The Connection Between Self-Taught AI and the Net Promoter Score

The Connection Between Self-Taught AI and the Net Promoter Score

In recent years, self-taught artificial intelligence (AI) has shown remarkable similarities to how the human brain works. These large language models, for example, can learn the syntactic structure of a language without external labels or supervision. They are trained with massive amounts of text data from the internet and are able to predict the next word in a sentence based on the context given.

This self-supervised learning approach mirrors how animals, including humans, learn in the real world. We don't rely on labeled data sets to gain knowledge. Instead, we explore our environment and learn from our own experiences. Just as self-supervised algorithms create gaps in data and ask the neural network to fill in the blanks, our brains are constantly predicting and filling in missing information. Whether it's predicting an object's future location or the next word in a sentence, our brains are wired to make these predictions.

However, while self-supervised learning algorithms have been successful in modeling human language and image recognition, they still have limitations. One major difference is the presence of feedback connections in our brains. Feedback connections play a crucial role in our ability to predict and make sense of the world around us. Current AI models have few to no feedback connections, which hinders their understanding of complex systems.

This brings us to the concept of the Net Promoter Score (NPS), which is widely regarded as the gold standard of customer experience metrics. NPS measures the likelihood of someone recommending an organization, product, or service to others. It is calculated by subtracting the percentage of detractors (unhappy customers) from the percentage of promoters (loyal and enthusiastic customers). The higher the NPS score, the better.

Just as the brain's ability to predict and fill in missing information is crucial for understanding the world, NPS provides valuable insights into how customers perceive a company. By asking customers how likely they are to recommend a product or service, companies can gauge their overall satisfaction and loyalty. Promoters, who give a score of 9 or 10, are more likely to become brand evangelists and bring in new customers. Passives, who give a score of 7 or 8, are satisfied but not enthusiastic. Detractors, who give a score of 0 to 6, are unhappy and may discourage others from buying.

The use of NPS can be both relational and transactional. Relational NPS surveys are conducted periodically to understand how customers feel about the company overall. Transactional NPS surveys, on the other hand, are sent after specific interactions with the company to gather feedback on a granular level. Both types of surveys aim to improve customer satisfaction and loyalty.

When it comes to designing NPS surveys, it's important to keep them simple and avoid unnecessary demographic questions. The goal is to get honest feedback from customers without overwhelming them with irrelevant questions. By focusing on the customer experience and understanding their needs, companies can use NPS to identify areas for improvement and build stronger relationships with their customers.

In conclusion, the connection between self-taught AI and the Net Promoter Score lies in their ability to predict and understand complex systems. While self-supervised learning algorithms have shown similarities to how the brain works, they still lack the feedback connections that are crucial for deeper understanding. Similarly, NPS provides valuable insights into customer satisfaction and loyalty but requires ongoing efforts to improve the overall customer experience.

To apply these concepts in practice, here are three actionable pieces of advice:

1. Embrace self-supervised learning: In the field of AI, self-supervised learning has proven to be highly effective. By training models to fill in gaps in data, we can mimic the brain's predictive capabilities. Invest in self-supervised learning algorithms to enhance AI systems and make them more adaptable and intelligent.

2. Prioritize customer satisfaction: Just as the brain fills in missing information to understand the world, companies should focus on understanding their customers' needs and improving their overall satisfaction. Implement NPS surveys on a regular basis to gain insights into customer perceptions and identify areas for improvement.

3. Foster brand loyalty: Promoters, who give high NPS scores, are more likely to become loyal customers and brand evangelists. Focus on providing exceptional customer experiences and exceeding expectations to turn customers into promoters. This can be achieved through personalized interactions, proactive support, and continuous efforts to improve products and services.

By combining the predictive capabilities of self-taught AI with the customer-centric approach of NPS, companies can gain a deeper understanding of their audience and build stronger relationships. As technology continues to evolve, leveraging these insights will be crucial for success in an increasingly competitive landscape.