*By Lyse Nogueira
In the hyperconnected world we live in, consumers can have multiple shopping journeys, with interactions through websites, apps, social networks or even a phone call. Despite the many options for contact channels available, what is most relevant to the customer is the quality and level of personalization of the service received.
After all, who likes to report a problem to a company and receive an offer instead of a solution? Situations like this are more common than they seem and undermine customer loyalty. The Cracking Tomorrow's CX Code survey, carried out by the CMO Council in partnership with SAS in 2022, shows that 56% of consumers in Latin America say that brands are not very good at offering a perfect experience in the digital and physical environment.
The same study reveals the key factors in guaranteeing the loyalty of Latin American customers, being consistency in service omnichannel and the quality of products of equal importance, with 67,50% each. The other three pillars are: high quality of service provided (50%), immediate availability of products for delivery (50%) and low prices (50%).
Having a complete view of the customer, in order to understand him and offer a good interaction according to his profile seems to be the great challenge for brands. This happens because the increase in the number of service channels, combined with the digital transformation of companies, generates a high volume of data. As a result, it has become urgent to adopt technologies for collecting, organizing and storing this information, as well as for processing and extracting relevant ideas using artificial intelligence (AI).
The use of AI allows finding patterns and similarities in the customer profile, scanning a much larger number of data than the human mind is capable of assimilating, forming clusters of consumers that can engage with a particular promotion or other experience.
The same resources also make it possible to identify customers with the greatest potential for canceling the service so that a retention strategy can be drawn up. All these measures are based on qualified data and automated processes, ensuring standardization and reducing decision-making time.
The time and place of PLN in marketing
To go deeper into the AI techniques that are applied to customer service, I highlight the importance of Natural Language Processing (NLP). This strand of AI allows machines to be able to understand, interpret and manipulate human language. That is, computers are able to automate the language understanding process, removing personal interpretations and allowing the scalability of this process, since human beings have a limited capacity for reading and interpreting volume.
The PLN is used in several industries and sectors, for example, to find patterns and extract relevant information in the submission of claims, assisting in the detection and prevention of fraud and even performing the automatic transcription of a call.
Applied to customer service, the PLN makes it possible to identify, in a clear, objective and automated way, the main reasons why customers contact the call center, facilitating directing to specific cells and even being able to generate suggestions for routes to be followed by the customer service. attendant.
More than simply identifying the reason for the call, the information generated by the PLN can be crucial to identify the moment the customer is in (segmentation, acquisition, monetization, retention or recovery) and identify which Next Best Action (NBA) to be done with him. When based on significant data and using techniques of machine learning, the NBA has enormous potential to improve the long-term customer experience and satisfaction.
Imagine that a customer of a telephone company makes a call informing that the internet signal seems to be weaker than expected on the Wi-Fi in some points of his house. The attendant follows the protocols and indicates that everything is within normal limits, with no instability in the network. Minutes after the call, the customer receives a push on your app with information about new router models that amplify the reach of the Wi-Fi network. It was exactly what he needed to solve the instability problem.
What would this customer's perception have been if, when he called and reported signal instability, the operator had offered him an increase in his broadband service at an additional cost? Most likely, the customer, in addition to not contracting the service, would end up changing his internet provider.
By using PLN, we remove the attendant's subjective interpretation factor, making it possible to combine: historical information (demographic profile, consumption profile, propensities, etc.) impactful and significant at that time.
The time has come for brands to go beyond collecting and storing data and using the full potential of the technology that is already on the table to combine them to enable the best possible customer experience. This is no longer a trend, but a strategy that can make a company more competitive and a leader in its market.
*Lyse Nogueira is Customer Advisor at SAS Brasil
Notice: The opinion presented in this article is the responsibility of its author and not of ABES - Brazilian Association of Software Companies
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