When artificial intelligence is related to customer service there is a rapid association with chatbots, enriched with certain cognitive capacities, to understand a natural conversation with a user and to be able to advise or direct him to the most appropriate answer or solution.
NLP (Natural Language Processing) and Machine Learning techniques are pillars for this type of solution. NLP is what enables the possibility to understand, process and reproduce human language, the way to generate “natural” conversations between human and machine. Machine Learning allows the automatic learning of patterns whose complexity increases with the learning itself over the time, enabling the AI to carry out interactions increasingly closer to the user and more accurate to their behavior and needs.
But there is much more potential in AI applied to the relationship with users and customers, the chatbot is only the tip of the iceberg.
What value does AI bring to the relationship with users?
Interaction with a user comes in multiple ways. On one hand, a complete or 360º vision will facilitate the knowledge that the company has of the user and / or client, helping to offer the services or products that best suit tastes, behaviors or consumption patterns. On the other, there are multiple tasks associated with the relationship with the client that are highly complex, mainly due to the state in which they are captured: format, lack of homogeneity and no common structure (unstructured data), sources …
For these reasons, did you know that many companies cannot analyze an almost close to 80% of their data? And the field of customer service is one of the most affected by the diversity of content, origins or platforms through which they are captured. At Keepler we have developed a solution that uses artificial intelligence that is able to extract relevant information and to adapt it for analysis in different areas of the relationship with the user.
Call centers: Call center conversations are full of information whose analysis with the right tools can streamline processes and improve service.
- Audio language detection to automatically catalog calls when there are centers in different countries and multi-language clients.
- Sizing of the centers by knowing the waiting time and its impact on the quality of the service.
- Automatic audio transcription generating texts from conversations, being able to separate conversations between different interlocutors.
- Detection of user intent, which is key to understand the reason of the call and automating response processes.
- Identification of key content in the call to generate relevant information about the result of the call, such as reference numbers, product names, dates …
Classification of emails: Receiving hundreds or thousands of emails that reach customer service mailboxes can generate an excessive classification and management workload. Automating the analysis and assigning it to the corresponding department reduces cost and time, as well as automatically detecting and discarding spam emails, thus reducing a considerable number of emails.
Social platform analytics: One of the most complicated environments when it comes to extract relevant information, are the platforms and social networks, full of direct and indirect comments, reviews, public conversations … In addition, all of them with an extremely unstructured format and full of particularities, expressions, symbols … With UDI it is possible to extract relevant data, process it and generate valuable information from it relevant to a brand, product, establishment …
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