The introduction of CryptoKicks non-fungible tokenized (NFT) shoes is beneficial for Nike since that technology would cut the losses from counterfeit exclusive models. However, the consumers would also gain several advantages from purchasing CryptoKicks. Most importantly, tokenization would immensely help in proving the authenticity of the shoes. In addition, NFT technology would allow setting the limit on the number of copies that can be produced (Fries, 2021). Essentially, a person who purchased an exclusive model of Nike shoes would be enabled to “breed” their unique, custom-made model (Fries, 2021). Therefore, a possible target audience of CryptoKicks should be searched among the wealthier people who already purchase exclusive shoes. The tokenization and possibilities which it creates would incline that target group to buy even more Nike products.
Several research methods can be utilized for revealing the consumer’s underlying needs. First of all, there are traditional methods that rely on human interaction, such as focus groups and experiential interviews (Timoshenko & Hauser, 2019). However, these methods would consume a significant amount of time (Timoshenko & Hauser, 2019). As a result, the competition will receive an opportunity to negate Nike’s technological advantage. Due to that reason, I would propose an analysis of user-generated content (UGC) to research the consumer’s needs. This text mining-based method is relatively quick and brings low costs, especially for a big company such as Nike. UGC analysis would allow mining hundreds of thousands of reviews and posts on social media and blogs. Moreover, Nike would be able to continuously study consumer needs and quickly explore any new insights found in the process. Overall, UGS analysis would help analyze a massive amount of open-access data related to potential consumers and avoid delays in time to market.
Reference List
Fries, T. CryptoKicks: Nike to tokenize shoe ownership on Ethereum (2021).
Timoshenko, A. and Hauser, J. R. (2019) ‘Identifying customer needs from user-generated content’, Marketing Science, 38(1), pp. 1–20.