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The purpose of this paper is to gain an insight about the strategy of diversification adopted by the company, Prime Technology, to increase its profits. The study is made so as to understand the essential aspects which any firm should consider before deciding to venture into a new business so that it may not end in failure.

The case is basically a narration of the business strategy adopted by Prime Technology and the aftermaths of the decisions taken in haste.

The paper showcases the experience of Prime Technology related to the correctness of the decisions taken related to the brand positioning and logistics involved with the import of a perishable commodity. It suggests that proper ground work while importing a product like chocolates plays a vital role in deciding the fate of an importer's fortune. Thus correct timing, packing and marketing are all close knit parameters for success.

The case provides traders with an insight in understanding the vitality of the decisions related to logistics and brand positioning while importing a product that is not very popular in the domestic country, and especially if it is a perishable commodity.

This case is an original attempt to illustrate the core drivers and capabilities for achieving success in a diverse field. The insights from the case demonstrate the value of proper planning, strategies to companies that are open to expand and recognize the need to focus the use of scarce value‐adding resources.

There is a strong need for companies to monitor customer-generated content of social media, not only about themselves but also about competitors, to deal with competition and to assess competitive environment of the business. The purpose of this paper is to help companies with social media competitive analysis and transformation of social media data into knowledge creation for decision-makers, specifically for app-based food delivery companies.

Three online app-based food delivery companies, i.e. Swiggy, Zomato and UberEats, were considered in this study. Twitter was used as the data collection platform where customer’s tweets related to all three companies are fetched using R-Studio and Lexicon-based sentiment analysis method is applied on the tweets fetched for the companies. A descriptive analytical method is used to compute the score of different sentiments. A negative and positive sentiment word list is created to match the word present on the tweets and based on the matching positive, negative and neutral sentiments score are decided. The sentiment analysis is a best method to analyze consumer’s text sentiment. Lexicon-based sentiment classification is always preferable than machine learning or other model because it gives flexibility to make your own sentiment dictionary to classify emotions. To perform tweets sentiment analysis, lexicon-based classification method and text mining were performed on R-Studio platform.

Results suggest that Zomato (26% positive sentiments) has received more positive sentiments as compared to the other two companies (25% positive sentiments for Swiggy and 24% positive sentiments for UberEats). Negative sentiments for the Zomato was also low (12% negative sentiments) compared to Swiggy and UberEats (13% negative sentiments for both). Further, based on negative sentiments concerning all the three food delivery companies, tweets were analyzed and recommendations for business provided.

The results of this study reveal the value of social media competitive analysis and show the power of text mining and sentiment analysis in extracting business value and competitive advantage. Suggestions, business and research implications are also provided to help companies in developing a social media competitive analysis strategy.

Twitter analysis of food-based companies has been performed.