Reselling/Product Recommendation.


Reselling/Product Recommendation

The concept of reselling revolves around the identification of potential returning customers within an existing customer base. A prime example of this can be observed in the car leasing industry, where customers often return for a new lease once their current lease expires. Given that it is generally more challenging to acquire new customers compared to retaining existing ones, companies often prioritize targeting their current customer base to ensure customer loyalty and prevent them from turning to competitors. To aid in this process, CACTuS has developed a model that effectively identifies customers with a high likelihood of re-initiating a contract. This model not only identifies these potential customers but also provides tailored product recommendations to enhance their experience and increase the likelihood of their continued engagement with the company.


AI Applications

One AI application for businesses facing the choice between open-source and proprietary models to deploy generative AI is natural language processing (NLP) for customer service or support chatbots. Businesses can utilize generative AI models to develop chatbots that can understand and respond to customer queries in a more human-like manner. The choice between open-source and proprietary models can impact the accuracy, scalability, and customization capabilities of the NLP models deployed in these chatbots.

Additionally, another AI application is the development of recommendation systems. Generative AI models can be used to create personalized recommendations for products or content based on user behavior and preferences. The choice between open-source and proprietary models can affect the quality of the recommendations, as well as the ability to tailor the recommendation system to specific business needs.

Furthermore, businesses can leverage generative AI for content generation, such as automated text summarization, language translation, and creative writing. The choice between open-source and proprietary models can influence the linguistic fluency, coherence, and originality of the generated content.

In each of these applications, the decision between open-source and proprietary models for generative AI deployment can significantly impact the performance, interpretability, and ethical considerations of the AI systems utilized by businesses.