A Research on Tata Motors Covid 19 Situation on Customer Brand Awareness

Abstract

Brand awareness version for enjoy products that overcomes the limitations of patron desire fashions, especially when it is not smooth to take into account a few qualitative attributes of a product or while there are too many attributes relative to the available quantity of preference date, via capturing the consequences of unobserved product attributes with the residuals of reference consumers for the identical product. They decompose the deterministic factor of product software into parts: that accounted for by way of found attributes and that because of non-located attributes. By using estimating the unobserved factor via concerning it to the corresponding residuals of digital experts representing homogeneous corporations of people who experienced the product earlier and evaluated it. The usage of Bayesian estimation methods and markov chain Monte Carlo simulation inference, applying two kinds of patron preference. 1. Standard preference data for online customer ratings in internet reference services. 2) Revolved preferences it is data for movies for offline customers. The values empirically show that this new approach outperforms several opportunity collaborative   filtering and attribute-based choice fashions with each in and out of sample fits. The model is relevant to each net recommendation services and client preference research.

KEYWORDS : Brand consciousness, consumer alternatives.