2023 is a year where Artificial Intelligence will be intensively used to understand the customers.
Virtually 100% of the data companies currently use to make decisions comes from the structured data they have, this data represents only 20% of the total data held by companies; the other 80% is unstructured and difficult to use.
However, in that 80% of unstructured data there is extremely valuable and direct information. A conversation with a customer, a comment in a survey, or a tweet, are feelings, opportunities, complaints, etc., that come directly from the customer and are not obtained by indirect methods.
Imagine if the company's CEO could talk directly to thousands of customers every day to understand them, improve services, implement new products or optimize processes.
Here is where Artificial Intelligence has a lot to contribute.
Challenges for Artificial Intelligence to understand the customers.
There are several challenges that need to be addressed to have a better analysis of customer feedback using artificial intelligence:
- Data quality: To train accurate models, it is important to have high-quality and representative data. This can be a challenge when dealing with customer feedback, as the data may be unstructured, noisy, and biased. Additionally, there may be issues such as missing data or data that is difficult to label.
- Handling unstructured data: Customer feedback can come in many forms, such as text, audio, and video. This unstructured data can be difficult to process and analyze using traditional methods, and requires specialized deep-learning models and techniques.
- Handling multiple languages: Customer feedback can come from a diverse population of customers and may be in different languages. This can make it difficult to train models and extract insights from the data.
- Handling sensitive information: Customer feedback may contain sensitive information, such as personal information, that needs to be handled with care. This requires data privacy and security to be considered, which can be challenging to implement.
- Understanding context: The meaning and sentiment of customer feedback can be highly dependent on the context in which it is given. Without understanding the context, models may misclassify or misunderstand the feedback.
- Generalization and explainability: The models must generalize well to new and unseen data when dealing with customer feedback. Additionally, it's important to explain the model's decisions, not just to achieve high accuracy but to understand the customer's needs and wants.
- Integration and Deployment: Once you have a good model, integration to your system and deployment in your company can be a challenge. This requires a lot of work on data pipelines, infrastructures, security, and monitoring.
Addressing these challenges requires a combination of advanced techniques in natural language processing (NLP), computer vision, and machine learning, as well as expertise in handling unstructured data and a good understanding of the specific domain of customer feedback analysis.
What can we expect for 2023?
Several trends are likely to continue to be important in the field of deep learning for text analysis in 2023:
- Pre-training: Pre-training models on large amounts of text data, such as using a variant of BERT, have proven to be very effective in many NLP tasks. This trend is likely to continue, with researchers exploring new ways to pre-train models, such as using unsupervised or semi-supervised methods.
- Transfer learning: Transfer learning, where pre-trained models are fine-tuned on smaller, task-specific datasets, has become a popular approach in NLP. The ability to use pre-trained models as a starting point is expected to make it easier to apply deep learning to new text analysis problems, even with limited labeled data.
- Language understanding: There has been a lot of progress in language understanding, particularly with the development of transformers and pre-training methods. In the coming years, we expect to see more research focused on developing models that can understand the meaning and context of the text rather than just recognizing patterns in the data.
- Multi-modal and cross-modal: With the rise of multi-modal data, such as text and images, there has been a growing interest in developing models that can handle multiple data types and make connections between them. This is known as cross-modal or multi-modal learning. This trend is expected to continue as more and more data becomes available in a multi-modal format.
- Explainable AI: With the increasing complexity of deep learning models, there has been a growing interest in developing more transparent and interpretable models. This includes techniques such as attention mechanisms and feature visualization that allow researchers to understand how models make decisions.
- Adversarial and robustness: With the deployment of models in more sensitive and high-stakes applications, there will be a continued focus on hostile attacks and robustness. Ensuring that models are robust to various forms of manipulation and malicious attacks will be crucial for their successful deployment in real-world applications.
All these are some of the trends that are expected to continue to be important in 2023, but the field of deep learning is highly dynamic, and new developments may emerge. In Deep Talk we are working to build this future.