Building machine learning models can be a time-consuming process that often takes several months to implement in typical business scenarios. To ensure consistent model performance and account for variations in data distribution, regular retraining is necessary. This paper introduces a solution for improving online customer service in e-commerce by presenting a universal model for predict-ing labels based on customer questions, without requiring training. Our novel approach involves using machine learning techniques to tag customer questions in transcripts and create a repository of questions and corresponding labels. When a customer requests assistance, an information retrieval model searches the repository for similar questions, and statistical analysis is used to predict the corresponding label. By eliminating the need for individual model training and maintenance, our approach reduces both the model development cycle and costs. The repository only requires periodic updating to maintain accuracy.

For both public and private firms, comparable companies' analysis is widely used as a method for company valuation. In particular, the method is of great value for valuation of private equity companies. The several approaches to the comparable companies' method usually rely on a qualitative approach to identifying similar peer companies, which tend to use established industry classification schemes and/or analyst intuition and knowledge. However, more quantitative methods have started being used in the literature and in the private equity industry, in particular, machine learning clustering, and natural language processing (NLP). For NLP methods, the process consists of extracting product entities from e.g., the company's website or company descriptions from some financial database system and then to perform similarity analysis. Here, using companies' descriptions/summaries from publicly available companies' Wikipedia websites, we show that using large language models (LLMs), such as GPT from OpenAI, has a much higher precision and success rate than using the standard named entity recognition (NER) methods which use manual annotation. We demonstrate quantitatively a higher precision rate, and show that, qualitatively, it can be used to create appropriate comparable companies peer groups which could then be used for equity valuation.

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Customer experience or CX focuses on the relationship between a brand and its customers. It includes every bit of interaction, brief or long, even if the conversation doesn't result in a purchase. Having said that, a few years ago, customer experience meant how attentive retailers were to address customers and their needs in a store. And if this was executed well, the results were: increase in sales, customer acquisition, and retention, thereby strengthening customers' trust in the brand. Today, how customers and brands interact with each other has evolved.

Artificial Intelligence in simple words can be explained as "The machines having the capability of performing cognitive functions like learning and problem-solving." Artificial intelligence has been there for long. But, it is taking off stupendously in the last few years. Artificial Intelligence is a disruptive form of technology that is changing not just the entire Technology Industry but even our day-to-day life. Artificial Intelligence is already changing the way marketers implement their strategies.

The ability to integrate multiple sources of information for business is revolutionized using Artificial Intelligence. Business strategy becomes a complex part due to the rise in competition. Using AI, we can perform certain tasks which fall beyond human efforts. A basic example of that is high-frequency stock trading. Let's look at particular areas of AI which help in revenue growth management.

Artificial Intelligence (AI) and Machine Learning (ML) are becoming an integral part of our lives, at work or home. Enterprises use AI and ML to streamline the business processes and help employees become more productive. AI and ML are used by social media sites, search engines, and OTT platforms to assist users in finding what they want. At home, we use AL-based voice assistants like Alexa, Siri, and Google Home Assistant for several purposes. As days pass, we see ML being extensively adopted by businesses.

Dynamic pricing is the practice of optimizing product and service prices according to supply and demand, competition price, or subsidiary product prices. It gained popularity in the 1980s as the airline industry in the US started developing software to adjust flight prices according to departure time, destination, season, etc. which results in 3-10% in profits according to the used module. The rise in popularity led other industries to leverage dynamic pricing, however, different industries use different dynamic pricing types according to their requirements and customers. In this article, we explore the different types of dynamic pricing, how to implement them, and industries benefiting from each type. Segmented pricing, also known as price discrimination, is where businesses set different prices for the same product based on customer data (e.g.

Marketing comes before creating your product, and it is necessary so that your new product survives better and for the long run in the market. All your efforts, time & money that you put into creating your product are of no use unless and until people know about your products or services, and here comes the marketing. Your business can't drive sales and conversions if you haven't done the marketing well of products/services. Let's dive more and know about marketing in detail and how artificial intelligence will have an impact on marketing? Marketing is completely based on science rather than creativity.

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