Multilingual AI analytics are key to unlocking the potential of customer experience to drive business growth

Text analysis is a powerful discipline capable of discovering and annotating every example of customer opinion, regardless of which language the customer speaks.

For business executives who are waking up to the vast amounts of unstructured data surrounding their businesses, the language-agnostic possibilities of AI for text analysis are a critical (but easily overlooked) )The problem.

After all, unstructured data (UD) is not structured data in a format such as spreadsheets, but is usually large amounts of data in various social media, blogs, website comments, call center calls, private chats, etc. – and this data represents a vast resource with even greater value for businesses interested in improving customer experience (CX).

Most data is unstructured data. According to estimates from MIT, 80% to 90% of data today is unstructured data, and it is growing rapidly. And this fact means that all opinions from customers can be collated and analyzed by businesses that have invested in technology and expertise.

This is the role of text analysis artificial intelligence. This results in every customer who comments on a business brand on any platform having unprecedented access to their thoughts, opinions and ideas. It allows companies to accurately and quickly identify customer pain points that are prioritized, thereby reducing customer churn.

Given this generality, it is particularly important to recognize the value of language agnosticism. Limiting analysis and annotation to English perspectives only (when other perspectives exist) undermines the scale of unstructured data and the generalizability of this text analysis.

Therefore, it is necessary to understand how multilingual AI analytics works and its potential to gather a comprehensive overview of customer opinions.

The Power of Natural Language Processing

The foundation of AI-driven text analysis is the combination of machine learning (ML) and natural language processing (NLP).

Machine learning is an artificial intelligence method designed to imitate human learning. While traditional programming requires the execution of rules created by humans, machine learning uses data analysis to learn extremely complex patterns that can be used for inference, making machine learning very good at solving problems and performing complex tasks.

At the same time, natural language processing (NLP) belongs to processing languages. In fact, it can be understood as one of the complex tasks supported by machine learning.

In this context, the uses of natural language processing (NLP) are diverse. It can be used for simpler goals, such as counting how often a given term or word appears in a text. Or one can take on the more difficult challenge of determining the mood or even emotion of a given text.

Obviously, both are of great use to businesses that want to understand in detail the opinions of all available customers.

These uses of natural language processing (NLP) allow businesses to evaluate large amounts of data to discover how often their brand is being talked about online or offline, as well as understand whether the comments are positive or negative, or related to a The series is about more nuanced emotions.

Multi-lingual approach

Crucially, the benefit of this approach is its ability to include all customer opinions – text analysis applies to each opinion rather than a sample or selection .

However, in order to achieve this goal, the language in which a given opinion is expressed cannot be limited, but AI needs to be completely language-agnostic, especially if a business is a multinational organization.

This can be achieved through the use of unsupervised and supervised machine learning. Supervised machine learning means that the algorithms involved are "trained" by humans annotating training data, and AI can do better than humans at tasks involving large amounts of data (also known as big data).

To ensure that all language needs are met, the researchers leveraged a team of approximately 300 native speakers of a variety of languages ​​who read, understood and manually annotated the unstructured data. For example, determine whether a tweet is positive or negative, whether there is sarcasm in its subject, or even what the customer journey is suggested by the content of an email or chat message.

Once the AI ​​is trained in its native language (without the need for translation into English and machine learning models using English) to achieve its goals (whether establishing emotions or identifying topics) with great accuracy, the results can be easily used Visualization in English to unlock all customer opinions in a language they can understand for customer experience (CX) professionals, customer retention managers, and more.

The most important thing is that the accuracy of artificial intelligence can continue to improve. For example, when a person annotates a small subset of tweets with a certain emotion, its accuracy can be measured. You can see that 80% to 90% or more of the content matches the algorithm, no matter what language the tweets are written in.

This shows how powerful these AI technologies have become, given the subjective nature of expressing emotions.

Finding the needle in the haystack of unstructured data

Unstructured data (UD) is everywhere and it represents an opportunity to understand the opinions of all customers, rather than, by definition, like polls Only sample-based customer opinions can be provided.

However, to truly realize this ability to gain unfettered access to consumer opinions, multinational companies will not only need to hire AI experts and technicians, but also ensure that their AI systems can obtain data in all relevant languages. The same high-precision training as in English.

This way, text analysis is not only source-independent but also language-independent. Allow business leaders to confidently assert that their understanding of customer perspectives, pain points, and gain points is detailed, precise, and comprehensive.

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