Psychographic Segmentation and Profileing of Online Social Media Users for Availing Banking Services
Bibliographic record
Abstract
These days social media has become a innovative and essential expertise for everyone including those who are not at all aware of Information Technology. The development of IT and expansion of social media are the irrefutable truth of modern era. It is clearly indicated by the statistics that the use of social media platforms has been growing as a result of penetration of internet and easy accessibility to mobile gadgets, smart phones, tablets, programs and applications. More than 2 billion people in the world have been using Social media platforms. The usage of social media platforms has been shifted from entertainment to business and trade. In financial service sector the customer interaction is an integral part so digital communication has become strong medium of communication between financial institutions and customers. The digital medias have become the precious instrument for prospective clients to bond with the banks. Customers differ from one another based on some specific features and characteristics, however they can be segmented into various homogenous groups based on the similarities with in the group and diversity between the groups. Hence, in the perspective of the present study, the hypothesis has been developed that the diverse classes of the respondents respond differently to the perceptual factors extracted out of the statements representing their attitude towards the usage of social media platforms in banking sector when they are segmented on the basis of their psychographics. In the present study, it is revealed by the cluster analysis that the two groups were developed on the basis of the responses from the selected sample of the respondents. The cluster one consisted of a group of 115 respondents who were holding negative attitude for the positive aspects of the usage of social media in availing banking services. The second cluster of 181 respondents were having positive feeling for the usage of social media in availing banking services. The discriminant analysis pointed out that the different groups of the respondents based on their values and ethics have different perceptions towards the usage of social media platforms for availing banking services. Financial organizations having presence on social media and catering to customer needs through these platforms are required to understand the customer concerns so as to expand and improve the experience of online social media customers for availing banking services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".