The Role of Big Data in Enhancing Customer Experience in UAE Retail
Bibliographic record
Abstract
Purpose: The objective of this research was to identify critical success factors for the adoption of Big Data in UAE retail. The use of Big Data, in this case, focused on improving its system of recommendations for a better understanding of consumer behavior and its impact on consumer experience.Design/Methodology/Approach: The research was done through interviews & observation of shopping patterns. A semi - structured interview script was used for the interviews.Findings: Based on the results, we outline some propositions related to the opportunities and obstacles for the implementation of Big Data in UAE retail.Originality/Value: The main contribution of the research was the identification of relevant factors to the adoption of Big Data that were not considered as critical for the adoption of previous technologies.Research Rationale: Few years ago, retailers had no idea who was buying what and from where they are buying and where not so bother about customer experience. Now Big Data helps retailers understand individuals' needs, allowing them to create segments to target. Big Data will help in understanding the buying trend. Not may study are conducted in UAE retail. This study will help to understand the adoption and role of Big data in UAE retail customer experience.This paper show how can big data analytics help to improve the retail business and can be applied in the sector and help in decision making.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".