Machine Learning: How To Improve Customer Relationships

Machine Learning: Anyone who has ever had to wait in line to speak to a customer service representative at any company knows there is a faster way to solve your problems.

And if you are finally served, the person on the other end is unfriendly, then the experience worsens. Artificial Intelligence (AI) technology allows computers to make decisions and interpret data automatically based on algorithms. It is optional to program them to perform specific actions.

An algorithm is a finite sequence of actions and rules that aim to solve a problem. Each of them triggers a different type of operation when coming into contact with the data that the computer receives. The result of all operations is what makes machine learning possible.

How Did Machine Learning Come About?

Computer scientist Arthur Lee Samuel first used the name “machine learning” in the 1950s. He developed the software Game of Checkers, considered the first demonstration of the ability of AI.

Samuel’s work gained a significant contribution from mathematician Alan Turing, who tested the learning potential of machines from contact with human language.

Six Benefits Of Machine Learning In Consumer Relationships

Machine learning offers valuable insights to ensure the best relationship experience for consumers.

When they look for the contact center, their main concern is the effectiveness of the service, followed by the accuracy in solving the problem and, finally, the security, according to Aspect’s Consumer Index Report 2020.

Implementing machine learning in customer relationships brings six main benefits:

  • Greater user convenience: the customer wants to find a solution to the problem quickly, preferably on the smartphone itself. The second contact channel they seek is chat, according to the same Aspect report, hence the importance of having a well-built chatbot strategy.
  • Greater predictability: machine learning combined with big data allows predictive tests to be carried out, thus avoiding situations of customer attrition. Some examples are data analysis to predict an overload on the energy and internet network or an increase in demand for a particular product.
  • Understanding behavior: the data lets you know the best offer and time to send it to a specific customer. They also help to identify new demands and guide the development of products to meet them.
  • More individualized post-sales: It is possible to automate the relationship with relevant content and offers based on the analysis of the customer’s purchase history.
  • Cross-selling optimization: machine learning allows consumer behavior analysis when using a given product. It is possible to predict what other services can be offered to it.
  • Autonomy in problem-solving: Aspect’s Consumer Index Report 2020 pointed out that customers would like to solve their problems alone, without depending on a human attendant. Automation and machine learning will help you to have more autonomy in an agile and well-oriented way.

Eight Examples Of Machine Learning In Everyday Life

If you watch Netflix movies or are impacted by ads on Instagram, know that you are in touch with machine learning. But the use of machine intelligence goes much further! Check out eight examples:

  1. Fraud detection: banks and card operators use technology to identify suspicious transactions that do not follow the user’s behavior pattern.
  2. Recommender systems: With machine learning, sites like Amazon and Netflix can predict which items you might like based on your usage history.
  3. Search Engines: Machine learning allows Google to find more specific answers to questions asked by users.
  4. Natural language processing: Personal assistants like Siri, Alexa and Cortana have improved abilities to recognize, understand and process voice input to fulfill user requests.
  5. Chatbots: also use natural language processing to answer the most common questions from consumers.
  6. Demand forecasting: e-commerces and industries only benefit from machine learning. The data allows you to forecast sales more accurately and make them faster.
  7. Logistics: Google Maps and Waze calculate the best routes thanks to machine learning. Technology has also become essential for transportation companies.
  8. Diagnostics and health care: machine learning is used in the analysis of exams to identify diseases, helping the doctor to define the most appropriate treatment for the patient.

Also Read: Difference Between Deep Learning And Machine Learning

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