already Have
Adopting virtual service doesn’t mean wiping out the entire call center team. On the contrary, with a good understanding of the results obtained with the adoption of this system, you can redirect people to other priority services and functions.
This is important as the human understanding of complex subjects has not been replaced by Machine Learning. Some people still prefer to be attended by people.
Use Your Data
The data you already have in the company can inform the need for virtual service or how to implement it.
For example, if your organization is online commerce and most contacts refer to basic requests such as order tracking or ticket issuance, you can deploy bots to automate the process.
Also Read: A Dive Into AI, Machine Learning, And Deep Learning
